diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3f6e7cdf332c6f9cfc7ac31b21db684c41389ad
--- /dev/null
+++ b/utils/__init__.py
@@ -0,0 +1,30 @@
+class HParams:  # Fix gpt-sovits torch.load 报缺少模块的问题
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            if type(v) == dict:
+                v = HParams(**v)
+            self[k] = v
+
+    def keys(self):
+        return self.__dict__.keys()
+
+    def items(self):
+        return self.__dict__.items()
+
+    def values(self):
+        return self.__dict__.values()
+
+    def __len__(self):
+        return len(self.__dict__)
+
+    def __getitem__(self, key):
+        return getattr(self, key)
+
+    def __setitem__(self, key, value):
+        return setattr(self, key, value)
+
+    def __contains__(self, key):
+        return key in self.__dict__
+
+    def __repr__(self):
+        return self.__dict__.__repr__()
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diff --git a/utils/agent/__init__.py b/utils/agent/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/utils/agent/delivery_time_query.py b/utils/agent/delivery_time_query.py
new file mode 100644
index 0000000000000000000000000000000000000000..d991c6e23c92d3150d57a6200c45b94dc5f95c0a
--- /dev/null
+++ b/utils/agent/delivery_time_query.py
@@ -0,0 +1,301 @@
+import base64
+from datetime import datetime
+import hashlib
+import json
+import os
+from typing import Optional, Type
+
+import jionlp as jio
+import requests
+from lagent.actions.base_action import BaseAction, tool_api
+from lagent.actions.parser import BaseParser, JsonParser
+from lagent.schema import ActionReturn, ActionStatusCode
+
+from utils.web_configs import WEB_CONFIGS
+
+'''
+class DeliveryTimeQueryAction(BaseAction):
+    """快递时效查询插件，用于根据用户提出的收货地址查询到达期限"""
+
+    def __init__(
+        self,
+        # departure_place: str,
+        # delivery_company_name: str,
+        description: Optional[dict] = None,
+        parser: Type[BaseParser] = JsonParser,
+        enable: bool = True,
+    ) -> None:
+        super().__init__(description, parser, enable)
+        self.departure_place = departure_place  # 发货地
+
+        # 天气查询
+        self.weather_query_handler = WeatherQuery(departure_place, WEB_CONFIGS.AGENT_WEATHER_API_KEY)
+        self.delivery_time_handler = DeliveryTimeQuery(delivery_company_name, WEB_CONFIGS.AGENT_DELIVERY_TIME_API_KEY)
+
+    @tool_api
+    def run(self, query: str) -> ActionReturn:
+        """一个到货时间查询API。可以根据城市名查询到货时间信息。
+
+        Args:
+            query (:class:`str`): 需要查询的城市名。
+        """
+
+        # 获取文本中收货地，发货地后台设置
+        # 防止 LLM 将城市识别错误，进行兜底
+        city_info = jio.parse_location(query, town_village=True)
+        city_name = city_info["city"]
+
+        # 获取收货地代号 -> 天气
+        destination_weather = self.weather_query_handler(city_name)
+
+        # 获取发货地代号 -> 天气
+        departure_weather = self.weather_query_handler(self.departure_place)
+
+        # 获取到达时间
+        delivery_time = self.delivery_time_handler(self.departure_place, city_name)
+
+        final_str = (
+            f"今天日期：{datetime.now().strftime('%m月%d日')}\n"
+            f"收货地天气：{destination_weather.result[0]['content']}\n"
+            f"发货地天气：{departure_weather.result[0]['content']}\n"
+            f"物流信息：{delivery_time.result[0]['content']}\n"
+            "回答突出“预计送达时间”和“收货地天气”，如果收货地或者发货地遇到暴雨暴雪等极端天气，须告知用户快递到达时间会有所增加。"
+        )
+
+        tool_return = ActionReturn(type=self.name)
+        tool_return.result = [dict(type="text", content=final_str)]
+        return tool_return
+'''
+'''
+class WeatherQuery:
+    """快递时效查询插件，用于根据用户提出的收货地址查询到达期限"""
+
+    def __init__(
+        self,
+        departure_place: str,
+        api_key: Optional[str] = None,
+    ) -> None:
+        self.departure_place = departure_place  # 发货地
+
+        # 天气查询
+        # api_key = os.environ.get("WEATHER_API_KEY", key)
+        if api_key is None:
+            raise ValueError("Please set Weather API key either in the environment as WEATHER_API_KEY")
+        self.api_key = api_key
+        self.location_query_url = "https://geoapi.qweather.com/v2/city/lookup"
+        self.weather_query_url = "https://devapi.qweather.com/v7/weather/now"
+
+    def parse_results(self, city_name: str, results: dict) -> str:
+        """解析 API 返回的信息
+
+        Args:
+            results (dict): JSON 格式的 API 报文。
+
+        Returns:
+            str: 解析后的结果。
+        """
+        now = results["now"]
+        data = (
+            # f'数据观测时间: {now["obsTime"]}；'
+            f"城市名: {city_name}；"
+            f'温度: {now["temp"]}°C；'
+            f'体感温度: {now["feelsLike"]}°C；'
+            f'天气: {now["text"]}；'
+            # f'风向: {now["windDir"]}，角度为 {now["wind360"]}°；'
+            f'风力等级: {now["windScale"]}，风速为 {now["windSpeed"]} km/h；'
+            f'相对湿度: {now["humidity"]}；'
+            f'当前小时累计降水量: {now["precip"]} mm；'
+            # f'大气压强: {now["pressure"]} 百帕；'
+            f'能见度: {now["vis"]} km。'
+        )
+        return data
+
+    def __call__(self, query):
+        tool_return = ActionReturn()
+        status_code, response = self.search_weather_with_city(query)
+        if status_code == -1:
+            tool_return.errmsg = response
+            tool_return.state = ActionStatusCode.HTTP_ERROR
+        elif status_code == 200:
+            parsed_res = self.parse_results(query, response)
+            tool_return.result = [dict(type="text", content=str(parsed_res))]
+            tool_return.state = ActionStatusCode.SUCCESS
+        else:
+            tool_return.errmsg = str(status_code)
+            tool_return.state = ActionStatusCode.API_ERROR
+        return tool_return
+
+    def search_weather_with_city(self, query: str):
+        """根据城市名获取城市代号，然后进行天气查询
+
+        Args:
+            query (str): 城市名
+
+        Returns:
+            int: 天气接口调用状态码
+            dict: 天气接口返回信息
+        """
+
+        # 获取城市代号
+        try:
+            city_code_response = requests.get(self.location_query_url, params={"key": self.api_key, "location": query})
+        except Exception as e:
+            return -1, str(e)
+
+        if city_code_response.status_code != 200:
+            return city_code_response.status_code, city_code_response.json()
+        city_code_response = city_code_response.json()
+        if len(city_code_response["location"]) == 0:
+            return -1, "未查询到城市"
+        city_code = city_code_response["location"][0]["id"]
+
+        # 获取天气
+        try:
+            weather_response = requests.get(self.weather_query_url, params={"key": self.api_key, "location": city_code})
+        except Exception as e:
+            return -1, str(e)
+        return weather_response.status_code, weather_response.json()
+'''
+
+class DeliveryTimeQuery:
+    def __init__(
+        self,
+        delivery_company_name: Optional[str] = "中通",
+        api_key: Optional[str] = None,
+    ) -> None:
+
+        # 快递时效查询
+        # api_key = os.environ.get("DELIVERY_TIME_API_KEY", key)
+        if api_key is None or "," not in api_key:
+            raise ValueError(
+                'Please set Delivery time API key either in the environment as DELIVERY_TIME_API_KEY="${e_business_id},${api_key}"'
+            )
+        self.e_business_id = api_key.split(",")[0]
+        self.api_key = api_key.split(",")[1]
+        self.api_url = "http://api.kdniao.com/api/dist"  # 快递鸟
+        self.china_location = jio.china_location_loader()
+        # 快递鸟对应的
+        DELIVERY_COMPANY_MAP = {
+            "德邦": "DBL",
+            "邮政": "EMS",
+            "京东": "JD",
+            "极兔速递": "JTSD",
+            "顺丰": "SF",
+            "申通": "STO",
+            "韵达": "YD",
+            "圆通": "YTO",
+            "中通": "ZTO",
+        }
+        self.delivery_company_name = delivery_company_name
+        self.delivery_company_id = DELIVERY_COMPANY_MAP[delivery_company_name]
+
+    @staticmethod
+    def data_md5(n):
+        # md5加密
+        md5 = hashlib.md5()
+        md5.update(str(n).encode("utf-8"))
+        return md5.hexdigest()
+
+    def get_data_sign(self, n):
+        # 签名
+        md5Data = self.data_md5(json.dumps(n) + self.api_key)
+        res = str(base64.b64encode(md5Data.encode("utf-8")), "utf-8")
+        return res
+
+    def get_city_detail(self, name):
+        # 如果是城市名，使用第一个区名
+        city_info = jio.parse_location(name, town_village=True)
+        # china_location = jio.china_location_loader()
+
+        county_name = ""
+        for i in self.china_location[city_info["province"]][city_info["city"]].keys():
+            if "区" == i[-1]:
+                county_name = i
+                break
+
+        return {
+            "province": city_info["province"],
+            "city": city_info["city"],
+            "county": county_name,
+        }
+
+    def get_params(self, send_city, receive_city):
+
+        # 根据市查出省份和区名称
+        send_city_info = self.get_city_detail(send_city)
+        receive_city_info = self.get_city_detail(receive_city)
+
+        # 预计送达时间接口文档；https://www.yuque.com/kdnjishuzhichi/dfcrg1/ynkmts0e5owsnpvu
+        # 请求接口指令
+        RequestType = "6004"
+        # 组装应用级参数
+        RequestData = {
+            "ShipperCode": self.delivery_company_id,
+            "ReceiveArea": receive_city_info["county"],
+            "ReceiveCity": receive_city_info["city"],
+            "ReceiveProvince": receive_city_info["province"],
+            "SendArea": send_city_info["county"],
+            "SendCity": send_city_info["city"],
+            "SendProvince": send_city_info["province"],
+        }
+        # 组装系统级参数
+        data = {
+            "RequestData": json.dumps(RequestData),
+            "RequestType": RequestType,
+            "EBusinessID": self.e_business_id,
+            "DataSign": self.get_data_sign(RequestData),
+            "DataType": 2,
+        }
+        return data
+
+    def parse_results(self, response):
+
+        # 返回例子：
+        # {
+        # "EBusinessID" : "1000000",
+        # "Data" : {
+        #     "DeliveryTime" : "06月15日下午可达",
+        #     "SendAddress" : null,
+        #     "ReceiveArea" : "芙蓉区",
+        #     "SendProvince" : "广东省",
+        #     "ReceiveProvince" : "湖南省",
+        #     "ShipperCode" : "DBL",
+        #     "Hour" : "52h",
+        #     "SendArea" : "白云区",
+        #     "ReceiveAddress" : null,
+        #     "SendCity" : "广州市",
+        #     "ReceiveCity" : "长沙市"
+        # },
+        # "ResultCode" : "100",
+        # "Success" : true
+        # }
+
+        response = response["Data"]
+        data = (
+            f'发货地点: {response["SendProvince"]} {response["SendCity"]}；'
+            f'收货地点: {response["ReceiveProvince"]} {response["ReceiveCity"]}；'
+            f'预计送达时间: {response["DeliveryTime"]}；'
+            f"快递公司: {self.delivery_company_name}；"
+            f'预计时效: {response["Hour"]}。'
+        )
+        return data
+
+    def __call__(self, send_city, receive_city):
+        tool_return = ActionReturn()
+        try:
+            res = requests.post(self.api_url, self.get_params(send_city, receive_city))
+            status_code = res.status_code
+            response = res.json()
+        except Exception as e:
+            tool_return.errmsg = str(e)
+            tool_return.state = ActionStatusCode.API_ERROR
+            return tool_return
+
+        if status_code == 200:
+            parsed_res = self.parse_results(response)
+            tool_return.result = [dict(type="text", content=str(parsed_res))]
+            tool_return.state = ActionStatusCode.SUCCESS
+        else:
+            tool_return.errmsg = str(status_code)
+            tool_return.state = ActionStatusCode.API_ERROR
+        return tool_return
diff --git a/utils/asr/__init__.py b/utils/asr/__init__.py
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diff --git a/utils/asr/asr_worker.py b/utils/asr/asr_worker.py
new file mode 100644
index 0000000000000000000000000000000000000000..22719fdcc9cb0fe57bd339e1f2607ffba4571c8f
--- /dev/null
+++ b/utils/asr/asr_worker.py
@@ -0,0 +1,54 @@
+import datetime
+from funasr import AutoModel
+import streamlit as st
+from utils.web_configs import WEB_CONFIGS
+from modelscope import snapshot_download
+from modelscope.utils.constant import Invoke, ThirdParty
+from funasr.download.name_maps_from_hub import name_maps_ms as NAME_MAPS_MS
+
+
+@st.cache_resource
+def load_asr_model():
+
+    # 模型下载
+    model_path_info = dict()
+    for model_name in ["paraformer-zh", "fsmn-vad", "ct-punc"]:
+        print(f"downloading asr model : {NAME_MAPS_MS[model_name]}")
+        mode_dir = snapshot_download(
+            NAME_MAPS_MS[model_name],
+            revision="master",
+            user_agent={Invoke.KEY: Invoke.PIPELINE, ThirdParty.KEY: "funasr"},
+            cache_dir=WEB_CONFIGS.ASR_MODEL_DIR,
+        )
+        model_path_info[model_name] = mode_dir
+        NAME_MAPS_MS[model_name] = mode_dir # 更新
+
+    print(f"ASR model path info = {model_path_info}")
+    # paraformer-zh is a multi-functional asr model
+    # use vad, punc, spk or not as you need
+    model = AutoModel(
+        model="paraformer-zh",  # 语音识别，带时间戳输出，非实时
+        vad_model="fsmn-vad",  # 语音端点检测，实时
+        punc_model="ct-punc",  # 标点恢复
+        # spk_model="cam++" # 说话人确认/分割
+        model_path=model_path_info["paraformer-zh"],
+        vad_kwargs={"model_path": model_path_info["fsmn-vad"]},
+        punc_kwargs={"model_path": model_path_info["ct-punc"]},
+    )
+    return model
+
+
+def process_asr(model: AutoModel, wav_path):
+    # https://github.com/modelscope/FunASR/blob/main/README_zh.md#%E5%AE%9E%E6%97%B6%E8%AF%AD%E9%9F%B3%E8%AF%86%E5%88%AB
+    f_start_time = datetime.datetime.now()
+    res = model.generate(input=wav_path, batch_size_s=50, hotword="魔搭")
+    delta_time = datetime.datetime.now() - f_start_time
+
+    try:
+        print(f"ASR using time {delta_time}s, text: ", res[0]["text"])
+        res_str = res[0]["text"]
+    except Exception as e:
+        print("ASR 解析失败，无法获取到文字")
+        return ""
+
+    return res_str
diff --git a/utils/digital_human/__init__.py b/utils/digital_human/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..870e92d30cd3486f30e3604dfcdcfd35d11e8a5e
--- /dev/null
+++ b/utils/digital_human/__init__.py
@@ -0,0 +1,6 @@
+from torch import hub
+from utils.web_configs import WEB_CONFIGS
+from pathlib import Path
+
+# 部分模型会使用 torch download 下载，需要设置路径
+hub.set_dir(str(Path(WEB_CONFIGS.DIGITAL_HUMAN_MODEL_DIR).joinpath("face-alignment")))
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diff --git a/utils/digital_human/__pycache__/realtime_inference.cpython-310.pyc b/utils/digital_human/__pycache__/realtime_inference.cpython-310.pyc
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diff --git a/utils/digital_human/digital_human_worker.py b/utils/digital_human/digital_human_worker.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4023aab1f93a8a9319d43bef10e990d748a6d63
--- /dev/null
+++ b/utils/digital_human/digital_human_worker.py
@@ -0,0 +1,36 @@
+from pathlib import Path
+import streamlit as st
+from utils.digital_human.realtime_inference import gen_digital_human_video
+from utils.model_loader import DIGITAL_HUMAN_HANDLER
+from utils.web_configs import WEB_CONFIGS
+
+def show_video(video_path, autoplay=True, loop=False, muted=False):
+    # 需要 fp25 才能显示
+    with open(video_path, "rb") as f_wav:
+        video_bytes = f_wav.read()
+
+    print(f"Show video: {video_path}")
+    st.video(video_bytes, format="video/mp4", autoplay=autoplay, loop=loop, muted=muted)
+
+
+def gen_digital_human_video_in_spinner(audio_path):
+    save_path = None
+    if st.session_state.gen_digital_human_checkbox and DIGITAL_HUMAN_HANDLER is not None:
+        with st.spinner(
+            "正在生成数字人，请稍等... 如果觉得生成时间太久，可以将侧边栏的【生成数字人】按钮取消选中，下次则不会生成"
+        ):
+            # save_tag = datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + ".wav"
+
+            st.session_state.digital_human_video_path = gen_digital_human_video(
+                DIGITAL_HUMAN_HANDLER,
+                audio_path,
+                work_dir=str(Path(WEB_CONFIGS.DIGITAL_HUMAN_GEN_PATH).absolute()),
+                video_path=st.session_state.digital_human_video_path,
+                fps=DIGITAL_HUMAN_HANDLER.model_handler.fps,
+            )
+
+            st.session_state.video_placeholder.empty()  # 清空
+            with st.session_state.video_placeholder.container():
+                show_video(st.session_state.digital_human_video_path)
+            st.toast("生成数字人视频成功!")
+    return save_path
diff --git a/utils/digital_human/musetalk/models/__pycache__/unet.cpython-310.pyc b/utils/digital_human/musetalk/models/__pycache__/unet.cpython-310.pyc
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diff --git a/utils/digital_human/musetalk/models/unet.py b/utils/digital_human/musetalk/models/unet.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bcc2b056db74437784ed8a53b929d652b3a552b
--- /dev/null
+++ b/utils/digital_human/musetalk/models/unet.py
@@ -0,0 +1,47 @@
+import torch
+import torch.nn as nn
+import math
+import json
+
+from diffusers import UNet2DConditionModel
+import sys
+import time
+import numpy as np
+import os
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model=384, max_len=5000):
+        super(PositionalEncoding, self).__init__()
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        b, seq_len, d_model = x.size()
+        pe = self.pe[:, :seq_len, :]
+        x = x + pe.to(x.device)
+        return x
+    
+class UNet():
+    def __init__(self, 
+                 unet_config,
+                 model_path,
+                 use_float16=False,
+        ):
+        with open(unet_config, 'r') as f:
+            unet_config = json.load(f)
+        self.model = UNet2DConditionModel(**unet_config)
+        self.pe = PositionalEncoding(d_model=384)
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        weights = torch.load(model_path) if torch.cuda.is_available() else torch.load(model_path, map_location=self.device)
+        self.model.load_state_dict(weights)
+        if use_float16:
+            self.model = self.model.half()
+        self.model.to(self.device)
+    
+if __name__ == "__main__":
+    unet = UNet()
diff --git a/utils/digital_human/musetalk/models/vae.py b/utils/digital_human/musetalk/models/vae.py
new file mode 100644
index 0000000000000000000000000000000000000000..84b80164089fb95637a95f298b263a0228a24515
--- /dev/null
+++ b/utils/digital_human/musetalk/models/vae.py
@@ -0,0 +1,149 @@
+import os
+
+import cv2
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+from diffusers import AutoencoderKL
+
+
+class VAE():
+    """
+    VAE (Variational Autoencoder) class for image processing.
+    """
+
+    def __init__(self, model_path="./models/sd-vae-ft-mse/", resized_img=256, use_float16=False):
+        """
+        Initialize the VAE instance.
+
+        :param model_path: Path to the trained model.
+        :param resized_img: The size to which images are resized.
+        :param use_float16: Whether to use float16 precision.
+        """
+        self.model_path = model_path
+        self.vae = AutoencoderKL.from_pretrained(self.model_path)
+
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.vae.to(self.device)
+
+        if use_float16:
+            self.vae = self.vae.half()
+            self._use_float16 = True
+        else:
+            self._use_float16 = False
+
+        self.scaling_factor = self.vae.config.scaling_factor
+        self.transform = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+        self._resized_img = resized_img
+        self._mask_tensor = self.get_mask_tensor()
+        
+    def get_mask_tensor(self):
+        """
+        Creates a mask tensor for image processing.
+        :return: A mask tensor.
+        """
+        mask_tensor = torch.zeros((self._resized_img,self._resized_img))
+        mask_tensor[:self._resized_img//2,:] = 1
+        mask_tensor[mask_tensor< 0.5] = 0
+        mask_tensor[mask_tensor>= 0.5] = 1
+        return mask_tensor
+            
+    def preprocess_img(self,img_name,half_mask=False):
+        """
+        Preprocess an image for the VAE.
+
+        :param img_name: The image file path or a list of image file paths.
+        :param half_mask: Whether to apply a half mask to the image.
+        :return: A preprocessed image tensor.
+        """
+        window = []
+        if isinstance(img_name, str):
+            window_fnames = [img_name]
+            for fname in window_fnames:
+                img = cv2.imread(fname)
+                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+                img = cv2.resize(img, (self._resized_img, self._resized_img),
+                                     interpolation=cv2.INTER_LANCZOS4)
+                window.append(img)
+        else:
+            img = cv2.cvtColor(img_name, cv2.COLOR_BGR2RGB)
+            window.append(img)
+            
+        x = np.asarray(window) / 255.
+        x = np.transpose(x, (3, 0, 1, 2))
+        x = torch.squeeze(torch.FloatTensor(x))
+        if half_mask:
+            x = x * (self._mask_tensor>0.5)
+        x = self.transform(x)
+        
+        x = x.unsqueeze(0) # [1, 3, 256, 256] torch tensor
+        x = x.to(self.vae.device)
+
+        return x
+
+    def encode_latents(self,image):
+        """
+        Encode an image into latent variables.
+
+        :param image: The image tensor to encode.
+        :return: The encoded latent variables.
+        """
+        with torch.no_grad():
+            init_latent_dist = self.vae.encode(image.to(self.vae.dtype)).latent_dist
+        init_latents = self.scaling_factor * init_latent_dist.sample()
+        return init_latents
+    
+    def decode_latents(self, latents):
+        """
+        Decode latent variables back into an image.
+        :param latents: The latent variables to decode.
+        :return: A NumPy array representing the decoded image.
+        """
+        latents = (1/  self.scaling_factor) * latents
+        image = self.vae.decode(latents.to(self.vae.dtype)).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.detach().cpu().permute(0, 2, 3, 1).float().numpy()
+        image = (image * 255).round().astype("uint8")
+        image = image[...,::-1] # RGB to BGR
+        return image
+    
+    def get_latents_for_unet(self,img):
+        """
+        Prepare latent variables for a U-Net model.
+        :param img: The image to process.
+        :return: A concatenated tensor of latents for U-Net input.
+        """
+        
+        ref_image = self.preprocess_img(img,half_mask=True) # [1, 3, 256, 256] RGB, torch tensor
+        masked_latents = self.encode_latents(ref_image) # [1, 4, 32, 32], torch tensor
+        ref_image = self.preprocess_img(img,half_mask=False) # [1, 3, 256, 256] RGB, torch tensor
+        ref_latents = self.encode_latents(ref_image) # [1, 4, 32, 32], torch tensor
+        latent_model_input = torch.cat([masked_latents, ref_latents], dim=1)
+        return latent_model_input
+
+if __name__ == "__main__":
+    vae_mode_path = "./models/sd-vae-ft-mse/"
+    vae = VAE(model_path = vae_mode_path,use_float16=False)
+    img_path = "./results/sun001_crop/00000.png"
+    
+    crop_imgs_path = "./results/sun001_crop/"
+    latents_out_path = "./results/latents/"
+    if not os.path.exists(latents_out_path):
+        os.mkdir(latents_out_path)
+
+    files = os.listdir(crop_imgs_path)
+    files.sort()
+    files = [file for file in files if file.split(".")[-1] == "png"]
+
+    for file in files:
+        index = file.split(".")[0]
+        img_path = crop_imgs_path + file
+        latents = vae.get_latents_for_unet(img_path)
+        print(img_path,"latents",latents.size())
+        #torch.save(latents,os.path.join(latents_out_path,index+".pt"))
+        #reload_tensor = torch.load('tensor.pt')
+        #print(reload_tensor.size())
+        
+
+    
\ No newline at end of file
diff --git a/utils/digital_human/musetalk/utils/__init__.py b/utils/digital_human/musetalk/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b58c41d239fae3136e82e0d680cd7ab4e9f78aaf
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/__init__.py
@@ -0,0 +1,5 @@
+import sys
+from os.path import abspath, dirname
+current_dir = dirname(abspath(__file__))
+parent_dir = dirname(current_dir)
+sys.path.append(parent_dir+'/utils')
diff --git a/utils/digital_human/musetalk/utils/__pycache__/__init__.cpython-310.pyc b/utils/digital_human/musetalk/utils/__pycache__/__init__.cpython-310.pyc
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diff --git a/utils/digital_human/musetalk/utils/blending.py b/utils/digital_human/musetalk/utils/blending.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdeaa6e3425a09d61222760237ceab435ddef9ff
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/blending.py
@@ -0,0 +1,110 @@
+from PIL import Image
+import numpy as np
+import cv2
+from face_parsing import FaceParsing
+
+
+def init_face_parsing_model(
+    resnet_path="./models/face-parse-bisent/resnet18-5c106cde.pth", face_model_pth="./models/face-parse-bisent/79999_iter.pth"
+):
+    fp_model = FaceParsing(resnet_path, face_model_pth)
+    return fp_model
+
+
+def get_crop_box(box, expand):
+    x, y, x1, y1 = box
+    x_c, y_c = (x + x1) // 2, (y + y1) // 2
+    w, h = x1 - x, y1 - y
+    s = int(max(w, h) // 2 * expand)
+    crop_box = [x_c - s, y_c - s, x_c + s, y_c + s]
+    return crop_box, s
+
+
+def face_seg(image, fp_model):
+    seg_image = fp_model(image)
+    if seg_image is None:
+        print("error, no person_segment")
+        return None
+
+    seg_image = seg_image.resize(image.size)
+    return seg_image
+
+
+def get_image(image, face, face_box, fp_model, upper_boundary_ratio=0.5, expand=1.2):
+    # print(image.shape)
+    # print(face.shape)
+
+    body = Image.fromarray(image[:, :, ::-1])
+    face = Image.fromarray(face[:, :, ::-1])
+
+    x, y, x1, y1 = face_box
+    # print(x1-x,y1-y)
+    crop_box, s = get_crop_box(face_box, expand)
+    x_s, y_s, x_e, y_e = crop_box
+    face_position = (x, y)
+
+    face_large = body.crop(crop_box)
+    ori_shape = face_large.size
+
+    mask_image = face_seg(face_large, fp_model)
+    mask_small = mask_image.crop((x - x_s, y - y_s, x1 - x_s, y1 - y_s))
+    mask_image = Image.new("L", ori_shape, 0)
+    mask_image.paste(mask_small, (x - x_s, y - y_s, x1 - x_s, y1 - y_s))
+
+    # keep upper_boundary_ratio of talking area
+    width, height = mask_image.size
+    top_boundary = int(height * upper_boundary_ratio)
+    modified_mask_image = Image.new("L", ori_shape, 0)
+    modified_mask_image.paste(mask_image.crop((0, top_boundary, width, height)), (0, top_boundary))
+
+    blur_kernel_size = int(0.1 * ori_shape[0] // 2 * 2) + 1
+    mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0)
+    mask_image = Image.fromarray(mask_array)
+
+    face_large.paste(face, (x - x_s, y - y_s, x1 - x_s, y1 - y_s))
+    body.paste(face_large, crop_box[:2], mask_image)
+    body = np.array(body)
+    return body[:, :, ::-1]
+
+
+def get_image_prepare_material(image, face_box, fp_model, upper_boundary_ratio=0.5, expand=1.2):
+    body = Image.fromarray(image[:, :, ::-1])
+
+    x, y, x1, y1 = face_box
+    # print(x1-x,y1-y)
+    crop_box, s = get_crop_box(face_box, expand)
+    x_s, y_s, x_e, y_e = crop_box
+
+    face_large = body.crop(crop_box)
+    ori_shape = face_large.size
+
+    mask_image = face_seg(face_large, fp_model)
+    mask_small = mask_image.crop((x - x_s, y - y_s, x1 - x_s, y1 - y_s))
+    mask_image = Image.new("L", ori_shape, 0)
+    mask_image.paste(mask_small, (x - x_s, y - y_s, x1 - x_s, y1 - y_s))
+
+    # keep upper_boundary_ratio of talking area
+    width, height = mask_image.size
+    top_boundary = int(height * upper_boundary_ratio)
+    modified_mask_image = Image.new("L", ori_shape, 0)
+    modified_mask_image.paste(mask_image.crop((0, top_boundary, width, height)), (0, top_boundary))
+
+    blur_kernel_size = int(0.1 * ori_shape[0] // 2 * 2) + 1
+    mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0)
+    return mask_array, crop_box
+
+
+def get_image_blending(image, face, face_box, mask_array, crop_box):
+    body = Image.fromarray(image[:, :, ::-1])
+    face = Image.fromarray(face[:, :, ::-1])
+
+    x, y, x1, y1 = face_box
+    x_s, y_s, x_e, y_e = crop_box
+    face_large = body.crop(crop_box)
+
+    mask_image = Image.fromarray(mask_array)
+    mask_image = mask_image.convert("L")
+    face_large.paste(face, (x - x_s, y - y_s, x1 - x_s, y1 - y_s))
+    body.paste(face_large, crop_box[:2], mask_image)
+    body = np.array(body)
+    return body[:, :, ::-1]
diff --git a/utils/digital_human/musetalk/utils/dwpose/default_runtime.py b/utils/digital_human/musetalk/utils/dwpose/default_runtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f27c0345a6cf1b7bc4b035eb456e2c9f3126804
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/dwpose/default_runtime.py
@@ -0,0 +1,54 @@
+default_scope = 'mmpose'
+
+# hooks
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=50),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', interval=10),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='PoseVisualizationHook', enable=False),
+    badcase=dict(
+        type='BadCaseAnalysisHook',
+        enable=False,
+        out_dir='badcase',
+        metric_type='loss',
+        badcase_thr=5))
+
+# custom hooks
+custom_hooks = [
+    # Synchronize model buffers such as running_mean and running_var in BN
+    # at the end of each epoch
+    dict(type='SyncBuffersHook')
+]
+
+# multi-processing backend
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'),
+)
+
+# visualizer
+vis_backends = [
+    dict(type='LocalVisBackend'),
+    # dict(type='TensorboardVisBackend'),
+    # dict(type='WandbVisBackend'),
+]
+visualizer = dict(
+    type='PoseLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+
+# logger
+log_processor = dict(
+    type='LogProcessor', window_size=50, by_epoch=True, num_digits=6)
+log_level = 'INFO'
+load_from = None
+resume = False
+
+# file I/O backend
+backend_args = dict(backend='local')
+
+# training/validation/testing progress
+train_cfg = dict(by_epoch=True)
+val_cfg = dict()
+test_cfg = dict()
diff --git a/utils/digital_human/musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py b/utils/digital_human/musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cd863d9154e98e356ebecee6593f3381c2b1498
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py
@@ -0,0 +1,257 @@
+#_base_ = ['../../../_base_/default_runtime.py']
+_base_ = ['default_runtime.py']
+
+# runtime
+max_epochs = 270
+stage2_num_epochs = 30
+base_lr = 4e-3
+train_batch_size = 8
+val_batch_size = 8
+
+train_cfg = dict(max_epochs=max_epochs, val_interval=10)
+randomness = dict(seed=21)
+
+# optimizer
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
+    paramwise_cfg=dict(
+        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR',
+        start_factor=1.0e-5,
+        by_epoch=False,
+        begin=0,
+        end=1000),
+    dict(
+        # use cosine lr from 150 to 300 epoch
+        type='CosineAnnealingLR',
+        eta_min=base_lr * 0.05,
+        begin=max_epochs // 2,
+        end=max_epochs,
+        T_max=max_epochs // 2,
+        by_epoch=True,
+        convert_to_iter_based=True),
+]
+
+# automatically scaling LR based on the actual training batch size
+auto_scale_lr = dict(base_batch_size=512)
+
+# codec settings
+codec = dict(
+    type='SimCCLabel',
+    input_size=(288, 384),
+    sigma=(6., 6.93),
+    simcc_split_ratio=2.0,
+    normalize=False,
+    use_dark=False)
+
+# model settings
+model = dict(
+    type='TopdownPoseEstimator',
+    data_preprocessor=dict(
+        type='PoseDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True),
+    backbone=dict(
+        _scope_='mmdet',
+        type='CSPNeXt',
+        arch='P5',
+        expand_ratio=0.5,
+        deepen_factor=1.,
+        widen_factor=1.,
+        out_indices=(4, ),
+        channel_attention=True,
+        norm_cfg=dict(type='SyncBN'),
+        act_cfg=dict(type='SiLU'),
+        init_cfg=dict(
+            type='Pretrained',
+            prefix='backbone.',
+            checkpoint='https://download.openmmlab.com/mmpose/v1/projects/'
+            'rtmpose/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth'  # noqa: E501
+        )),
+    head=dict(
+        type='RTMCCHead',
+        in_channels=1024,
+        out_channels=133,
+        input_size=codec['input_size'],
+        in_featuremap_size=(9, 12),
+        simcc_split_ratio=codec['simcc_split_ratio'],
+        final_layer_kernel_size=7,
+        gau_cfg=dict(
+            hidden_dims=256,
+            s=128,
+            expansion_factor=2,
+            dropout_rate=0.,
+            drop_path=0.,
+            act_fn='SiLU',
+            use_rel_bias=False,
+            pos_enc=False),
+        loss=dict(
+            type='KLDiscretLoss',
+            use_target_weight=True,
+            beta=10.,
+            label_softmax=True),
+        decoder=codec),
+    test_cfg=dict(flip_test=True, ))
+
+# base dataset settings
+dataset_type = 'UBody2dDataset'
+data_mode = 'topdown'
+data_root = 'data/UBody/'
+
+backend_args = dict(backend='local')
+
+scenes = [
+    'Magic_show', 'Entertainment', 'ConductMusic', 'Online_class', 'TalkShow',
+    'Speech', 'Fitness', 'Interview', 'Olympic', 'TVShow', 'Singing',
+    'SignLanguage', 'Movie', 'LiveVlog', 'VideoConference'
+]
+
+train_datasets = [
+    dict(
+        type='CocoWholeBodyDataset',
+        data_root='data/coco/',
+        data_mode=data_mode,
+        ann_file='annotations/coco_wholebody_train_v1.0.json',
+        data_prefix=dict(img='train2017/'),
+        pipeline=[])
+]
+
+for scene in scenes:
+    train_dataset = dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file=f'annotations/{scene}/train_annotations.json',
+        data_prefix=dict(img='images/'),
+        pipeline=[],
+        sample_interval=10)
+    train_datasets.append(train_dataset)
+
+# pipelines
+train_pipeline = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='RandomFlip', direction='horizontal'),
+    dict(type='RandomHalfBody'),
+    dict(
+        type='RandomBBoxTransform', scale_factor=[0.5, 1.5], rotate_factor=90),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(
+        type='Albumentation',
+        transforms=[
+            dict(type='Blur', p=0.1),
+            dict(type='MedianBlur', p=0.1),
+            dict(
+                type='CoarseDropout',
+                max_holes=1,
+                max_height=0.4,
+                max_width=0.4,
+                min_holes=1,
+                min_height=0.2,
+                min_width=0.2,
+                p=1.0),
+        ]),
+    dict(type='GenerateTarget', encoder=codec),
+    dict(type='PackPoseInputs')
+]
+val_pipeline = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='PackPoseInputs')
+]
+
+train_pipeline_stage2 = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='RandomFlip', direction='horizontal'),
+    dict(type='RandomHalfBody'),
+    dict(
+        type='RandomBBoxTransform',
+        shift_factor=0.,
+        scale_factor=[0.5, 1.5],
+        rotate_factor=90),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(
+        type='Albumentation',
+        transforms=[
+            dict(type='Blur', p=0.1),
+            dict(type='MedianBlur', p=0.1),
+            dict(
+                type='CoarseDropout',
+                max_holes=1,
+                max_height=0.4,
+                max_width=0.4,
+                min_holes=1,
+                min_height=0.2,
+                min_width=0.2,
+                p=0.5),
+        ]),
+    dict(type='GenerateTarget', encoder=codec),
+    dict(type='PackPoseInputs')
+]
+
+# data loaders
+train_dataloader = dict(
+    batch_size=train_batch_size,
+    num_workers=10,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type='CombinedDataset',
+        metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
+        datasets=train_datasets,
+        pipeline=train_pipeline,
+        test_mode=False,
+    ))
+
+val_dataloader = dict(
+    batch_size=val_batch_size,
+    num_workers=10,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
+    dataset=dict(
+        type='CocoWholeBodyDataset',
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json',
+        bbox_file='data/coco/person_detection_results/'
+        'COCO_val2017_detections_AP_H_56_person.json',
+        data_prefix=dict(img='coco/val2017/'),
+        test_mode=True,
+        pipeline=val_pipeline,
+    ))
+test_dataloader = val_dataloader
+
+# hooks
+default_hooks = dict(
+    checkpoint=dict(
+        save_best='coco-wholebody/AP', rule='greater', max_keep_ckpts=1))
+
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0002,
+        update_buffers=True,
+        priority=49),
+    dict(
+        type='mmdet.PipelineSwitchHook',
+        switch_epoch=max_epochs - stage2_num_epochs,
+        switch_pipeline=train_pipeline_stage2)
+]
+
+# evaluators
+val_evaluator = dict(
+    type='CocoWholeBodyMetric',
+    ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json')
+test_evaluator = val_evaluator
diff --git a/utils/digital_human/musetalk/utils/face_detection/README.md b/utils/digital_human/musetalk/utils/face_detection/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..c073376e4eeda6d4b29cc31c50cb7e88ab42bb73
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_detection/README.md
@@ -0,0 +1 @@
+The code for Face Detection in this folder has been taken from the wonderful [face_alignment](https://github.com/1adrianb/face-alignment) repository. This has been modified to take batches of faces at a time. 
\ No newline at end of file
diff --git a/utils/digital_human/musetalk/utils/face_detection/__init__.py b/utils/digital_human/musetalk/utils/face_detection/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..31c3e227b0b61c2270225cea370ffd35c4e24b99
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_detection/__init__.py
@@ -0,0 +1,7 @@
+# -*- coding: utf-8 -*-
+
+__author__ = """Adrian Bulat"""
+__email__ = 'adrian.bulat@nottingham.ac.uk'
+__version__ = '1.0.1'
+
+from .api import FaceAlignment, LandmarksType, NetworkSize, YOLOv8_face
diff --git a/utils/digital_human/musetalk/utils/face_detection/__pycache__/__init__.cpython-310.pyc b/utils/digital_human/musetalk/utils/face_detection/__pycache__/__init__.cpython-310.pyc
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diff --git a/utils/digital_human/musetalk/utils/face_detection/api.py b/utils/digital_human/musetalk/utils/face_detection/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d61480ef5a402e7797fe163658683681d43a442
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_detection/api.py
@@ -0,0 +1,240 @@
+from __future__ import print_function
+import os
+import torch
+from torch.utils.model_zoo import load_url
+from enum import Enum
+import numpy as np
+import cv2
+try:
+    import urllib.request as request_file
+except BaseException:
+    import urllib as request_file
+
+from .models import FAN, ResNetDepth
+from .utils import *
+
+
+class LandmarksType(Enum):
+    """Enum class defining the type of landmarks to detect.
+
+    ``_2D`` - the detected points ``(x,y)`` are detected in a 2D space and follow the visible contour of the face
+    ``_2halfD`` - this points represent the projection of the 3D points into 3D
+    ``_3D`` - detect the points ``(x,y,z)``` in a 3D space
+
+    """
+    _2D = 1
+    _2halfD = 2
+    _3D = 3
+
+
+class NetworkSize(Enum):
+    # TINY = 1
+    # SMALL = 2
+    # MEDIUM = 3
+    LARGE = 4
+
+    def __new__(cls, value):
+        member = object.__new__(cls)
+        member._value_ = value
+        return member
+
+    def __int__(self):
+        return self.value
+
+
+
+class FaceAlignment:
+    def __init__(self, landmarks_type, network_size=NetworkSize.LARGE,
+                 device='cuda', flip_input=False, face_detector='sfd', verbose=False):
+        self.device = device
+        self.flip_input = flip_input
+        self.landmarks_type = landmarks_type
+        self.verbose = verbose
+
+        network_size = int(network_size)
+
+        if 'cuda' in device:
+            torch.backends.cudnn.benchmark = True
+#             torch.backends.cuda.matmul.allow_tf32 = False
+#             torch.backends.cudnn.benchmark = True
+#             torch.backends.cudnn.deterministic = False
+#             torch.backends.cudnn.allow_tf32 = True
+            # print('cuda start')
+
+
+        # Get the face detector
+        face_detector_module = __import__('face_detection.detection.' + face_detector,
+                                          globals(), locals(), [face_detector], 0)
+        
+        self.face_detector = face_detector_module.FaceDetector(device=device, verbose=verbose)
+
+    def get_detections_for_batch(self, images):
+        images = images[..., ::-1]
+        detected_faces = self.face_detector.detect_from_batch(images.copy())
+        results = []
+
+        for i, d in enumerate(detected_faces):
+            if len(d) == 0:
+                results.append(None)
+                continue
+            d = d[0]
+            d = np.clip(d, 0, None)
+            
+            x1, y1, x2, y2 = map(int, d[:-1])
+            results.append((x1, y1, x2, y2))
+
+        return results
+    
+    
+class YOLOv8_face:
+    def __init__(self, path = 'face_detection/weights/yolov8n-face.onnx', conf_thres=0.2, iou_thres=0.5):
+        self.conf_threshold = conf_thres
+        self.iou_threshold = iou_thres
+        self.class_names = ['face']
+        self.num_classes = len(self.class_names)
+        # Initialize model
+        self.net = cv2.dnn.readNet(path)
+        self.input_height = 640
+        self.input_width = 640
+        self.reg_max = 16
+
+        self.project = np.arange(self.reg_max)
+        self.strides = (8, 16, 32)
+        self.feats_hw = [(math.ceil(self.input_height / self.strides[i]), math.ceil(self.input_width / self.strides[i])) for i in range(len(self.strides))]
+        self.anchors = self.make_anchors(self.feats_hw)
+
+    def make_anchors(self, feats_hw, grid_cell_offset=0.5):
+        """Generate anchors from features."""
+        anchor_points = {}
+        for i, stride in enumerate(self.strides):
+            h,w = feats_hw[i]
+            x = np.arange(0, w) + grid_cell_offset  # shift x
+            y = np.arange(0, h) + grid_cell_offset  # shift y
+            sx, sy = np.meshgrid(x, y)
+            # sy, sx = np.meshgrid(y, x)
+            anchor_points[stride] = np.stack((sx, sy), axis=-1).reshape(-1, 2)
+        return anchor_points
+
+    def softmax(self, x, axis=1):
+        x_exp = np.exp(x)
+        # 如果是列向量，则axis=0
+        x_sum = np.sum(x_exp, axis=axis, keepdims=True)
+        s = x_exp / x_sum
+        return s
+    
+    def resize_image(self, srcimg, keep_ratio=True):
+        top, left, newh, neww = 0, 0, self.input_width, self.input_height
+        if keep_ratio and srcimg.shape[0] != srcimg.shape[1]:
+            hw_scale = srcimg.shape[0] / srcimg.shape[1]
+            if hw_scale > 1:
+                newh, neww = self.input_height, int(self.input_width / hw_scale)
+                img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
+                left = int((self.input_width - neww) * 0.5)
+                img = cv2.copyMakeBorder(img, 0, 0, left, self.input_width - neww - left, cv2.BORDER_CONSTANT,
+                                         value=(0, 0, 0))  # add border
+            else:
+                newh, neww = int(self.input_height * hw_scale), self.input_width
+                img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
+                top = int((self.input_height - newh) * 0.5)
+                img = cv2.copyMakeBorder(img, top, self.input_height - newh - top, 0, 0, cv2.BORDER_CONSTANT,
+                                         value=(0, 0, 0))
+        else:
+            img = cv2.resize(srcimg, (self.input_width, self.input_height), interpolation=cv2.INTER_AREA)
+        return img, newh, neww, top, left
+
+    def detect(self, srcimg):
+        input_img, newh, neww, padh, padw = self.resize_image(cv2.cvtColor(srcimg, cv2.COLOR_BGR2RGB))
+        scale_h, scale_w = srcimg.shape[0]/newh, srcimg.shape[1]/neww
+        input_img = input_img.astype(np.float32) / 255.0
+
+        blob = cv2.dnn.blobFromImage(input_img)
+        self.net.setInput(blob)
+        outputs = self.net.forward(self.net.getUnconnectedOutLayersNames())
+        # if isinstance(outputs, tuple):
+        #     outputs = list(outputs)
+        # if float(cv2.__version__[:3])>=4.7:
+        #     outputs = [outputs[2], outputs[0], outputs[1]] ###opencv4.7需要这一步，opencv4.5不需要
+        # Perform inference on the image
+        det_bboxes, det_conf, det_classid, landmarks = self.post_process(outputs, scale_h, scale_w, padh, padw)
+        return det_bboxes, det_conf, det_classid, landmarks
+
+    def post_process(self, preds, scale_h, scale_w, padh, padw):
+        bboxes, scores, landmarks = [], [], []
+        for i, pred in enumerate(preds):
+            stride = int(self.input_height/pred.shape[2])
+            pred = pred.transpose((0, 2, 3, 1))
+            
+            box = pred[..., :self.reg_max * 4]
+            cls = 1 / (1 + np.exp(-pred[..., self.reg_max * 4:-15])).reshape((-1,1))
+            kpts = pred[..., -15:].reshape((-1,15)) ### x1,y1,score1, ..., x5,y5,score5
+
+            # tmp = box.reshape(self.feats_hw[i][0], self.feats_hw[i][1], 4, self.reg_max)
+            tmp = box.reshape(-1, 4, self.reg_max)
+            bbox_pred = self.softmax(tmp, axis=-1)
+            bbox_pred = np.dot(bbox_pred, self.project).reshape((-1,4))
+
+            bbox = self.distance2bbox(self.anchors[stride], bbox_pred, max_shape=(self.input_height, self.input_width)) * stride
+            kpts[:, 0::3] = (kpts[:, 0::3] * 2.0 + (self.anchors[stride][:, 0].reshape((-1,1)) - 0.5)) * stride
+            kpts[:, 1::3] = (kpts[:, 1::3] * 2.0 + (self.anchors[stride][:, 1].reshape((-1,1)) - 0.5)) * stride
+            kpts[:, 2::3] = 1 / (1+np.exp(-kpts[:, 2::3]))
+
+            bbox -= np.array([[padw, padh, padw, padh]])  ###合理使用广播法则
+            bbox *= np.array([[scale_w, scale_h, scale_w, scale_h]])
+            kpts -= np.tile(np.array([padw, padh, 0]), 5).reshape((1,15))
+            kpts *= np.tile(np.array([scale_w, scale_h, 1]), 5).reshape((1,15))
+
+            bboxes.append(bbox)
+            scores.append(cls)
+            landmarks.append(kpts)
+
+        bboxes = np.concatenate(bboxes, axis=0)
+        scores = np.concatenate(scores, axis=0)
+        landmarks = np.concatenate(landmarks, axis=0)
+    
+        bboxes_wh = bboxes.copy()
+        bboxes_wh[:, 2:4] = bboxes[:, 2:4] - bboxes[:, 0:2]  ####xywh
+        classIds = np.argmax(scores, axis=1)
+        confidences = np.max(scores, axis=1)  ####max_class_confidence
+        
+        mask = confidences>self.conf_threshold
+        bboxes_wh = bboxes_wh[mask]  ###合理使用广播法则
+        confidences = confidences[mask]
+        classIds = classIds[mask]
+        landmarks = landmarks[mask]
+        
+        indices = cv2.dnn.NMSBoxes(bboxes_wh.tolist(), confidences.tolist(), self.conf_threshold,
+                                   self.iou_threshold).flatten()
+        if len(indices) > 0:
+            mlvl_bboxes = bboxes_wh[indices]
+            confidences = confidences[indices]
+            classIds = classIds[indices]
+            landmarks = landmarks[indices]
+            return mlvl_bboxes, confidences, classIds, landmarks
+        else:
+            print('nothing detect')
+            return np.array([]), np.array([]), np.array([]), np.array([])
+
+    def distance2bbox(self, points, distance, max_shape=None):
+        x1 = points[:, 0] - distance[:, 0]
+        y1 = points[:, 1] - distance[:, 1]
+        x2 = points[:, 0] + distance[:, 2]
+        y2 = points[:, 1] + distance[:, 3]
+        if max_shape is not None:
+            x1 = np.clip(x1, 0, max_shape[1])
+            y1 = np.clip(y1, 0, max_shape[0])
+            x2 = np.clip(x2, 0, max_shape[1])
+            y2 = np.clip(y2, 0, max_shape[0])
+        return np.stack([x1, y1, x2, y2], axis=-1)
+    
+    def draw_detections(self, image, boxes, scores, kpts):
+        for box, score, kp in zip(boxes, scores, kpts):
+            x, y, w, h = box.astype(int)
+            # Draw rectangle
+            cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), thickness=3)
+            cv2.putText(image, "face:"+str(round(score,2)), (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), thickness=2)
+            for i in range(5):
+                cv2.circle(image, (int(kp[i * 3]), int(kp[i * 3 + 1])), 4, (0, 255, 0), thickness=-1)
+                # cv2.putText(image, str(i), (int(kp[i * 3]), int(kp[i * 3 + 1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), thickness=1)
+        return image
+    
+ROOT = os.path.dirname(os.path.abspath(__file__))
\ No newline at end of file
diff --git a/utils/digital_human/musetalk/utils/face_detection/detection/__init__.py b/utils/digital_human/musetalk/utils/face_detection/detection/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a6b0402dae864a3cc5dc2a90a412fd842a0efc7
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_detection/detection/__init__.py
@@ -0,0 +1 @@
+from .core import FaceDetector
\ No newline at end of file
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diff --git a/utils/digital_human/musetalk/utils/face_detection/detection/core.py b/utils/digital_human/musetalk/utils/face_detection/detection/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f8275e8e53143f66298f75f0517c234a68778cd
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_detection/detection/core.py
@@ -0,0 +1,130 @@
+import logging
+import glob
+from tqdm import tqdm
+import numpy as np
+import torch
+import cv2
+
+
+class FaceDetector(object):
+    """An abstract class representing a face detector.
+
+    Any other face detection implementation must subclass it. All subclasses
+    must implement ``detect_from_image``, that return a list of detected
+    bounding boxes. Optionally, for speed considerations detect from path is
+    recommended.
+    """
+
+    def __init__(self, device, verbose):
+        self.device = device
+        self.verbose = verbose
+
+        if verbose:
+            if 'cpu' in device:
+                logger = logging.getLogger(__name__)
+                logger.warning("Detection running on CPU, this may be potentially slow.")
+
+        if 'cpu' not in device and 'cuda' not in device:
+            if verbose:
+                logger.error("Expected values for device are: {cpu, cuda} but got: %s", device)
+            raise ValueError
+
+    def detect_from_image(self, tensor_or_path):
+        """Detects faces in a given image.
+
+        This function detects the faces present in a provided BGR(usually)
+        image. The input can be either the image itself or the path to it.
+
+        Arguments:
+            tensor_or_path {numpy.ndarray, torch.tensor or string} -- the path
+            to an image or the image itself.
+
+        Example::
+
+            >>> path_to_image = 'data/image_01.jpg'
+            ...   detected_faces = detect_from_image(path_to_image)
+            [A list of bounding boxes (x1, y1, x2, y2)]
+            >>> image = cv2.imread(path_to_image)
+            ...   detected_faces = detect_from_image(image)
+            [A list of bounding boxes (x1, y1, x2, y2)]
+
+        """
+        raise NotImplementedError
+
+    def detect_from_directory(self, path, extensions=['.jpg', '.png'], recursive=False, show_progress_bar=True):
+        """Detects faces from all the images present in a given directory.
+
+        Arguments:
+            path {string} -- a string containing a path that points to the folder containing the images
+
+        Keyword Arguments:
+            extensions {list} -- list of string containing the extensions to be
+            consider in the following format: ``.extension_name`` (default:
+            {['.jpg', '.png']}) recursive {bool} -- option wherever to scan the
+            folder recursively (default: {False}) show_progress_bar {bool} --
+            display a progressbar (default: {True})
+
+        Example:
+        >>> directory = 'data'
+        ...   detected_faces = detect_from_directory(directory)
+        {A dictionary of [lists containing bounding boxes(x1, y1, x2, y2)]}
+
+        """
+        if self.verbose:
+            logger = logging.getLogger(__name__)
+
+        if len(extensions) == 0:
+            if self.verbose:
+                logger.error("Expected at list one extension, but none was received.")
+            raise ValueError
+
+        if self.verbose:
+            logger.info("Constructing the list of images.")
+        additional_pattern = '/**/*' if recursive else '/*'
+        files = []
+        for extension in extensions:
+            files.extend(glob.glob(path + additional_pattern + extension, recursive=recursive))
+
+        if self.verbose:
+            logger.info("Finished searching for images. %s images found", len(files))
+            logger.info("Preparing to run the detection.")
+
+        predictions = {}
+        for image_path in tqdm(files, disable=not show_progress_bar):
+            if self.verbose:
+                logger.info("Running the face detector on image: %s", image_path)
+            predictions[image_path] = self.detect_from_image(image_path)
+
+        if self.verbose:
+            logger.info("The detector was successfully run on all %s images", len(files))
+
+        return predictions
+
+    @property
+    def reference_scale(self):
+        raise NotImplementedError
+
+    @property
+    def reference_x_shift(self):
+        raise NotImplementedError
+
+    @property
+    def reference_y_shift(self):
+        raise NotImplementedError
+
+    @staticmethod
+    def tensor_or_path_to_ndarray(tensor_or_path, rgb=True):
+        """Convert path (represented as a string) or torch.tensor to a numpy.ndarray
+
+        Arguments:
+            tensor_or_path {numpy.ndarray, torch.tensor or string} -- path to the image, or the image itself
+        """
+        if isinstance(tensor_or_path, str):
+            return cv2.imread(tensor_or_path) if not rgb else cv2.imread(tensor_or_path)[..., ::-1]
+        elif torch.is_tensor(tensor_or_path):
+            # Call cpu in case its coming from cuda
+            return tensor_or_path.cpu().numpy()[..., ::-1].copy() if not rgb else tensor_or_path.cpu().numpy()
+        elif isinstance(tensor_or_path, np.ndarray):
+            return tensor_or_path[..., ::-1].copy() if not rgb else tensor_or_path
+        else:
+            raise TypeError
diff --git a/utils/digital_human/musetalk/utils/face_detection/detection/sfd/__init__.py b/utils/digital_human/musetalk/utils/face_detection/detection/sfd/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a63ecd45658f22e66c171ada751fb33764d4559
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_detection/detection/sfd/__init__.py
@@ -0,0 +1 @@
+from .sfd_detector import SFDDetector as FaceDetector
\ No newline at end of file
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diff --git a/utils/digital_human/musetalk/utils/face_detection/detection/sfd/__pycache__/sfd_detector.cpython-310.pyc b/utils/digital_human/musetalk/utils/face_detection/detection/sfd/__pycache__/sfd_detector.cpython-310.pyc
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diff --git a/utils/digital_human/musetalk/utils/face_detection/detection/sfd/bbox.py b/utils/digital_human/musetalk/utils/face_detection/detection/sfd/bbox.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bd7222e5e5f78a51944cbeed3cccbacddc46bed
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_detection/detection/sfd/bbox.py
@@ -0,0 +1,129 @@
+from __future__ import print_function
+import os
+import sys
+import cv2
+import random
+import datetime
+import time
+import math
+import argparse
+import numpy as np
+import torch
+
+try:
+    from iou import IOU
+except BaseException:
+    # IOU cython speedup 10x
+    def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2):
+        sa = abs((ax2 - ax1) * (ay2 - ay1))
+        sb = abs((bx2 - bx1) * (by2 - by1))
+        x1, y1 = max(ax1, bx1), max(ay1, by1)
+        x2, y2 = min(ax2, bx2), min(ay2, by2)
+        w = x2 - x1
+        h = y2 - y1
+        if w < 0 or h < 0:
+            return 0.0
+        else:
+            return 1.0 * w * h / (sa + sb - w * h)
+
+
+def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh):
+    xc, yc, ww, hh = (x2 + x1) / 2, (y2 + y1) / 2, x2 - x1, y2 - y1
+    dx, dy = (xc - axc) / aww, (yc - ayc) / ahh
+    dw, dh = math.log(ww / aww), math.log(hh / ahh)
+    return dx, dy, dw, dh
+
+
+def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh):
+    xc, yc = dx * aww + axc, dy * ahh + ayc
+    ww, hh = math.exp(dw) * aww, math.exp(dh) * ahh
+    x1, x2, y1, y2 = xc - ww / 2, xc + ww / 2, yc - hh / 2, yc + hh / 2
+    return x1, y1, x2, y2
+
+
+def nms(dets, thresh):
+    if 0 == len(dets):
+        return []
+    x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4]
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]])
+        xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]])
+
+        w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1)
+        ovr = w * h / (areas[i] + areas[order[1:]] - w * h)
+
+        inds = np.where(ovr <= thresh)[0]
+        order = order[inds + 1]
+
+    return keep
+
+
+def encode(matched, priors, variances):
+    """Encode the variances from the priorbox layers into the ground truth boxes
+    we have matched (based on jaccard overlap) with the prior boxes.
+    Args:
+        matched: (tensor) Coords of ground truth for each prior in point-form
+            Shape: [num_priors, 4].
+        priors: (tensor) Prior boxes in center-offset form
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        encoded boxes (tensor), Shape: [num_priors, 4]
+    """
+
+    # dist b/t match center and prior's center
+    g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
+    # encode variance
+    g_cxcy /= (variances[0] * priors[:, 2:])
+    # match wh / prior wh
+    g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
+    g_wh = torch.log(g_wh) / variances[1]
+    # return target for smooth_l1_loss
+    return torch.cat([g_cxcy, g_wh], 1)  # [num_priors,4]
+
+
+def decode(loc, priors, variances):
+    """Decode locations from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        loc (tensor): location predictions for loc layers,
+            Shape: [num_priors,4]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded bounding box predictions
+    """
+
+    boxes = torch.cat((
+        priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
+        priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
+    boxes[:, :2] -= boxes[:, 2:] / 2
+    boxes[:, 2:] += boxes[:, :2]
+    return boxes
+
+def batch_decode(loc, priors, variances):
+    """Decode locations from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        loc (tensor): location predictions for loc layers,
+            Shape: [num_priors,4]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded bounding box predictions
+    """
+
+    boxes = torch.cat((
+        priors[:, :, :2] + loc[:, :, :2] * variances[0] * priors[:, :, 2:],
+        priors[:, :, 2:] * torch.exp(loc[:, :, 2:] * variances[1])), 2)
+    boxes[:, :, :2] -= boxes[:, :, 2:] / 2
+    boxes[:, :, 2:] += boxes[:, :, :2]
+    return boxes
diff --git a/utils/digital_human/musetalk/utils/face_detection/detection/sfd/detect.py b/utils/digital_human/musetalk/utils/face_detection/detection/sfd/detect.py
new file mode 100644
index 0000000000000000000000000000000000000000..15ccdc5abf5df22937f997ec59993f5da4a14ed6
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_detection/detection/sfd/detect.py
@@ -0,0 +1,114 @@
+import torch
+import torch.nn.functional as F
+
+import os
+import sys
+import cv2
+import random
+import datetime
+import math
+import argparse
+import numpy as np
+
+import scipy.io as sio
+import zipfile
+from .net_s3fd import s3fd
+from .bbox import *
+
+
+def detect(net, img, device):
+    img = img - np.array([104, 117, 123])
+    img = img.transpose(2, 0, 1)
+    img = img.reshape((1,) + img.shape)
+
+    if 'cuda' in device:
+        torch.backends.cudnn.benchmark = True
+
+    img = torch.from_numpy(img).float().to(device)
+    BB, CC, HH, WW = img.size()
+    with torch.no_grad():
+        olist = net(img)
+
+    bboxlist = []
+    for i in range(len(olist) // 2):
+        olist[i * 2] = F.softmax(olist[i * 2], dim=1)
+    olist = [oelem.data.cpu() for oelem in olist]
+    for i in range(len(olist) // 2):
+        ocls, oreg = olist[i * 2], olist[i * 2 + 1]
+        FB, FC, FH, FW = ocls.size()  # feature map size
+        stride = 2**(i + 2)    # 4,8,16,32,64,128
+        anchor = stride * 4
+        poss = zip(*np.where(ocls[:, 1, :, :] > 0.05))
+        for Iindex, hindex, windex in poss:
+            axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride
+            score = ocls[0, 1, hindex, windex]
+            loc = oreg[0, :, hindex, windex].contiguous().view(1, 4)
+            priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]])
+            variances = [0.1, 0.2]
+            box = decode(loc, priors, variances)
+            x1, y1, x2, y2 = box[0] * 1.0
+            # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1)
+            bboxlist.append([x1, y1, x2, y2, score])
+    bboxlist = np.array(bboxlist)
+    if 0 == len(bboxlist):
+        bboxlist = np.zeros((1, 5))
+
+    return bboxlist
+
+def batch_detect(net, imgs, device):
+    imgs = imgs - np.array([104, 117, 123])
+    imgs = imgs.transpose(0, 3, 1, 2)
+
+    if 'cuda' in device:
+        torch.backends.cudnn.benchmark = True
+
+    imgs = torch.from_numpy(imgs).float().to(device)
+    BB, CC, HH, WW = imgs.size()
+    with torch.no_grad():
+        olist = net(imgs)
+#     print(olist)
+    
+    bboxlist = []
+    for i in range(len(olist) // 2):
+        olist[i * 2] = F.softmax(olist[i * 2], dim=1)
+    
+    olist = [oelem.cpu() for oelem in olist]
+    for i in range(len(olist) // 2):
+        ocls, oreg = olist[i * 2], olist[i * 2 + 1]
+        FB, FC, FH, FW = ocls.size()  # feature map size
+        stride = 2**(i + 2)    # 4,8,16,32,64,128
+        anchor = stride * 4
+        poss = zip(*np.where(ocls[:, 1, :, :] > 0.05))
+        for Iindex, hindex, windex in poss:
+            axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride
+            score = ocls[:, 1, hindex, windex]
+            loc = oreg[:, :, hindex, windex].contiguous().view(BB, 1, 4)
+            priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]).view(1, 1, 4)
+            variances = [0.1, 0.2]
+            box = batch_decode(loc, priors, variances)
+            box = box[:, 0] * 1.0
+            # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1)
+            bboxlist.append(torch.cat([box, score.unsqueeze(1)], 1).cpu().numpy())
+    bboxlist = np.array(bboxlist)
+    if 0 == len(bboxlist):
+        bboxlist = np.zeros((1, BB, 5))
+
+    return bboxlist
+
+def flip_detect(net, img, device):
+    img = cv2.flip(img, 1)
+    b = detect(net, img, device)
+
+    bboxlist = np.zeros(b.shape)
+    bboxlist[:, 0] = img.shape[1] - b[:, 2]
+    bboxlist[:, 1] = b[:, 1]
+    bboxlist[:, 2] = img.shape[1] - b[:, 0]
+    bboxlist[:, 3] = b[:, 3]
+    bboxlist[:, 4] = b[:, 4]
+    return bboxlist
+
+
+def pts_to_bb(pts):
+    min_x, min_y = np.min(pts, axis=0)
+    max_x, max_y = np.max(pts, axis=0)
+    return np.array([min_x, min_y, max_x, max_y])
diff --git a/utils/digital_human/musetalk/utils/face_detection/detection/sfd/net_s3fd.py b/utils/digital_human/musetalk/utils/face_detection/detection/sfd/net_s3fd.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc64313c277ab594d0257585c70f147606693452
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_detection/detection/sfd/net_s3fd.py
@@ -0,0 +1,129 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class L2Norm(nn.Module):
+    def __init__(self, n_channels, scale=1.0):
+        super(L2Norm, self).__init__()
+        self.n_channels = n_channels
+        self.scale = scale
+        self.eps = 1e-10
+        self.weight = nn.Parameter(torch.Tensor(self.n_channels))
+        self.weight.data *= 0.0
+        self.weight.data += self.scale
+
+    def forward(self, x):
+        norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps
+        x = x / norm * self.weight.view(1, -1, 1, 1)
+        return x
+
+
+class s3fd(nn.Module):
+    def __init__(self):
+        super(s3fd, self).__init__()
+        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
+        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
+
+        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
+        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
+
+        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
+        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
+        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
+
+        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
+        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+
+        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
+
+        self.fc6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=3)
+        self.fc7 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0)
+
+        self.conv6_1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
+        self.conv6_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1)
+
+        self.conv7_1 = nn.Conv2d(512, 128, kernel_size=1, stride=1, padding=0)
+        self.conv7_2 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
+
+        self.conv3_3_norm = L2Norm(256, scale=10)
+        self.conv4_3_norm = L2Norm(512, scale=8)
+        self.conv5_3_norm = L2Norm(512, scale=5)
+
+        self.conv3_3_norm_mbox_conf = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+        self.conv3_3_norm_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+        self.conv4_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv4_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+        self.conv5_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv5_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+
+        self.fc7_mbox_conf = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1)
+        self.fc7_mbox_loc = nn.Conv2d(1024, 4, kernel_size=3, stride=1, padding=1)
+        self.conv6_2_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
+        self.conv6_2_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+        self.conv7_2_mbox_conf = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1)
+        self.conv7_2_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        h = F.relu(self.conv1_1(x))
+        h = F.relu(self.conv1_2(h))
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv2_1(h))
+        h = F.relu(self.conv2_2(h))
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv3_1(h))
+        h = F.relu(self.conv3_2(h))
+        h = F.relu(self.conv3_3(h))
+        f3_3 = h
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv4_1(h))
+        h = F.relu(self.conv4_2(h))
+        h = F.relu(self.conv4_3(h))
+        f4_3 = h
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.conv5_1(h))
+        h = F.relu(self.conv5_2(h))
+        h = F.relu(self.conv5_3(h))
+        f5_3 = h
+        h = F.max_pool2d(h, 2, 2)
+
+        h = F.relu(self.fc6(h))
+        h = F.relu(self.fc7(h))
+        ffc7 = h
+        h = F.relu(self.conv6_1(h))
+        h = F.relu(self.conv6_2(h))
+        f6_2 = h
+        h = F.relu(self.conv7_1(h))
+        h = F.relu(self.conv7_2(h))
+        f7_2 = h
+
+        f3_3 = self.conv3_3_norm(f3_3)
+        f4_3 = self.conv4_3_norm(f4_3)
+        f5_3 = self.conv5_3_norm(f5_3)
+
+        cls1 = self.conv3_3_norm_mbox_conf(f3_3)
+        reg1 = self.conv3_3_norm_mbox_loc(f3_3)
+        cls2 = self.conv4_3_norm_mbox_conf(f4_3)
+        reg2 = self.conv4_3_norm_mbox_loc(f4_3)
+        cls3 = self.conv5_3_norm_mbox_conf(f5_3)
+        reg3 = self.conv5_3_norm_mbox_loc(f5_3)
+        cls4 = self.fc7_mbox_conf(ffc7)
+        reg4 = self.fc7_mbox_loc(ffc7)
+        cls5 = self.conv6_2_mbox_conf(f6_2)
+        reg5 = self.conv6_2_mbox_loc(f6_2)
+        cls6 = self.conv7_2_mbox_conf(f7_2)
+        reg6 = self.conv7_2_mbox_loc(f7_2)
+
+        # max-out background label
+        chunk = torch.chunk(cls1, 4, 1)
+        bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2])
+        cls1 = torch.cat([bmax, chunk[3]], dim=1)
+
+        return [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6]
diff --git a/utils/digital_human/musetalk/utils/face_detection/detection/sfd/sfd_detector.py b/utils/digital_human/musetalk/utils/face_detection/detection/sfd/sfd_detector.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fbce15253251d403754ab4348f93ae85a6ba2fb
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_detection/detection/sfd/sfd_detector.py
@@ -0,0 +1,59 @@
+import os
+import cv2
+from torch.utils.model_zoo import load_url
+
+from ..core import FaceDetector
+
+from .net_s3fd import s3fd
+from .bbox import *
+from .detect import *
+
+models_urls = {
+    's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth',
+}
+
+
+class SFDDetector(FaceDetector):
+    def __init__(self, device, path_to_detector=os.path.join(os.path.dirname(os.path.abspath(__file__)), 's3fd.pth'), verbose=False):
+        super(SFDDetector, self).__init__(device, verbose)
+
+        # Initialise the face detector
+        if not os.path.isfile(path_to_detector):
+            model_weights = load_url(models_urls['s3fd'])
+        else:
+            model_weights = torch.load(path_to_detector)
+
+        self.face_detector = s3fd()
+        self.face_detector.load_state_dict(model_weights)
+        self.face_detector.to(device)
+        self.face_detector.eval()
+
+    def detect_from_image(self, tensor_or_path):
+        image = self.tensor_or_path_to_ndarray(tensor_or_path)
+
+        bboxlist = detect(self.face_detector, image, device=self.device)
+        keep = nms(bboxlist, 0.3)
+        bboxlist = bboxlist[keep, :]
+        bboxlist = [x for x in bboxlist if x[-1] > 0.5]
+
+        return bboxlist
+
+    def detect_from_batch(self, images):
+        bboxlists = batch_detect(self.face_detector, images, device=self.device)
+        keeps = [nms(bboxlists[:, i, :], 0.3) for i in range(bboxlists.shape[1])]
+        bboxlists = [bboxlists[keep, i, :] for i, keep in enumerate(keeps)]
+        bboxlists = [[x for x in bboxlist if x[-1] > 0.5] for bboxlist in bboxlists]
+
+        return bboxlists
+
+    @property
+    def reference_scale(self):
+        return 195
+
+    @property
+    def reference_x_shift(self):
+        return 0
+
+    @property
+    def reference_y_shift(self):
+        return 0
diff --git a/utils/digital_human/musetalk/utils/face_detection/models.py b/utils/digital_human/musetalk/utils/face_detection/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee2dde32bdf72c25a4600e48efa73ffc0d4a3893
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_detection/models.py
@@ -0,0 +1,261 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+
+def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3,
+                     stride=strd, padding=padding, bias=bias)
+
+
+class ConvBlock(nn.Module):
+    def __init__(self, in_planes, out_planes):
+        super(ConvBlock, self).__init__()
+        self.bn1 = nn.BatchNorm2d(in_planes)
+        self.conv1 = conv3x3(in_planes, int(out_planes / 2))
+        self.bn2 = nn.BatchNorm2d(int(out_planes / 2))
+        self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))
+        self.bn3 = nn.BatchNorm2d(int(out_planes / 4))
+        self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))
+
+        if in_planes != out_planes:
+            self.downsample = nn.Sequential(
+                nn.BatchNorm2d(in_planes),
+                nn.ReLU(True),
+                nn.Conv2d(in_planes, out_planes,
+                          kernel_size=1, stride=1, bias=False),
+            )
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        residual = x
+
+        out1 = self.bn1(x)
+        out1 = F.relu(out1, True)
+        out1 = self.conv1(out1)
+
+        out2 = self.bn2(out1)
+        out2 = F.relu(out2, True)
+        out2 = self.conv2(out2)
+
+        out3 = self.bn3(out2)
+        out3 = F.relu(out3, True)
+        out3 = self.conv3(out3)
+
+        out3 = torch.cat((out1, out2, out3), 1)
+
+        if self.downsample is not None:
+            residual = self.downsample(residual)
+
+        out3 += residual
+
+        return out3
+
+
+class Bottleneck(nn.Module):
+
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class HourGlass(nn.Module):
+    def __init__(self, num_modules, depth, num_features):
+        super(HourGlass, self).__init__()
+        self.num_modules = num_modules
+        self.depth = depth
+        self.features = num_features
+
+        self._generate_network(self.depth)
+
+    def _generate_network(self, level):
+        self.add_module('b1_' + str(level), ConvBlock(self.features, self.features))
+
+        self.add_module('b2_' + str(level), ConvBlock(self.features, self.features))
+
+        if level > 1:
+            self._generate_network(level - 1)
+        else:
+            self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features))
+
+        self.add_module('b3_' + str(level), ConvBlock(self.features, self.features))
+
+    def _forward(self, level, inp):
+        # Upper branch
+        up1 = inp
+        up1 = self._modules['b1_' + str(level)](up1)
+
+        # Lower branch
+        low1 = F.avg_pool2d(inp, 2, stride=2)
+        low1 = self._modules['b2_' + str(level)](low1)
+
+        if level > 1:
+            low2 = self._forward(level - 1, low1)
+        else:
+            low2 = low1
+            low2 = self._modules['b2_plus_' + str(level)](low2)
+
+        low3 = low2
+        low3 = self._modules['b3_' + str(level)](low3)
+
+        up2 = F.interpolate(low3, scale_factor=2, mode='nearest')
+
+        return up1 + up2
+
+    def forward(self, x):
+        return self._forward(self.depth, x)
+
+
+class FAN(nn.Module):
+
+    def __init__(self, num_modules=1):
+        super(FAN, self).__init__()
+        self.num_modules = num_modules
+
+        # Base part
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.conv2 = ConvBlock(64, 128)
+        self.conv3 = ConvBlock(128, 128)
+        self.conv4 = ConvBlock(128, 256)
+
+        # Stacking part
+        for hg_module in range(self.num_modules):
+            self.add_module('m' + str(hg_module), HourGlass(1, 4, 256))
+            self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256))
+            self.add_module('conv_last' + str(hg_module),
+                            nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
+            self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))
+            self.add_module('l' + str(hg_module), nn.Conv2d(256,
+                                                            68, kernel_size=1, stride=1, padding=0))
+
+            if hg_module < self.num_modules - 1:
+                self.add_module(
+                    'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
+                self.add_module('al' + str(hg_module), nn.Conv2d(68,
+                                                                 256, kernel_size=1, stride=1, padding=0))
+
+    def forward(self, x):
+        x = F.relu(self.bn1(self.conv1(x)), True)
+        x = F.avg_pool2d(self.conv2(x), 2, stride=2)
+        x = self.conv3(x)
+        x = self.conv4(x)
+
+        previous = x
+
+        outputs = []
+        for i in range(self.num_modules):
+            hg = self._modules['m' + str(i)](previous)
+
+            ll = hg
+            ll = self._modules['top_m_' + str(i)](ll)
+
+            ll = F.relu(self._modules['bn_end' + str(i)]
+                        (self._modules['conv_last' + str(i)](ll)), True)
+
+            # Predict heatmaps
+            tmp_out = self._modules['l' + str(i)](ll)
+            outputs.append(tmp_out)
+
+            if i < self.num_modules - 1:
+                ll = self._modules['bl' + str(i)](ll)
+                tmp_out_ = self._modules['al' + str(i)](tmp_out)
+                previous = previous + ll + tmp_out_
+
+        return outputs
+
+
+class ResNetDepth(nn.Module):
+
+    def __init__(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes=68):
+        self.inplanes = 64
+        super(ResNetDepth, self).__init__()
+        self.conv1 = nn.Conv2d(3 + 68, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AvgPool2d(7)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+
+        return x
diff --git a/utils/digital_human/musetalk/utils/face_detection/utils.py b/utils/digital_human/musetalk/utils/face_detection/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3dc4cf3e328efaa227cbcfdd969e1056688adad5
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_detection/utils.py
@@ -0,0 +1,313 @@
+from __future__ import print_function
+import os
+import sys
+import time
+import torch
+import math
+import numpy as np
+import cv2
+
+
+def _gaussian(
+        size=3, sigma=0.25, amplitude=1, normalize=False, width=None,
+        height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5,
+        mean_vert=0.5):
+    # handle some defaults
+    if width is None:
+        width = size
+    if height is None:
+        height = size
+    if sigma_horz is None:
+        sigma_horz = sigma
+    if sigma_vert is None:
+        sigma_vert = sigma
+    center_x = mean_horz * width + 0.5
+    center_y = mean_vert * height + 0.5
+    gauss = np.empty((height, width), dtype=np.float32)
+    # generate kernel
+    for i in range(height):
+        for j in range(width):
+            gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / (
+                sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0))
+    if normalize:
+        gauss = gauss / np.sum(gauss)
+    return gauss
+
+
+def draw_gaussian(image, point, sigma):
+    # Check if the gaussian is inside
+    ul = [math.floor(point[0] - 3 * sigma), math.floor(point[1] - 3 * sigma)]
+    br = [math.floor(point[0] + 3 * sigma), math.floor(point[1] + 3 * sigma)]
+    if (ul[0] > image.shape[1] or ul[1] > image.shape[0] or br[0] < 1 or br[1] < 1):
+        return image
+    size = 6 * sigma + 1
+    g = _gaussian(size)
+    g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) - int(max(1, ul[0])) + int(max(1, -ul[0]))]
+    g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) - int(max(1, ul[1])) + int(max(1, -ul[1]))]
+    img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))]
+    img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))]
+    assert (g_x[0] > 0 and g_y[1] > 0)
+    image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]
+          ] = image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]]
+    image[image > 1] = 1
+    return image
+
+
+def transform(point, center, scale, resolution, invert=False):
+    """Generate and affine transformation matrix.
+
+    Given a set of points, a center, a scale and a targer resolution, the
+    function generates and affine transformation matrix. If invert is ``True``
+    it will produce the inverse transformation.
+
+    Arguments:
+        point {torch.tensor} -- the input 2D point
+        center {torch.tensor or numpy.array} -- the center around which to perform the transformations
+        scale {float} -- the scale of the face/object
+        resolution {float} -- the output resolution
+
+    Keyword Arguments:
+        invert {bool} -- define wherever the function should produce the direct or the
+        inverse transformation matrix (default: {False})
+    """
+    _pt = torch.ones(3)
+    _pt[0] = point[0]
+    _pt[1] = point[1]
+
+    h = 200.0 * scale
+    t = torch.eye(3)
+    t[0, 0] = resolution / h
+    t[1, 1] = resolution / h
+    t[0, 2] = resolution * (-center[0] / h + 0.5)
+    t[1, 2] = resolution * (-center[1] / h + 0.5)
+
+    if invert:
+        t = torch.inverse(t)
+
+    new_point = (torch.matmul(t, _pt))[0:2]
+
+    return new_point.int()
+
+
+def crop(image, center, scale, resolution=256.0):
+    """Center crops an image or set of heatmaps
+
+    Arguments:
+        image {numpy.array} -- an rgb image
+        center {numpy.array} -- the center of the object, usually the same as of the bounding box
+        scale {float} -- scale of the face
+
+    Keyword Arguments:
+        resolution {float} -- the size of the output cropped image (default: {256.0})
+
+    Returns:
+        [type] -- [description]
+    """  # Crop around the center point
+    """ Crops the image around the center. Input is expected to be an np.ndarray """
+    ul = transform([1, 1], center, scale, resolution, True)
+    br = transform([resolution, resolution], center, scale, resolution, True)
+    # pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0)
+    if image.ndim > 2:
+        newDim = np.array([br[1] - ul[1], br[0] - ul[0],
+                           image.shape[2]], dtype=np.int32)
+        newImg = np.zeros(newDim, dtype=np.uint8)
+    else:
+        newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int)
+        newImg = np.zeros(newDim, dtype=np.uint8)
+    ht = image.shape[0]
+    wd = image.shape[1]
+    newX = np.array(
+        [max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32)
+    newY = np.array(
+        [max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32)
+    oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32)
+    oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32)
+    newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1]
+           ] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :]
+    newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)),
+                        interpolation=cv2.INTER_LINEAR)
+    return newImg
+
+
+def get_preds_fromhm(hm, center=None, scale=None):
+    """Obtain (x,y) coordinates given a set of N heatmaps. If the center
+    and the scale is provided the function will return the points also in
+    the original coordinate frame.
+
+    Arguments:
+        hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
+
+    Keyword Arguments:
+        center {torch.tensor} -- the center of the bounding box (default: {None})
+        scale {float} -- face scale (default: {None})
+    """
+    max, idx = torch.max(
+        hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2)
+    idx += 1
+    preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float()
+    preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1)
+    preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1)
+
+    for i in range(preds.size(0)):
+        for j in range(preds.size(1)):
+            hm_ = hm[i, j, :]
+            pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
+            if pX > 0 and pX < 63 and pY > 0 and pY < 63:
+                diff = torch.FloatTensor(
+                    [hm_[pY, pX + 1] - hm_[pY, pX - 1],
+                     hm_[pY + 1, pX] - hm_[pY - 1, pX]])
+                preds[i, j].add_(diff.sign_().mul_(.25))
+
+    preds.add_(-.5)
+
+    preds_orig = torch.zeros(preds.size())
+    if center is not None and scale is not None:
+        for i in range(hm.size(0)):
+            for j in range(hm.size(1)):
+                preds_orig[i, j] = transform(
+                    preds[i, j], center, scale, hm.size(2), True)
+
+    return preds, preds_orig
+
+def get_preds_fromhm_batch(hm, centers=None, scales=None):
+    """Obtain (x,y) coordinates given a set of N heatmaps. If the centers
+    and the scales is provided the function will return the points also in
+    the original coordinate frame.
+
+    Arguments:
+        hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
+
+    Keyword Arguments:
+        centers {torch.tensor} -- the centers of the bounding box (default: {None})
+        scales {float} -- face scales (default: {None})
+    """
+    max, idx = torch.max(
+        hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2)
+    idx += 1
+    preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float()
+    preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1)
+    preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1)
+
+    for i in range(preds.size(0)):
+        for j in range(preds.size(1)):
+            hm_ = hm[i, j, :]
+            pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
+            if pX > 0 and pX < 63 and pY > 0 and pY < 63:
+                diff = torch.FloatTensor(
+                    [hm_[pY, pX + 1] - hm_[pY, pX - 1],
+                     hm_[pY + 1, pX] - hm_[pY - 1, pX]])
+                preds[i, j].add_(diff.sign_().mul_(.25))
+
+    preds.add_(-.5)
+
+    preds_orig = torch.zeros(preds.size())
+    if centers is not None and scales is not None:
+        for i in range(hm.size(0)):
+            for j in range(hm.size(1)):
+                preds_orig[i, j] = transform(
+                    preds[i, j], centers[i], scales[i], hm.size(2), True)
+
+    return preds, preds_orig
+
+def shuffle_lr(parts, pairs=None):
+    """Shuffle the points left-right according to the axis of symmetry
+    of the object.
+
+    Arguments:
+        parts {torch.tensor} -- a 3D or 4D object containing the
+        heatmaps.
+
+    Keyword Arguments:
+        pairs {list of integers} -- [order of the flipped points] (default: {None})
+    """
+    if pairs is None:
+        pairs = [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0,
+                 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35,
+                 34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41,
+                 40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63,
+                 62, 61, 60, 67, 66, 65]
+    if parts.ndimension() == 3:
+        parts = parts[pairs, ...]
+    else:
+        parts = parts[:, pairs, ...]
+
+    return parts
+
+
+def flip(tensor, is_label=False):
+    """Flip an image or a set of heatmaps left-right
+
+    Arguments:
+        tensor {numpy.array or torch.tensor} -- [the input image or heatmaps]
+
+    Keyword Arguments:
+        is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False})
+    """
+    if not torch.is_tensor(tensor):
+        tensor = torch.from_numpy(tensor)
+
+    if is_label:
+        tensor = shuffle_lr(tensor).flip(tensor.ndimension() - 1)
+    else:
+        tensor = tensor.flip(tensor.ndimension() - 1)
+
+    return tensor
+
+# From pyzolib/paths.py (https://bitbucket.org/pyzo/pyzolib/src/tip/paths.py)
+
+
+def appdata_dir(appname=None, roaming=False):
+    """ appdata_dir(appname=None, roaming=False)
+
+    Get the path to the application directory, where applications are allowed
+    to write user specific files (e.g. configurations). For non-user specific
+    data, consider using common_appdata_dir().
+    If appname is given, a subdir is appended (and created if necessary).
+    If roaming is True, will prefer a roaming directory (Windows Vista/7).
+    """
+
+    # Define default user directory
+    userDir = os.getenv('FACEALIGNMENT_USERDIR', None)
+    if userDir is None:
+        userDir = os.path.expanduser('~')
+        if not os.path.isdir(userDir):  # pragma: no cover
+            userDir = '/var/tmp'  # issue #54
+
+    # Get system app data dir
+    path = None
+    if sys.platform.startswith('win'):
+        path1, path2 = os.getenv('LOCALAPPDATA'), os.getenv('APPDATA')
+        path = (path2 or path1) if roaming else (path1 or path2)
+    elif sys.platform.startswith('darwin'):
+        path = os.path.join(userDir, 'Library', 'Application Support')
+    # On Linux and as fallback
+    if not (path and os.path.isdir(path)):
+        path = userDir
+
+    # Maybe we should store things local to the executable (in case of a
+    # portable distro or a frozen application that wants to be portable)
+    prefix = sys.prefix
+    if getattr(sys, 'frozen', None):
+        prefix = os.path.abspath(os.path.dirname(sys.executable))
+    for reldir in ('settings', '../settings'):
+        localpath = os.path.abspath(os.path.join(prefix, reldir))
+        if os.path.isdir(localpath):  # pragma: no cover
+            try:
+                open(os.path.join(localpath, 'test.write'), 'wb').close()
+                os.remove(os.path.join(localpath, 'test.write'))
+            except IOError:
+                pass  # We cannot write in this directory
+            else:
+                path = localpath
+                break
+
+    # Get path specific for this app
+    if appname:
+        if path == userDir:
+            appname = '.' + appname.lstrip('.')  # Make it a hidden directory
+        path = os.path.join(path, appname)
+        if not os.path.isdir(path):  # pragma: no cover
+            os.mkdir(path)
+
+    # Done
+    return path
diff --git a/utils/digital_human/musetalk/utils/face_parsing/__init__.py b/utils/digital_human/musetalk/utils/face_parsing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a8e2c024d4aa0ca0b7549e22e98e6da2f6d6be9
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_parsing/__init__.py
@@ -0,0 +1,58 @@
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+
+from .model import BiSeNet
+
+
+class FaceParsing:
+    def __init__(self, resnet_path, face_model_pth):
+        self.resnet_path = resnet_path
+        self.model_pth = face_model_pth
+
+        self.net = self.model_init()
+        self.preprocess = self.image_preprocess()
+
+    def model_init(self):
+        net = BiSeNet(self.resnet_path)
+        if torch.cuda.is_available():
+            net.cuda()
+            net.load_state_dict(torch.load(self.model_pth))
+        else:
+            net.load_state_dict(torch.load(self.model_pth, map_location=torch.device("cpu")))
+        net.eval()
+        return net
+
+    def image_preprocess(self):
+        return transforms.Compose(
+            [
+                transforms.ToTensor(),
+                transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+            ]
+        )
+
+    def __call__(self, image, size=(512, 512)):
+        if isinstance(image, str):
+            image = Image.open(image)
+
+        width, height = image.size
+        with torch.no_grad():
+            image = image.resize(size, Image.BILINEAR)
+            img = self.preprocess(image)
+            if torch.cuda.is_available():
+                img = torch.unsqueeze(img, 0).cuda()
+            else:
+                img = torch.unsqueeze(img, 0)
+            out = self.net(img)[0]
+            parsing = out.squeeze(0).cpu().numpy().argmax(0)
+            parsing[np.where(parsing > 13)] = 0
+            parsing[np.where(parsing >= 1)] = 255
+        parsing = Image.fromarray(parsing.astype(np.uint8))
+        return parsing
+
+
+if __name__ == "__main__":
+    fp = FaceParsing()
+    segmap = fp("154_small.png")
+    segmap.save("res.png")
diff --git a/utils/digital_human/musetalk/utils/face_parsing/__pycache__/__init__.cpython-310.pyc b/utils/digital_human/musetalk/utils/face_parsing/__pycache__/__init__.cpython-310.pyc
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diff --git a/utils/digital_human/musetalk/utils/face_parsing/__pycache__/model.cpython-310.pyc b/utils/digital_human/musetalk/utils/face_parsing/__pycache__/model.cpython-310.pyc
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diff --git a/utils/digital_human/musetalk/utils/face_parsing/__pycache__/resnet.cpython-310.pyc b/utils/digital_human/musetalk/utils/face_parsing/__pycache__/resnet.cpython-310.pyc
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diff --git a/utils/digital_human/musetalk/utils/face_parsing/model.py b/utils/digital_human/musetalk/utils/face_parsing/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..13d13e03b38508216615ac15cb42e3ea0db2d76a
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_parsing/model.py
@@ -0,0 +1,283 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+from .resnet import Resnet18
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
+        super(ConvBNReLU, self).__init__()
+        self.conv = nn.Conv2d(in_chan,
+                out_chan,
+                kernel_size = ks,
+                stride = stride,
+                padding = padding,
+                bias = False)
+        self.bn = nn.BatchNorm2d(out_chan)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = F.relu(self.bn(x))
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+class BiSeNetOutput(nn.Module):
+    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
+        super(BiSeNetOutput, self).__init__()
+        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
+        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.conv_out(x)
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class AttentionRefinementModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(AttentionRefinementModule, self).__init__()
+        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
+        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
+        self.bn_atten = nn.BatchNorm2d(out_chan)
+        self.sigmoid_atten = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv(x)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv_atten(atten)
+        atten = self.bn_atten(atten)
+        atten = self.sigmoid_atten(atten)
+        out = torch.mul(feat, atten)
+        return out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+
+class ContextPath(nn.Module):
+    def __init__(self, resnet_path, *args, **kwargs):
+        super(ContextPath, self).__init__()
+        self.resnet = Resnet18(resnet_path)
+        self.arm16 = AttentionRefinementModule(256, 128)
+        self.arm32 = AttentionRefinementModule(512, 128)
+        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
+
+        self.init_weight()
+
+    def forward(self, x):
+        H0, W0 = x.size()[2:]
+        feat8, feat16, feat32 = self.resnet(x)
+        H8, W8 = feat8.size()[2:]
+        H16, W16 = feat16.size()[2:]
+        H32, W32 = feat32.size()[2:]
+
+        avg = F.avg_pool2d(feat32, feat32.size()[2:])
+        avg = self.conv_avg(avg)
+        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
+
+        feat32_arm = self.arm32(feat32)
+        feat32_sum = feat32_arm + avg_up
+        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
+        feat32_up = self.conv_head32(feat32_up)
+
+        feat16_arm = self.arm16(feat16)
+        feat16_sum = feat16_arm + feat32_up
+        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
+        feat16_up = self.conv_head16(feat16_up)
+
+        return feat8, feat16_up, feat32_up  # x8, x8, x16
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+### This is not used, since I replace this with the resnet feature with the same size
+class SpatialPath(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(SpatialPath, self).__init__()
+        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
+        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv1(x)
+        feat = self.conv2(feat)
+        feat = self.conv3(feat)
+        feat = self.conv_out(feat)
+        return feat
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class FeatureFusionModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(FeatureFusionModule, self).__init__()
+        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
+        self.conv1 = nn.Conv2d(out_chan,
+                out_chan//4,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.conv2 = nn.Conv2d(out_chan//4,
+                out_chan,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.relu = nn.ReLU(inplace=True)
+        self.sigmoid = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, fsp, fcp):
+        fcat = torch.cat([fsp, fcp], dim=1)
+        feat = self.convblk(fcat)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv1(atten)
+        atten = self.relu(atten)
+        atten = self.conv2(atten)
+        atten = self.sigmoid(atten)
+        feat_atten = torch.mul(feat, atten)
+        feat_out = feat_atten + feat
+        return feat_out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class BiSeNet(nn.Module):
+    def __init__(self, resnet_path='models/resnet18-5c106cde.pth', n_classes=19, *args, **kwargs):
+        super(BiSeNet, self).__init__()
+        self.cp = ContextPath(resnet_path)
+        ## here self.sp is deleted
+        self.ffm = FeatureFusionModule(256, 256)
+        self.conv_out = BiSeNetOutput(256, 256, n_classes)
+        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
+        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
+        self.init_weight()
+
+    def forward(self, x):
+        H, W = x.size()[2:]
+        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
+        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
+        feat_fuse = self.ffm(feat_sp, feat_cp8)
+
+        feat_out = self.conv_out(feat_fuse)
+        feat_out16 = self.conv_out16(feat_cp8)
+        feat_out32 = self.conv_out32(feat_cp16)
+
+        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
+        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
+        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
+        return feat_out, feat_out16, feat_out32
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
+        for name, child in self.named_children():
+            child_wd_params, child_nowd_params = child.get_params()
+            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
+                lr_mul_wd_params += child_wd_params
+                lr_mul_nowd_params += child_nowd_params
+            else:
+                wd_params += child_wd_params
+                nowd_params += child_nowd_params
+        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
+
+
+if __name__ == "__main__":
+    net = BiSeNet(19)
+    net.cuda()
+    net.eval()
+    in_ten = torch.randn(16, 3, 640, 480).cuda()
+    out, out16, out32 = net(in_ten)
+    print(out.shape)
+
+    net.get_params()
diff --git a/utils/digital_human/musetalk/utils/face_parsing/resnet.py b/utils/digital_human/musetalk/utils/face_parsing/resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2e5d87e34439e42f85b1cf80e83b750b4a38eac
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/face_parsing/resnet.py
@@ -0,0 +1,109 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.model_zoo as modelzoo
+
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    def __init__(self, in_chan, out_chan, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(in_chan, out_chan, stride)
+        self.bn1 = nn.BatchNorm2d(out_chan)
+        self.conv2 = conv3x3(out_chan, out_chan)
+        self.bn2 = nn.BatchNorm2d(out_chan)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = None
+        if in_chan != out_chan or stride != 1:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(in_chan, out_chan,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_chan),
+                )
+
+    def forward(self, x):
+        residual = self.conv1(x)
+        residual = F.relu(self.bn1(residual))
+        residual = self.conv2(residual)
+        residual = self.bn2(residual)
+
+        shortcut = x
+        if self.downsample is not None:
+            shortcut = self.downsample(x)
+
+        out = shortcut + residual
+        out = self.relu(out)
+        return out
+
+
+def create_layer_basic(in_chan, out_chan, bnum, stride=1):
+    layers = [BasicBlock(in_chan, out_chan, stride=stride)]
+    for i in range(bnum-1):
+        layers.append(BasicBlock(out_chan, out_chan, stride=1))
+    return nn.Sequential(*layers)
+
+
+class Resnet18(nn.Module):
+    def __init__(self, model_path):
+        super(Resnet18, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
+        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
+        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
+        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
+        self.init_weight(model_path)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(self.bn1(x))
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        feat8 = self.layer2(x) # 1/8
+        feat16 = self.layer3(feat8) # 1/16
+        feat32 = self.layer4(feat16) # 1/32
+        return feat8, feat16, feat32
+
+    def init_weight(self, model_path):
+        state_dict = torch.load(model_path) #modelzoo.load_url(resnet18_url)
+        self_state_dict = self.state_dict()
+        for k, v in state_dict.items():
+            if 'fc' in k: continue
+            self_state_dict.update({k: v})
+        self.load_state_dict(self_state_dict)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module,  nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+if __name__ == "__main__":
+    net = Resnet18()
+    x = torch.randn(16, 3, 224, 224)
+    out = net(x)
+    print(out[0].size())
+    print(out[1].size())
+    print(out[2].size())
+    net.get_params()
diff --git a/utils/digital_human/musetalk/utils/preprocessing.py b/utils/digital_human/musetalk/utils/preprocessing.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ee34eaf64392763333980f73318c3cc3c7d0830
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/preprocessing.py
@@ -0,0 +1,163 @@
+import pickle
+
+import cv2
+import numpy as np
+import torch
+from face_detection import FaceAlignment, LandmarksType
+from mmpose.apis import inference_topdown
+from mmpose.structures import merge_data_samples
+from tqdm import tqdm
+
+# initialize the face detection model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+fa = FaceAlignment(LandmarksType._2D, flip_input=False, device=device)
+
+# maker if the bbox is not sufficient
+coord_placeholder = (0.0, 0.0, 0.0, 0.0)
+
+
+def resize_landmark(landmark, w, h, new_w, new_h):
+    w_ratio = new_w / w
+    h_ratio = new_h / h
+    landmark_norm = landmark / [w, h]
+    landmark_resized = landmark_norm * [new_w, new_h]
+    return landmark_resized
+
+
+def read_imgs(img_list):
+    frames = []
+    print("reading images...")
+    for img_path in tqdm(img_list):
+        frame = cv2.imread(img_path)
+        frames.append(frame)
+    return frames
+
+
+def get_bbox_range(img_list, model, upperbondrange=0):
+    frames = read_imgs(img_list)
+    batch_size_fa = 1
+    batches = [frames[i : i + batch_size_fa] for i in range(0, len(frames), batch_size_fa)]
+    coords_list = []
+    landmarks = []
+    if upperbondrange != 0:
+        print("get key_landmark and face bounding boxes with the bbox_shift:", upperbondrange)
+    else:
+        print("get key_landmark and face bounding boxes with the default value")
+    average_range_minus = []
+    average_range_plus = []
+    for fb in tqdm(batches):
+        results = inference_topdown(model, np.asarray(fb)[0])
+        results = merge_data_samples(results)
+        keypoints = results.pred_instances.keypoints
+        face_land_mark = keypoints[0][23:91]
+        face_land_mark = face_land_mark.astype(np.int32)
+
+        # get bounding boxes by face detetion
+        bbox = fa.get_detections_for_batch(np.asarray(fb))
+
+        # adjust the bounding box refer to landmark
+        # Add the bounding box to a tuple and append it to the coordinates list
+        for j, f in enumerate(bbox):
+            if f is None:  # no face in the image
+                coords_list += [coord_placeholder]
+                continue
+
+            half_face_coord = face_land_mark[29]  # np.mean([face_land_mark[28], face_land_mark[29]], axis=0)
+            range_minus = (face_land_mark[30] - face_land_mark[29])[1]
+            range_plus = (face_land_mark[29] - face_land_mark[28])[1]
+            average_range_minus.append(range_minus)
+            average_range_plus.append(range_plus)
+            if upperbondrange != 0:
+                half_face_coord[1] = upperbondrange + half_face_coord[1]  # 手动调整  + 向下（偏29）  - 向上（偏28）
+
+    text_range = f"Total frame:「{len(frames)}」 Manually adjust range : [ -{int(sum(average_range_minus) / len(average_range_minus))}~{int(sum(average_range_plus) / len(average_range_plus))} ] , the current value: {upperbondrange}"
+    return text_range
+
+
+def get_landmark_and_bbox(img_list, model, upperbondrange=0):
+    frames = read_imgs(img_list)
+    batch_size_fa = 1
+    batches = [frames[i : i + batch_size_fa] for i in range(0, len(frames), batch_size_fa)]
+    coords_list = []
+    landmarks = []
+    if upperbondrange != 0:
+        print("get key_landmark and face bounding boxes with the bbox_shift:", upperbondrange)
+    else:
+        print("get key_landmark and face bounding boxes with the default value")
+    average_range_minus = []
+    average_range_plus = []
+    for fb in tqdm(batches):
+        results = inference_topdown(model, np.asarray(fb)[0])
+        results = merge_data_samples(results)
+        keypoints = results.pred_instances.keypoints
+        face_land_mark = keypoints[0][23:91]
+        face_land_mark = face_land_mark.astype(np.int32)
+
+        # get bounding boxes by face detetion
+        bbox = fa.get_detections_for_batch(np.asarray(fb))
+
+        # adjust the bounding box refer to landmark
+        # Add the bounding box to a tuple and append it to the coordinates list
+        for j, f in enumerate(bbox):
+            if f is None:  # no face in the image
+                coords_list += [coord_placeholder]
+                continue
+
+            half_face_coord = face_land_mark[29]  # np.mean([face_land_mark[28], face_land_mark[29]], axis=0)
+            range_minus = (face_land_mark[30] - face_land_mark[29])[1]
+            range_plus = (face_land_mark[29] - face_land_mark[28])[1]
+            average_range_minus.append(range_minus)
+            average_range_plus.append(range_plus)
+            if upperbondrange != 0:
+                half_face_coord[1] = upperbondrange + half_face_coord[1]  # 手动调整  + 向下（偏29）  - 向上（偏28）
+            half_face_dist = np.max(face_land_mark[:, 1]) - half_face_coord[1]
+            upper_bond = half_face_coord[1] - half_face_dist
+
+            f_landmark = (
+                np.min(face_land_mark[:, 0]),
+                int(upper_bond),
+                np.max(face_land_mark[:, 0]),
+                np.max(face_land_mark[:, 1]),
+            )
+            x1, y1, x2, y2 = f_landmark
+
+            if y2 - y1 <= 0 or x2 - x1 <= 0 or x1 < 0:  # if the landmark bbox is not suitable, reuse the bbox
+                coords_list += [f]
+                w, h = f[2] - f[0], f[3] - f[1]
+                print("error bbox:", f)
+            else:
+                coords_list += [f_landmark]
+
+    print(
+        "********************************************bbox_shift parameter adjustment**********************************************************"
+    )
+    print(
+        f"Total frame:「{len(frames)}」 Manually adjust range : [ -{int(sum(average_range_minus) / len(average_range_minus))}~{int(sum(average_range_plus) / len(average_range_plus))} ] , the current value: {upperbondrange}"
+    )
+    print(
+        "*************************************************************************************************************************************"
+    )
+    return coords_list, frames
+
+
+if __name__ == "__main__":
+    img_list = [
+        "./results/lyria/00000.png",
+        "./results/lyria/00001.png",
+        "./results/lyria/00002.png",
+        "./results/lyria/00003.png",
+    ]
+    crop_coord_path = "./coord_face.pkl"
+    coords_list, full_frames = get_landmark_and_bbox(img_list)
+    with open(crop_coord_path, "wb") as f:
+        pickle.dump(coords_list, f)
+
+    for bbox, frame in zip(coords_list, full_frames):
+        if bbox == coord_placeholder:
+            continue
+        x1, y1, x2, y2 = bbox
+        crop_frame = frame[y1:y2, x1:x2]
+        print("Cropped shape", crop_frame.shape)
+
+        # cv2.imwrite(path.join(save_dir, '{}.png'.format(i)),full_frames[i][0][y1:y2, x1:x2])
+    print(coords_list)
diff --git a/utils/digital_human/musetalk/utils/utils.py b/utils/digital_human/musetalk/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5263a984f2ba73b8f90f3c25e97fe91aa734b930
--- /dev/null
+++ b/utils/digital_human/musetalk/utils/utils.py
@@ -0,0 +1,64 @@
+import os
+import cv2
+import numpy as np
+import torch
+
+# ffmpeg_path = os.getenv('FFMPEG_PATH')
+# if ffmpeg_path is None:
+#     print("please download ffmpeg-static and export to FFMPEG_PATH. \nFor example: export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static")
+# elif ffmpeg_path not in os.getenv('PATH'):
+#     print("add ffmpeg to path")
+#     os.environ["PATH"] = f"{ffmpeg_path}:{os.environ['PATH']}"
+
+    
+from utils.digital_human.musetalk.whisper.audio2feature import Audio2Feature
+from utils.digital_human.musetalk.models.vae import VAE
+from utils.digital_human.musetalk.models.unet import UNet,PositionalEncoding
+
+def load_all_model(audio2feature_model_path, vae_model_path, unet_model_dict, ):
+    audio_processor = Audio2Feature(model_path=audio2feature_model_path)
+    vae = VAE(model_path =vae_model_path)
+    unet = UNet(unet_config=unet_model_dict['unet_config'],
+                model_path =unet_model_dict['model_path'])
+    pe = PositionalEncoding(d_model=384)
+    return audio_processor,vae,unet,pe
+
+def get_file_type(video_path):
+    _, ext = os.path.splitext(video_path)
+
+    if ext.lower() in ['.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff']:
+        return 'image'
+    elif ext.lower() in ['.avi', '.mp4', '.mov', '.flv', '.mkv']:
+        return 'video'
+    else:
+        return 'unsupported'
+
+def get_video_fps(video_path):
+    video = cv2.VideoCapture(video_path)
+    fps = video.get(cv2.CAP_PROP_FPS)
+    video.release()
+    return fps
+
+def datagen(whisper_chunks,
+            vae_encode_latents,
+            batch_size=8,
+            delay_frame=0):
+    whisper_batch, latent_batch = [], []
+    for i, w in enumerate(whisper_chunks):
+        idx = (i+delay_frame)%len(vae_encode_latents)
+        latent = vae_encode_latents[idx]
+        whisper_batch.append(w)
+        latent_batch.append(latent)
+
+        if len(latent_batch) >= batch_size:
+            whisper_batch = np.stack(whisper_batch)
+            latent_batch = torch.cat(latent_batch, dim=0)
+            yield whisper_batch, latent_batch
+            whisper_batch, latent_batch = [], []
+
+    # the last batch may smaller than batch size
+    if len(latent_batch) > 0:
+        whisper_batch = np.stack(whisper_batch)
+        latent_batch = torch.cat(latent_batch, dim=0)
+
+        yield whisper_batch, latent_batch
diff --git a/utils/digital_human/musetalk/whisper/__pycache__/audio2feature.cpython-310.pyc b/utils/digital_human/musetalk/whisper/__pycache__/audio2feature.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3c9745b235b3de2b9447705b46bf1e55571c0991
Binary files /dev/null and b/utils/digital_human/musetalk/whisper/__pycache__/audio2feature.cpython-310.pyc differ
diff --git a/utils/digital_human/musetalk/whisper/audio2feature.py b/utils/digital_human/musetalk/whisper/audio2feature.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cfd3a9af14ff2fa4c77944371b264c7dd759d7c
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/audio2feature.py
@@ -0,0 +1,128 @@
+import os
+from .whisper import load_model
+import soundfile as sf
+import numpy as np
+import time
+import sys
+sys.path.append("..")
+
+class Audio2Feature():
+    def __init__(self, 
+                 whisper_model_type="tiny",
+                 model_path="./models/whisper/tiny.pt"):
+        self.whisper_model_type = whisper_model_type
+        self.model = load_model(model_path) #
+
+    def get_sliced_feature(self,
+                           feature_array, 
+                           vid_idx, 
+                           audio_feat_length=[2,2],
+                           fps=25):
+        """
+        Get sliced features based on a given index
+        :param feature_array: 
+        :param start_idx: the start index of the feature
+        :param audio_feat_length:
+        :return: 
+        """
+        length = len(feature_array)
+        selected_feature = []
+        selected_idx = []
+        
+        center_idx = int(vid_idx*50/fps) 
+        left_idx = center_idx-audio_feat_length[0]*2
+        right_idx = center_idx + (audio_feat_length[1]+1)*2
+        
+        for idx in range(left_idx,right_idx):
+            idx = max(0, idx)
+            idx = min(length-1, idx)
+            x = feature_array[idx]
+            selected_feature.append(x)
+            selected_idx.append(idx)
+        
+        selected_feature = np.concatenate(selected_feature, axis=0)
+        selected_feature = selected_feature.reshape(-1, 384)# 50*384
+        return selected_feature,selected_idx
+
+    def get_sliced_feature_sparse(self,feature_array, vid_idx, audio_feat_length= [2,2],fps = 25):
+        """
+        Get sliced features based on a given index
+        :param feature_array: 
+        :param start_idx: the start index of the feature
+        :param audio_feat_length:
+        :return: 
+        """
+        length = len(feature_array)
+        selected_feature = []
+        selected_idx = []
+
+        for dt in range(-audio_feat_length[0],audio_feat_length[1]+1):
+            left_idx = int((vid_idx+dt)*50/fps)
+            if left_idx<1 or left_idx>length-1:
+                left_idx = max(0, left_idx)
+                left_idx = min(length-1, left_idx)
+
+                x = feature_array[left_idx]
+                x = x[np.newaxis,:,:]
+                x = np.repeat(x, 2, axis=0)
+                selected_feature.append(x)
+                selected_idx.append(left_idx)
+                selected_idx.append(left_idx)
+            else:
+                x = feature_array[left_idx-1:left_idx+1]
+                selected_feature.append(x)
+                selected_idx.append(left_idx-1)
+                selected_idx.append(left_idx)
+        selected_feature = np.concatenate(selected_feature, axis=0)
+        selected_feature = selected_feature.reshape(-1, 384)# 50*384
+        return selected_feature,selected_idx
+    
+
+    def feature2chunks(self,feature_array,fps,audio_feat_length = [2,2]):
+        whisper_chunks = []
+        whisper_idx_multiplier = 50./fps 
+        i = 0
+        print(f"video in {fps} FPS, audio idx in 50FPS")
+        while 1:
+            start_idx = int(i * whisper_idx_multiplier)
+            selected_feature,selected_idx = self.get_sliced_feature(feature_array= feature_array,vid_idx = i,audio_feat_length=audio_feat_length,fps=fps)
+            #print(f"i:{i},selected_idx {selected_idx}")
+            whisper_chunks.append(selected_feature)
+            i += 1
+            if start_idx>len(feature_array):
+                break
+
+        return whisper_chunks
+
+    def audio2feat(self,audio_path):
+        # get the sample rate of the audio
+        result = self.model.transcribe(audio_path)
+        embed_list = []
+        for emb in result['segments']:
+            encoder_embeddings = emb['encoder_embeddings']
+            encoder_embeddings = encoder_embeddings.transpose(0,2,1,3)
+            encoder_embeddings = encoder_embeddings.squeeze(0)
+            start_idx = int(emb['start'])
+            end_idx = int(emb['end'])
+            emb_end_idx = int((end_idx - start_idx)/2)
+            embed_list.append(encoder_embeddings[:emb_end_idx])
+        concatenated_array = np.concatenate(embed_list, axis=0)
+        return concatenated_array
+
+if __name__ == "__main__":
+    audio_processor = Audio2Feature(model_path="../../models/whisper/whisper_tiny.pt")
+    audio_path = "./test.mp3"
+    array = audio_processor.audio2feat(audio_path)
+    print(array.shape)
+    fps = 25
+    whisper_idx_multiplier = 50./fps 
+
+    i = 0
+    print(f"video in {fps} FPS, audio idx in 50FPS")
+    while 1:
+        start_idx = int(i * whisper_idx_multiplier)
+        selected_feature,selected_idx = audio_processor.get_sliced_feature(feature_array= array,vid_idx = i,audio_feat_length=[2,2],fps=fps)
+        print(f"video idx {i},\t audio idx {selected_idx},\t shape {selected_feature.shape}")
+        i += 1
+        if start_idx>len(array):
+            break
diff --git a/utils/digital_human/musetalk/whisper/whisper/__init__.py b/utils/digital_human/musetalk/whisper/whisper/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9255534ceae3014481780ab0571b4edb6523212
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/__init__.py
@@ -0,0 +1,116 @@
+import hashlib
+import io
+import os
+import urllib
+import warnings
+from typing import List, Optional, Union
+
+import torch
+from tqdm import tqdm
+
+from .audio import load_audio, log_mel_spectrogram, pad_or_trim
+from .decoding import DecodingOptions, DecodingResult, decode, detect_language
+from .model import Whisper, ModelDimensions
+from .transcribe import transcribe
+
+
+_MODELS = {
+    "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
+    "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
+    "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
+    "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
+    "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
+    "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
+    "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
+    "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
+    "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
+    "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt",
+    "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
+    "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
+}
+
+
+def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]:
+    os.makedirs(root, exist_ok=True)
+
+    expected_sha256 = url.split("/")[-2]
+    download_target = os.path.join(root, os.path.basename(url))
+
+    if os.path.exists(download_target) and not os.path.isfile(download_target):
+        raise RuntimeError(f"{download_target} exists and is not a regular file")
+
+    if os.path.isfile(download_target):
+        model_bytes = open(download_target, "rb").read()
+        if hashlib.sha256(model_bytes).hexdigest() == expected_sha256:
+            return model_bytes if in_memory else download_target
+        else:
+            warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
+
+    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
+        with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
+            while True:
+                buffer = source.read(8192)
+                if not buffer:
+                    break
+
+                output.write(buffer)
+                loop.update(len(buffer))
+
+    model_bytes = open(download_target, "rb").read()
+    if hashlib.sha256(model_bytes).hexdigest() != expected_sha256:
+        raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.")
+
+    return model_bytes if in_memory else download_target
+
+
+def available_models() -> List[str]:
+    """Returns the names of available models"""
+    return list(_MODELS.keys())
+
+
+def load_model(name: str, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False) -> Whisper:
+    """
+    Load a Whisper ASR model
+
+    Parameters
+    ----------
+    name : str
+        one of the official model names listed by `whisper.available_models()`, or
+        path to a model checkpoint containing the model dimensions and the model state_dict.
+    device : Union[str, torch.device]
+        the PyTorch device to put the model into
+    download_root: str
+        path to download the model files; by default, it uses "~/.cache/whisper"
+    in_memory: bool
+        whether to preload the model weights into host memory
+
+    Returns
+    -------
+    model : Whisper
+        The Whisper ASR model instance
+    """
+
+    if device is None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+    if download_root is None:
+        download_root = os.getenv(
+            "XDG_CACHE_HOME", 
+            os.path.join(os.path.expanduser("~"), ".cache", "whisper")
+        )
+
+    if name in _MODELS:
+        checkpoint_file = _download(_MODELS[name], download_root, in_memory)
+    elif os.path.isfile(name):
+        checkpoint_file = open(name, "rb").read() if in_memory else name
+    else:
+        raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
+
+    with (io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")) as fp:
+        checkpoint = torch.load(fp, map_location=device)
+    del checkpoint_file
+
+    dims = ModelDimensions(**checkpoint["dims"])
+    model = Whisper(dims)
+    model.load_state_dict(checkpoint["model_state_dict"])
+
+    return model.to(device)
diff --git a/utils/digital_human/musetalk/whisper/whisper/__main__.py b/utils/digital_human/musetalk/whisper/whisper/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc9b04a39283f71082f46ec8bb11fe15f8cf75b2
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/__main__.py
@@ -0,0 +1,4 @@
+from .transcribe import cli
+
+
+cli()
diff --git a/utils/digital_human/musetalk/whisper/whisper/__pycache__/__init__.cpython-310.pyc b/utils/digital_human/musetalk/whisper/whisper/__pycache__/__init__.cpython-310.pyc
new file mode 100644
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diff --git a/utils/digital_human/musetalk/whisper/whisper/__pycache__/audio.cpython-310.pyc b/utils/digital_human/musetalk/whisper/whisper/__pycache__/audio.cpython-310.pyc
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diff --git a/utils/digital_human/musetalk/whisper/whisper/__pycache__/decoding.cpython-310.pyc b/utils/digital_human/musetalk/whisper/whisper/__pycache__/decoding.cpython-310.pyc
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index 0000000000000000000000000000000000000000..dd370db4ad2942b8fe2ce3973c67e79023f48d8b
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diff --git a/utils/digital_human/musetalk/whisper/whisper/__pycache__/model.cpython-310.pyc b/utils/digital_human/musetalk/whisper/whisper/__pycache__/model.cpython-310.pyc
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diff --git a/utils/digital_human/musetalk/whisper/whisper/__pycache__/tokenizer.cpython-310.pyc b/utils/digital_human/musetalk/whisper/whisper/__pycache__/tokenizer.cpython-310.pyc
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index 0000000000000000000000000000000000000000..5dfa226fac2003652fa3905bfe3b58af95b42be3
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diff --git a/utils/digital_human/musetalk/whisper/whisper/__pycache__/transcribe.cpython-310.pyc b/utils/digital_human/musetalk/whisper/whisper/__pycache__/transcribe.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8af0550f9e804eba99824d178d11906611d4ffb9
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diff --git a/utils/digital_human/musetalk/whisper/whisper/__pycache__/utils.cpython-310.pyc b/utils/digital_human/musetalk/whisper/whisper/__pycache__/utils.cpython-310.pyc
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diff --git a/utils/digital_human/musetalk/whisper/whisper/assets/gpt2/merges.txt b/utils/digital_human/musetalk/whisper/whisper/assets/gpt2/merges.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6636bda4a1fd7a63653dffb22683b8162c8de956
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/assets/gpt2/merges.txt
@@ -0,0 +1,50001 @@
+#version: 0.2 - Trained by `huggingface/tokenizers`
+Ġ t
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diff --git a/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/added_tokens.json b/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/added_tokens.json
new file mode 100644
index 0000000000000000000000000000000000000000..5c509999d68b2eb119d8a4d1218376910beac339
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/added_tokens.json
@@ -0,0 +1 @@
+{"<|endoftext|>": 50257}
diff --git a/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/merges.txt b/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/merges.txt
new file mode 100644
index 0000000000000000000000000000000000000000..afde9f4fa8bcf6d4762c5780977825666dc49eac
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/merges.txt
@@ -0,0 +1,50000 @@
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diff --git a/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/special_tokens_map.json b/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..817762d631ad6f9c799f6b9dc713c46420e65546
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/special_tokens_map.json
@@ -0,0 +1 @@
+{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}
\ No newline at end of file
diff --git a/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/tokenizer_config.json b/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..0235ccff90efbc022fbe30360220375c5bad68da
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/tokenizer_config.json
@@ -0,0 +1 @@
+{"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "multilingual", "errors": "replace", "tokenizer_class": "GPT2Tokenizer"}
\ No newline at end of file
diff --git a/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/vocab.json b/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/vocab.json
new file mode 100644
index 0000000000000000000000000000000000000000..406c3f2600089389bd8bf3920c2fa3770d11789a
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/assets/multilingual/vocab.json
@@ -0,0 +1 @@
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13255, "Ġнадо": 13256, "Ġbarrel": 13257, "ãĥŀ": 13258, "ARD": 13259, "Ġinstallation": 13260, "Ġinsect": 13261, "Ġëħ¸ë": 13262, "ujÄħ": 13263, "ĠÄiji": 13264, "Ġpacked": 13265, "Ġfiction": 13266, "Now": 13267, "ĠYay": 13268, "Ġpert": 13269, "rons": 13270, "unde": 13271, "aches": 13272, "Ġstyles": 13273, "Ġaprès": 13274, "oku": 13275, "ĠVice": 13276, "ınız": 13277, "comm": 13278, "Ġassigned": 13279, "Ġinteractions": 13280, "Ġacab": 13281, "FELIPE": 13282, "Ġrescue": 13283, "Ġindustries": 13284, "ĠAndy": 13285, "Ġpraise": 13286, "Ġflame": 13287, "Ġsnack": 13288, "íĤ": 13289, "çģ": 13290, "Ġswo": 13291, "render": 13292, "Ġboards": 13293, "ĠÑĤом": 13294, "enne": 13295, "Ġpasta": 13296, "Ġdevil": 13297, "ĠFel": 13298, "Ġhatte": 13299, "Ġcolleg": 13300, "eh": 13301, "ì»": 13302, "ãģĵãģ®": 13303, "Ġproductive": 13304, "forward": 13305, "ип": 13306, "Ġsmartphone": 13307, "Ġinvis": 13308, "Ġbum": 13309, "Ġwhoa": 13310, "ìŀĦ": 13311, "ĠocksÃ¥": 13312, "ĠLang": 13313, "ĠSyria": 13314, "Ġsesi": 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"Ġritual": 13792, "Ġ>>>": 13793, "Ġtempt": 13794, "ĠTech": 13795, "ĠPokemon": 13796, "Ġimprovements": 13797, "Ġspare": 13798, "Ġtranslate": 13799, "Ġsonra": 13800, "ĠFilm": 13801, "wort": 13802, "Ġми": 13803, "Ġperiods": 13804, "Ġjealous": 13805, "ãģĦãģĦ": 13806, "Ġtir": 13807, "MI": 13808, "Ġconducted": 13809, "ĠìķĪëħķ": 13810, "09": 13811, "ĠPolit": 13812, "ĠWhereas": 13813, "Ġmoisture": 13814, "Ġsins": 13815, "Ġkap": 13816, "ĠÑįк": 13817, "Ġbenim": 13818, "Ġeliminate": 13819, "Ġathletes": 13820, "ĠManager": 13821, "Ġfeatured": 13822, "apore": 13823, "äºĽ": 13824, "Ġë°ľ": 13825, "Ġperf": 13826, "ĠThus": 13827, "Ġdebut": 13828, "обÑĢ": 13829, "Ġseñ": 13830, "Ġmysterious": 13831, "words": 13832, "Ķê°Ģ": 13833, "Ġchecks": 13834, "Ġvolunteer": 13835, "Ġwashing": 13836, "ĠMarvel": 13837, "ĠAB": 13838, "issors": 13839, "!'": 13840, "ĠFull": 13841, "yeon": 13842, "Ġweigh": 13843, "ĠJOHN": 13844, "Ġvos": 13845, "Ġprocedures": 13846, "Ġaddressed": 13847, "ĠBerlin": 13848, "puter": 13849, 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"ateral": 17274, "Ġdistant": 17275, "Ġbears": 17276, "Ġjournalist": 17277, "解": 17278, "ĠMarshall": 17279, "ĠIhnen": 17280, "uetooth": 17281, "bag": 17282, "ĠÄijã": 17283, "ĠHighness": 17284, "Ġì°į": 17285, "ика": 17286, "ĠWu": 17287, "ĠFran": 17288, "Ġpeng": 17289, "Ġfon": 17290, "Ġhypothesis": 17291, "ĠÑĢÑĥ": 17292, "Ġly": 17293, "×ļ": 17294, "ìĽĶ": 17295, "ĠRadio": 17296, "à¸ŀ": 17297, "Dav": 17298, "Ġembarrassing": 17299, "ĠìŀĪìĸ´": 17300, "Ġcasting": 17301, "Ġcage": 17302, "ĠPsych": 17303, "ĠìĿ¼ëĭ¨": 17304, "Ġž": 17305, "imb": 17306, "Ġdirectors": 17307, "SH": 17308, "ĠÏĦην": 17309, "á»ģu": 17310, "ĠkonuÅŁ": 17311, "Ġoptional": 17312, "quarters": 17313, "iker": 17314, "ĠSant": 17315, "Ġverses": 17316, "ë¶Ģ": 17317, "Ġolar": 17318, "ĠÏĩ": 17319, "ãĥķ": 17320, "Ġγια": 17321, "ĠImm": 17322, "Ġcontroversial": 17323, "Ġersten": 17324, "Ġrecip": 17325, "ĠChristianity": 17326, "Ġê´ľ": 17327, "ordon": 17328, "×ķש": 17329, "Ġslash": 17330, "ĠPf": 17331, "ÑĥдÑĮ": 17332, "×ķ×Ŀ": 17333, "ĠPerry": 17334, "Ġmamy": 17335, "Ġbackgrounds": 17336, "Ġà®İன": 17337, "Ġpendant": 17338, "ĠColumbia": 17339, "Ġinverse": 17340, "ĠÑĩеÑĢез": 17341, "Ġsv": 17342, "Ġdigging": 17343, "41": 17344, "chem": 17345, "Ġnavigation": 17346, "ĠShin": 17347, "ĠFront": 17348, "PD": 17349, "Ġbearing": 17350, "ĠWasser": 17351, "Ġwax": 17352, "ĠCHRIS": 17353, "ching": 17354, "Ġpressed": 17355, "El": 17356, "ĠDal": 17357, "onsin": 17358, "Ġbinding": 17359, "Ñģкой": 17360, "poons": 17361, "Ġmock": 17362, "arest": 17363, "кÑĢа": 17364, "MM": 17365, "Ġcorrupt": 17366, "storm": 17367, "Ġrefres": 17368, "ĠCoach": 17369, "llä": 17370, "ĠTHIS": 17371, "Ġparag": 17372, "Ġìĵ°": 17373, "pool": 17374, "Ġbillions": 17375, "Ġê¹Ģ": 17376, "group": 17377, "Ġwelcoming": 17378, "cellence": 17379, "ĠDuke": 17380, "긴": 17381, "Ġprimera": 17382, "ìł¸": 17383, "Ġpond": 17384, "Ġstatue": 17385, "Ġ구ë": 17386, "Ġhatch": 17387, "Ġinstrumental": 17388, "Ġresidential": 17389, "커": 17390, "Ġaccepting": 17391, "oshi": 17392, "date": 17393, "ĠìĶ¨": 17394, "Ġplanted": 17395, "Ġjoking": 17396, "ĠìĦľ": 17397, "Ġhated": 17398, "ĠÑĢаÑģÑģк": 17399, "Ġslept": 17400, "Ġpackages": 17401, "Ġislands": 17402, "esen": 17403, "ģı": 17404, "Ġdiagon": 17405, "ĠOsc": 17406, "Ġmesh": 17407, "Ġscales": 17408, "arity": 17409, "ĠDefense": 17410, "ãģ¡ãĤĩ": 17411, "ĠLewis": 17412, "ĠÑģегоднÑı": 17413, "Ġflies": 17414, "uinely": 17415, "ĠConsider": 17416, "Ġstark": 17417, "hew": 17418, "ĠAsÃŃ": 17419, "³´ë": 17420, "Ġpropose": 17421, "Ġíķĺë©´": 17422, "odo": 17423, "ĠNormally": 17424, "Ġheeft": 17425, "ĠHarris": 17426, "gro": 17427, "ĠBlood": 17428, "base": 17429, "ĠiOS": 17430, "Ġtouches": 17431, "Ġinspir": 17432, "Ġ×ĵ": 17433, "Ġbinary": 17434, "Ġì¶Ķ": 17435, "Ġserial": 17436, "Ġion": 17437, "Ġunemployment": 17438, "Ġodds": 17439, "ĠFab": 17440, "ĠFBI": 17441, "BRUN": 17442, "Ġweights": 17443, "νο": 17444, "atile": 17445, "Ġnurses": 17446, "Ġinvolvement": 17447, "ĠíĶ¼": 17448, "Ġgovernance": 17449, "ĠâĤ¬": 17450, "ÑĢÑĥп": 17451, "ierra": 17452, "íĺķ": 17453, "ĠJerry": 17454, "Ġbeard": 17455, "Ġsalvation": 17456, "ĠAlong": 17457, "gentle": 17458, "ĠKi": 17459, "bol": 17460, "ĠPlat": 17461, "Ġhasht": 17462, "è¿ij": 17463, "Ġware": 17464, "Ġpartie": 17465, "ycz": 17466, "Ġintr": 17467, "Fih": 17468, "nent": 17469, "Ġcheat": 17470, "ilen": 17471, "Ġë¯": 17472, "orie": 17473, "Ġfácil": 17474, "etric": 17475, "Ġaffecting": 17476, "unciation": 17477, "Ġaffairs": 17478, "Ġbee": 17479, "Ġviewing": 17480, "Ġorang": 17481, "ĠLan": 17482, "ĠСÑĤ": 17483, "ä¸ĸ": 17484, "ĠMes": 17485, "ĥģ": 17486, "erie": 17487, "Ġespa": 17488, "Ġinterpre": 17489, "Ġpossess": 17490, "Ġpurely": 17491, "rito": 17492, "found": 17493, "asma": 17494, "ìłģìĿ¸": 17495, "Ġexamine": 17496, "ĠÑĥм": 17497, "Ġbesch": 17498, "ĠTomorrow": 17499, "ĠBlock": 17500, "Ġvariant": 17501, "Ġpreference": 17502, "Ġcoaches": 17503, "Ġmedications": 17504, "ĠíĺĦ": 17505, "Ġempire": 17506, "ëĦ¤": 17507, "ĠIllinois": 17508, "Ġcrispy": 17509, "Ġthì": 17510, "Ġbees": 17511, "77": 17512, "Ġglow": 17513, "èº": 17514, "ĠStudies": 17515, "åIJĦ": 17516, "ĠChallenge": 17517, "Ġunlikely": 17518, "Ч": 17519, "ıyorsun": 17520, "DIE": 17521, "Ġminimize": 17522, "izard": 17523, "Ġún": 17524, "Ġencontrar": 17525, "ĠKill": 17526, "å»": 17527, "Ġvanilla": 17528, "ĠGrant": 17529, "ĠGT": 17530, "sea": 17531, "Ġsought": 17532, "вод": 17533, "Ġnäm": 17534, "ĠAunt": 17535, "OWN": 17536, "Ġpumpkin": 17537, "stellen": 17538, "Ġrag": 17539, "егда": 17540, "Ġstoryt": 17541, "Ġforum": 17542, "æ©Ł": 17543, "Ġestaba": 17544, "uche": 17545, "Ġcongress": 17546, "ĠRey": 17547, "Ġdramatically": 17548, "ĠSport": 17549, "ĠYellow": 17550, "Ġê³ĦìĨį": 17551, "Ġdisgusting": 17552, "ĠRecent": 17553, "Ġacquired": 17554, "Ġcables": 17555, "çĶļ": 17556, "din": 17557, "Ġvisto": 17558, "Ġcommunicating": 17559, "ÑģÑĤавлÑı": 17560, "еÑģÑĤо": 17561, "ãĥ»ãĥ»ãĥ»": 17562, "Ġrég": 17563, "Ġsocks": 17564, "Ġproces": 17565, "because": 17566, "Ġutter": 17567, "Ġcolocar": 17568, "Ġnewest": 17569, "Ġgramm": 17570, "表": 17571, "ä¸įçŁ¥éģĵ": 17572, "Ġshifting": 17573, "Ġcarrier": 17574, "ĠÑģкоÑĢ": 17575, "ĠSchw": 17576, "Ġexecuted": 17577, "Ġmaintained": 17578, "ĠÏĨ": 17579, "ĠMoses": 17580, "Ġdisse": 17581, "Ġhorr": 17582, "ãĢľ": 17583, "Ġrally": 17584, "Ġallem": 17585, "ĠEventually": 17586, "Ġdiyor": 17587, "lvania": 17588, "Ġschnell": 17589, "Ġê³¼": 17590, "Ġ매": 17591, "Ġstruggles": 17592, "late": 17593, "Ġclarify": 17594, "ément": 17595, "Ġmultiplic": 17596, "ибо": 17597, "Ġjourn": 17598, "Ġfragr": 17599, "Ġsurprisingly": 17600, "Ġdesperate": 17601, "52": 17602, "Ġsul": 17603, "ĠRead": 17604, "ĠFried": 17605, "Ġmond": 17606, "woo": 17607, "Ġorganizing": 17608, "ãģĹãĤĩãģĨ": 17609, "ĠSoon": 17610, "ĠвопÑĢоÑģ": 17611, "ĠNur": 17612, "ĠÐĹд": 17613, "Ġspider": 17614, "еÑģÑı": 17615, "Ġtutorials": 17616, "Ġnutrients": 17617, "orer": 17618, "Ġcoefficient": 17619, "Ġarrangement": 17620, "Ġpricing": 17621, "nan": 17622, "yu": 17623, "BL": 17624, "Ġtribe": 17625, "ĠHoward": 17626, "unks": 17627, "Ġnewer": 17628, "Ġprovin": 17629, "Ġprediction": 17630, "hos": 17631, "Ġolsun": 17632, "ĠAround": 17633, "Ġvier": 17634, "ĠÑģÑĤоÑĢон": 17635, "Ġvalley": 17636, "ĠEla": 17637, "ifi": 17638, "Ġgalaxy": 17639, "Ġtranqu": 17640, "Ġadvers": 17641, "ĠTemple": 17642, "iffs": 17643, "igence": 17644, "èĩªå·±": 17645, "Ġkönnte": 17646, "ĠÄijó": 17647, "Did": 17648, "Ġphotographs": 17649, "ĠAWS": 17650, "ÑĨиÑı": 17651, "Ġguards": 17652, "Ġappointed": 17653, "ĠGil": 17654, "Ġмом": 17655, "Ġcod": 17656, "ĠUnlike": 17657, "Ġevenly": 17658, "isconsin": 17659, "Ġestou": 17660, "Ġmnie": 17661, "ĠExec": 17662, "ĠMV": 17663, "ĠEine": 17664, "ä¿¡": 17665, "ĠRoger": 17666, "ĠFac": 17667, "ĠList": 17668, "Ġfuer": 17669, "аеÑĤе": 17670, "omed": 17671, "Ġattraction": 17672, "èī²": 17673, "Ġterrain": 17674, "ĠDrop": 17675, "Ġcorporations": 17676, "Ġsciences": 17677, "Ġthrone": 17678, "ãģĦãģŁ": 17679, "Ġaj": 17680, "ĠRot": 17681, "çī¹": 17682, "Ġsupporters": 17683, "ĠBere": 17684, "Here": 17685, "Ġdiferentes": 17686, "Ġsignificance": 17687, "Ïĥη": 17688, "æĪij覺å¾Ĺ": 17689, "Ġclamp": 17690, "ĠëĮĢë": 17691, "Ġfabulous": 17692, "rez": 17693, "æĮģ": 17694, "Ġassumptions": 17695, "uther": 17696, "wid": 17697, "pot": 17698, "è¿İ": 17699, "Ġyan": 17700, "ulin": 17701, "ÑĢÑĭв": 17702, "ĠSlow": 17703, "ĠPennsy": 17704, "Ġíķ´ìĦľ": 17705, "Ġmeio": 17706, "Ġwealthy": 17707, "ĠEight": 17708, "Ġpulse": 17709, "Ġfriction": 17710, "idity": 17711, "ĠHoll": 17712, "iyorum": 17713, "Ġsounded": 17714, "ĠCarr": 17715, "Ġfork": 17716, "âĺ": 17717, "ĠPA": 17718, "Ġconspir": 17719, "Ġcoding": 17720, "rt": 17721, "ĠTyp": 17722, "Ġìĸij": 17723, "Ġпог": 17724, "Ġmiser": 17725, "ĠÑģмоÑĤÑĢ": 17726, "ĠSweden": 17727, "Ġolarak": 17728, "ĠZhang": 17729, "ĠChi": 17730, "ĠTitan": 17731, "Ġscreening": 17732, "ĠSpider": 17733, "ĠÅŀimdi": 17734, "Ġobstacles": 17735, "lara": 17736, "Ġchallenged": 17737, "pse": 17738, "TON": 17739, "ụ": 17740, "ĠPi": 17741, "Ġlagi": 17742, "ieurs": 17743, "Ġhurting": 17744, "Ġneglect": 17745, "Ġgenerating": 17746, "Ġyoungest": 17747, "Ġaudit": 17748, "ĠÑĢез": 17749, "Ïģά": 17750, "Ġdonate": 17751, "ĠPDF": 17752, "Ġvisits": 17753, "Ġcruise": 17754, "PP": 17755, "aser": 17756, "Ġwsp": 17757, "backs": 17758, "ivals": 17759, "ãģĨãĤĵ": 17760, "Ġdeve": 17761, "Ġproport": 17762, "Ġcath": 17763, "ĠEffect": 17764, "Ġwinds": 17765, "ĠìĻĶ": 17766, "Ġcharts": 17767, "Ġsama": 17768, "Ġautomation": 17769, "Ġпока": 17770, "Ġolan": 17771, "Ġboats": 17772, "Ġcafe": 17773, "Ġdenied": 17774, "ĠMama": 17775, "Ġblocking": 17776, "ĠThor": 17777, "Ġphenomenal": 17778, "Ġstakeholders": 17779, "Ġunos": 17780, "ÑĥеÑĤ": 17781, "ĠAbraham": 17782, "ãģ§ãĤĤ": 17783, "Ġdetection": 17784, "Ġjuris": 17785, "Ġpowered": 17786, "zial": 17787, "Ġwelfare": 17788, "Ġupgrad": 17789, "Ġmożna": 17790, "ĠCase": 17791, "cular": 17792, "ĶìĿ´": 17793, "ãĥģ": 17794, "ĠGuess": 17795, "Ġcycles": 17796, "ä¾ĭ": 17797, "給": 17798, "rock": 17799, "umi": 17800, "Ġelite": 17801, "Ġquè": 17802, "åł±": 17803, "ÑĤом": 17804, "Ġshore": 17805, "gunta": 17806, "Ġku": 17807, "Ġfaithful": 17808, "ĠJeremy": 17809, "aid": 17810, "à·": 17811, "ugal": 17812, "å°įåķĬ": 17813, "ĠVel": 17814, "Ġvrai": 17815, "stell": 17816, "¨¸": 17817, "Ġkol": 17818, "è½": 17819, "Ġquanto": 17820, "ĠзаÑĢ": 17821, "Ġ2002": 17822, "esy": 17823, "Ġreserve": 17824, "ĠмоменÑĤ": 17825, "Ġdeployed": 17826, "Ġdefining": 17827, "Ġsau": 17828, "Ġgaat": 17829, "\")": 17830, "Ġtransmit": 17831, "Ġpublishing": 17832, "Ġranking": 17833, "Ġoffense": 17834, "Ġ46": 17835, "pin": 17836, "ĠTaking": 17837, "Ġentitled": 17838, "Ġgenuinely": 17839, "Ġvariations": 17840, "Ġfinde": 17841, "Ġtau": 17842, "Ġunfortunate": 17843, "ĠRah": 17844, "ports": 17845, "ĠcÅ": 17846, "Ġmonkey": 17847, "Ġbrac": 17848, "wei": 17849, "lung": 17850, "Ġartif": 17851, "Ġsyrup": 17852, "ĠÐĶав": 17853, "Ġlifted": 17854, "Ġchez": 17855, "ĠAdvent": 17856, "ĠStock": 17857, "Ġdol": 17858, "мен": 17859, "иÑĪÑĮ": 17860, "Ġyn": 17861, "gio": 17862, "det": 17863, "Ġdesse": 17864, "Ġgri": 17865, "ĠChairman": 17866, "çħ": 17867, "Ġcuenta": 17868, "anim": 17869, "Ġcrab": 17870, "Ġescal": 17871, "Ġpremière": 17872, "ĠGef": 17873, "Ġdining": 17874, "Ġseventh": 17875, "Ġchasing": 17876, "ĠTower": 17877, "Ġbrutal": 17878, "Ġfundamentally": 17879, "ãģ¨ãģĨ": 17880, "лениÑı": 17881, "stage": 17882, "Ġacquis": 17883, "Ġcylinder": 17884, "Ġcommander": 17885, "mem": 17886, "ĠUV": 17887, "happy": 17888, "Ġepsilon": 17889, "Ġinvitation": 17890, "Ġfarmer": 17891, "chair": 17892, "Ġdestiny": 17893, "Ġsovere": 17894, "ĠHebrew": 17895, "Ġservant": 17896, "Ġbew": 17897, "Ġgast": 17898, "uties": 17899, "Ġadministrative": 17900, "ĠCommand": 17901, "éta": 17902, "Ġnitrogen": 17903, "ê·¼": 17904, "Ġabi": 17905, "Ġvillain": 17906, "Ġblanket": 17907, "ĠSend": 17908, "Ġbeaten": 17909, "²Ħ": 17910, "Ġvolunt": 17911, "Ġscholar": 17912, "ĠEmperor": 17913, "Ġ43": 17914, "vable": 17915, "ĠDus": 17916, "ĠGU": 17917, "Ġtargeting": 17918, "www": 17919, "Ġamendment": 17920, 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17978, "ĠIls": 17979, "Ġunfold": 17980, "¶Ģë": 17981, "Ġprofits": 17982, "Ġwarming": 17983, "ĠGang": 17984, "Ġnetworking": 17985, "Ġmega": 17986, "Ġthoroughly": 17987, "lements": 17988, "ĠHm": 17989, "Ġdeciding": 17990, "Ġemotionally": 17991, "Ġexhausted": 17992, "ĠÐŁÐ¾ÑĤ": 17993, "cido": 17994, "ĠHTML": 17995, "Ġcopyright": 17996, "Ġmelody": 17997, "yim": 17998, "Ġanders": 17999, "oshop": 18000, "Ġë³¼": 18001, "Ġathlete": 18002, "ĠGE": 18003, "Ġfrequent": 18004, "Ġdesires": 18005, "Ġneeding": 18006, "ĠYun": 18007, "Ġrifle": 18008, "Ġlover": 18009, "'T": 18010, "Ġdense": 18011, "Ġtão": 18012, "Ġnotified": 18013, "Ġidi": 18014, "ìĹŃ": 18015, "íĨ": 18016, "Ġinteracting": 18017, "Ġrapport": 18018, "еÑĢи": 18019, "ski": 18020, "Ġbesser": 18021, "Ġmanufacturer": 18022, "ĠKyle": 18023, "Ġaccountable": 18024, "ĠSak": 18025, "ĠPil": 18026, "ĠDomin": 18027, "Ġpresum": 18028, "ĠÐĴÑģе": 18029, "Ġvinegar": 18030, "Ġguaranteed": 18031, "çľĭåĪ°": 18032, "Ġhandled": 18033, "éŁ³": 18034, "cat": 18035, 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21922, "Ġìĭ¬": 21923, "Ġging": 21924, "Ġumbrella": 21925, "IGHT": 21926, "Ġpension": 21927, "Ġcombining": 21928, "SS": 21929, "Ġrectangle": 21930, "á»ĩt": 21931, "Ġproxim": 21932, "ĠCow": 21933, "¸Į": 21934, "Ġintentional": 21935, "æķĻ": 21936, "Ġdecid": 21937, "ĠÑģкаж": 21938, "ĠUma": 21939, "iasm": 21940, "buz": 21941, "Ġdebris": 21942, "Ġcass": 21943, "ĠProp": 21944, "iska": 21945, "ëł¥": 21946, "esterol": 21947, "ussian": 21948, "ìĿ´ëŀij": 21949, "Ġunlimited": 21950, "Ġadmire": 21951, "Ġtightly": 21952, "Ġgenome": 21953, "ĠJunior": 21954, "venir": 21955, "gus": 21956, "ĠcÄĥ": 21957, "ĠVlad": 21958, "ĠíĤ": 21959, "Ġrelativ": 21960, "inci": 21961, "Ġaunque": 21962, "ĠBoys": 21963, "ÑĨион": 21964, "ĠSwiss": 21965, "Ġphysicians": 21966, "Ġíıī": 21967, "ĠPET": 21968, "Ġwounds": 21969, "about": 21970, "Ãłi": 21971, "onz": 21972, "urities": 21973, "ĠÑĥвид": 21974, "å·¦": 21975, "Ġmentality": 21976, "Ġvariance": 21977, "Ġsegunda": 21978, "Ġvolcano": 21979, "alie": 21980, "à¥ĩ": 21981, 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"Ġlava": 22097, "ĠgÃ¥": 22098, "試": 22099, "ת×Ļ": 22100, "ĠWayne": 22101, "Ġinstagram": 22102, "Ġrated": 22103, "paper": 22104, "Ġbild": 22105, "Ġpretending": 22106, "Ġobserving": 22107, "ĠÑģамом": 22108, "Ġtror": 22109, "Ġorganisms": 22110, "Ġfalta": 22111, "Ġhometown": 22112, "ç±": 22113, "Ġíĭ": 22114, "Ġcheg": 22115, "Ġì¡": 22116, "Ġcomma": 22117, "isé": 22118, "Ġlikelihood": 22119, "avored": 22120, "Ġgeldi": 22121, "ников": 22122, "Ġmedio": 22123, "Ġjakie": 22124, "ĠJup": 22125, "Ġgreenhouse": 22126, "Ġspit": 22127, "кое": 22128, "Ġкаж": 22129, "ĠGram": 22130, "ĠConference": 22131, "Ġdeficit": 22132, "sın": 22133, "inse": 22134, "uÄŁ": 22135, "Ġricht": 22136, "Ġcoincidence": 22137, "åıį": 22138, "Ġeurop": 22139, "Ġbutterfly": 22140, "pread": 22141, "Ġìĸ¼": 22142, "èĢ¶": 22143, "Ġwavel": 22144, "ĠInfin": 22145, "ĠPlanet": 22146, "Ġselfie": 22147, "ientras": 22148, "Ġarrog": 22149, "oser": 22150, "idal": 22151, "ł×Ĺ׳×ķ": 22152, "ütün": 22153, "Ġfreshman": 22154, "ĠMachine": 22155, "ÏĥÏĦ": 22156, "ĠDia": 22157, "ìĿ´ëĭ¤": 22158, "ãģĵãģĨ": 22159, "nea": 22160, "Ġlisting": 22161, "Ġconfigure": 22162, "utor": 22163, "Up": 22164, "tschaft": 22165, "rière": 22166, "Ġupwards": 22167, "ĠÑħоÑĩÑĥ": 22168, "Ġsweep": 22169, "Br": 22170, "Ġexpressing": 22171, "Ġunhappy": 22172, "Ġmandatory": 22173, "gender": 22174, "ĠAÃŃ": 22175, "Ġindicators": 22176, "Ġoils": 22177, "note": 22178, "Ġsegur": 22179, "ожеÑĤ": 22180, "ynasty": 22181, "Ġdistances": 22182, "Ġmerge": 22183, "BERT": 22184, "Ġsurrender": 22185, "Ġbuat": 22186, "ĠAwards": 22187, "Ġseñor": 22188, "odox": 22189, "Ġflavour": 22190, "Ġabdom": 22191, "Ġconfigur": 22192, "86": 22193, "ĠDIY": 22194, "Ġrigid": 22195, "°ĺ": 22196, "Ġcorporation": 22197, "Ġgroom": 22198, "jaw": 22199, "ĠNear": 22200, "ило": 22201, "Ġopera": 22202, "ĠInnov": 22203, "иÑĢа": 22204, "ĵ±": 22205, "Ġspecified": 22206, "Ġcosm": 22207, "ĠFreedom": 22208, "Ġclown": 22209, "ĠNem": 22210, "Ġвол": 22211, "Ñijн": 22212, "Ġcharger": 22213, "à¹ģล": 22214, 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"Ġanterior": 22272, "Ġstair": 22273, "кÑĢ": 22274, "Ġatomic": 22275, "Ġsympath": 22276, "Ġcontinually": 22277, "Ġkleine": 22278, "ète": 22279, "иÑī": 22280, "οÏĤ": 22281, "peut": 22282, "Ġreposit": 22283, "Ġentra": 22284, "Em": 22285, "Ġfinancing": 22286, "Ġмног": 22287, "Ġthesis": 22288, "ĠComputer": 22289, "eau": 22290, "ĠTree": 22291, "Ġbride": 22292, "onsieur": 22293, "shire": 22294, "wic": 22295, "DE": 22296, "ĠìĪĺë": 22297, "Ġacom": 22298, "ĠPO": 22299, "ersch": 22300, "ĠпомоÑī": 22301, "ĠArmen": 22302, "Ġ죽": 22303, "Ġzor": 22304, "Ġprints": 22305, "ĠDass": 22306, "港": 22307, "Ġdurable": 22308, "ĠTransport": 22309, "ìŀIJê°Ģ": 22310, "Ġлег": 22311, "Ġdét": 22312, "ôle": 22313, "amous": 22314, "YN": 22315, "Ġcliff": 22316, "Ġgrammar": 22317, "ĠÐŁÐ¾ÑįÑĤомÑĥ": 22318, "ĠlÃłm": 22319, "esch": 22320, "Ġmiserable": 22321, "Ġvolts": 22322, "ĠCad": 22323, "ukan": 22324, "ÑĤив": 22325, "rust": 22326, "Ġìĺ¬ëĿ¼": 22327, "Ġverk": 22328, "Ġchickens": 22329, "ĠYoo": 22330, "Ġoutfits": 22331, "code": 22332, "Ġhierarchy": 22333, "netes": 22334, "Ġcounterpart": 22335, "Ġtôi": 22336, "Ġted": 22337, "ĠBart": 22338, "ĠëĿ¼": 22339, "ĠGenau": 22340, "Ġincoming": 22341, "ĠABC": 22342, "rique": 22343, "ĠоÑĤп": 22344, "qual": 22345, "Ġincentive": 22346, "Ġihren": 22347, "׳×Ļ": 22348, "loe": 22349, "Ġ1930": 22350, "Ġbarg": 22351, "Ġdiction": 22352, "Ġönce": 22353, "INS": 22354, "Ġreh": 22355, "isiaj": 22356, "mouth": 22357, "Ġscoring": 22358, "lık": 22359, "ĠìķĦ주": 22360, "ORIA": 22361, "ĠEstados": 22362, "Ġcompanion": 22363, "Ġassemble": 22364, "Ġpunished": 22365, "Ġital": 22366, "Ġprevents": 22367, "istes": 22368, "ĠKentucky": 22369, "Ġlocate": 22370, "Ġfasting": 22371, "ãģ¨æĢĿ": 22372, "ĥĢ": 22373, "ĠSeb": 22374, "ĠCrown": 22375, "opia": 22376, "Ġwhip": 22377, "usz": 22378, "ками": 22379, "Ġdatabases": 22380, "åŃĹ": 22381, "Ġprosec": 22382, "Ġ1997": 22383, "ĠìĤ´ì§Ŀ": 22384, "ĠSolar": 22385, "ĠPues": 22386, "ĠZen": 22387, "ollo": 22388, "ĠGuru": 22389, "Ġsqueez": 22390, "ĠÐĹа": 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"ĠCrazy": 22509, "Ġneu": 22510, "Ġkull": 22511, "Ġtumor": 22512, "ĠAPP": 22513, "gate": 22514, "ĠARE": 22515, "98": 22516, "ĠSteam": 22517, "Ġfucked": 22518, "lage": 22519, "ĠâĻ¬": 22520, "ĠMD": 22521, "fy": 22522, "Ġshells": 22523, "ĠSeems": 22524, "izers": 22525, "Ġranges": 22526, "ĠAntonio": 22527, "ATION": 22528, "ĠBaba": 22529, "Ġìĥī": 22530, "kun": 22531, "Ġprayed": 22532, "ÑĢÑı": 22533, "ĠпÑĢоÑĤив": 22534, "Ġseas": 22535, "bury": 22536, "Ġ×Ķש": 22537, "Ġtrait": 22538, "ĠDepending": 22539, "Ġdre": 22540, "Ġkönnt": 22541, "ÑĨÑĥ": 22542, "Ġlipstick": 22543, "eez": 22544, "ĠпÑĢимеÑĢ": 22545, "Ġassignments": 22546, "Bob": 22547, "Ġmetals": 22548, "Ġspecially": 22549, "å°įä¸įå°į": 22550, "ĠìĺĪë": 22551, "ĠÅ¡": 22552, "Ġvista": 22553, "Ġά": 22554, "Ġtwins": 22555, "Ġnotable": 22556, "ĠSau": 22557, "Ġdévelop": 22558, "Ġçek": 22559, "Ġpolynom": 22560, "avam": 22561, "Ġtambé": 22562, "оном": 22563, "Ġplasma": 22564, "Ġefect": 22565, "Ġläng": 22566, "Ġcasi": 22567, "Ñģа": 22568, "ımı": 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23569, "ä¸ĩ": 23570, "ĠÑħод": 23571, "Ġquier": 23572, "Ġpressures": 23573, "ĠAnh": 23574, "å¹¾": 23575, "Ġelles": 23576, "ĠдÑĢÑĥз": 23577, "ĠможеÑĤе": 23578, "Ġchá»": 23579, "ĠMé": 23580, "ök": 23581, "ầu": 23582, "ìłĪ": 23583, "zin": 23584, "Ġcaution": 23585, "iban": 23586, "Ġjudging": 23587, "ÑĥÑİÑĤ": 23588, "Ġbaj": 23589, "ĠСейÑĩаÑģ": 23590, "ĠPoor": 23591, "ĠNazi": 23592, "Ġupbeat": 23593, "yang": 23594, "Ġweekends": 23595, "ĠEssentially": 23596, "Ġoluyor": 23597, "Ġspatial": 23598, "acker": 23599, "Ġseller": 23600, "Ġ×IJ×ķת": 23601, "ij׾": 23602, "Ġvivid": 23603, "ĠBond": 23604, "ê¶Į": 23605, "iskt": 23606, "ãĤµ": 23607, "Ġgoat": 23608, "driver": 23609, "Ġmug": 23610, "ictional": 23611, "Ġallt": 23612, "ĠIniti": 23613, "ĠRand": 23614, "Ġfinishes": 23615, "Ġê°Ī": 23616, "Ġvitam": 23617, "Ġteenagers": 23618, "ĠMorris": 23619, "ì¤Ħ": 23620, "ĠOri": 23621, "iya": 23622, "Ġmyös": 23623, "Step": 23624, "ĠKre": 23625, "辦": 23626, "Ġdinosaur": 23627, "Ġëªĩ": 23628, "affe": 23629, "ĠëIJ©ëĭĪëĭ¤": 23630, "Ġzeg": 23631, "åĪĩ": 23632, "ĠManhattan": 23633, "Ġsujet": 23634, "uelle": 23635, "stoff": 23636, "Ġdür": 23637, "Ġsubmar": 23638, "eses": 23639, "Ġaquele": 23640, "Ġnou": 23641, "ĠFaith": 23642, "tz": 23643, "ĠÑĤомÑĥ": 23644, "aceut": 23645, "liers": 23646, "Ġbandwidth": 23647, "Æ°á»Ŀ": 23648, "Ġrespective": 23649, "ĠAve": 23650, "Ġspreadshe": 23651, "ĠSent": 23652, "icamente": 23653, "Ġinfra": 23654, "Ġlearners": 23655, "Ġà®ī": 23656, "aiah": 23657, "renal": 23658, "Ġmustard": 23659, "Ġhabt": 23660, "çĥ": 23661, "ĠQué": 23662, "Ġanalyzing": 23663, "æ¯ı": 23664, "Ġsolic": 23665, "Ġ×Ķ×ķ×IJ": 23666, "Ġcausa": 23667, "Ġwelcomed": 23668, "ĠSuccess": 23669, "Ġfacile": 23670, "ĠÐŁÐ¾ÑĤомÑĥ": 23671, "schein": 23672, "Ġfetch": 23673, "Ġstrat": 23674, "ĠÑģÑĤоиÑĤ": 23675, "ìĹIJìĦľëĬĶ": 23676, "ĠÑģпоÑģоб": 23677, "mam": 23678, "ĠserÃŃa": 23679, "naments": 23680, "writer": 23681, "Ġconsulting": 23682, "íĺĢ": 23683, "ĠBerkeley": 23684, "eu": 23685, "asive": 23686, "UU": 23687, "ĠAnalyt": 23688, "Ġsubmission": 23689, "Ġmagnificent": 23690, "enza": 23691, "Ġecon": 23692, "Ġprofiles": 23693, "Ġincar": 23694, "Ab": 23695, "ĠNun": 23696, "Ġhic": 23697, "screaming": 23698, "Ġresilient": 23699, "åĪ©": 23700, "grund": 23701, "Ġconcur": 23702, "Ġbereits": 23703, "LD": 23704, "Ġnurt": 23705, "ìī": 23706, "Ġfeast": 23707, "Ġencuent": 23708, "ĠMichel": 23709, "Ġsuprem": 23710, "\"]": 23711, "Ġfeeds": 23712, "ĠKollegen": 23713, "isser": 23714, "ĠFeng": 23715, "ĠWen": 23716, "mun": 23717, "ĠtenÃŃa": 23718, "ĠWrest": 23719, "Ġìĺ¤ëĬĺìĿĢ": 23720, "Ġstead": 23721, "Ġrestoration": 23722, "Ġdonated": 23723, "Ġdels": 23724, "Ġcensus": 23725, "Ġdesperately": 23726, "worthy": 23727, "HE": 23728, "ĠSpa": 23729, "ĠBryan": 23730, "Ġhj": 23731, "ĠRaw": 23732, "ìķĦë": 23733, "ĠCamera": 23734, "Ġzien": 23735, "Ġstyl": 23736, "ĠTW": 23737, "ĠCheese": 23738, "borne": 23739, "Ġobl": 23740, "ĠAlready": 23741, "Ġunstable": 23742, "Ġflames": 23743, "post": 23744, "Ha": 23745, "romagn": 23746, "ĠìĹĦë§Ī": 23747, "dest": 23748, "Ġkolej": 23749, "Ġtemporarily": 23750, "Ġdetermining": 23751, "ĠGlass": 23752, "ÑĢон": 23753, "olan": 23754, "Ġdominated": 23755, "åĮĸ": 23756, "____": 23757, "ĠÙĩذا": 23758, "ĠDana": 23759, "Ġdinheiro": 23760, "aqu": 23761, "민": 23762, "ĠÃłs": 23763, "ĠJoey": 23764, "ĠGriff": 23765, "Ġattain": 23766, "Ġtransitions": 23767, "ĠLiterally": 23768, "енд": 23769, "ĠHaven": 23770, "Ġgrabbing": 23771, "Ġcrystals": 23772, "ĠFourth": 23773, "Ġcandles": 23774, "ĠÑģлÑĥÑĩа": 23775, "rico": 23776, "Ġ5000": 23777, "etto": 23778, "Ġundo": 23779, "Ġkto": 23780, "Ġdivert": 23781, "Ġchir": 23782, "Ġpersec": 23783, "Ġhiking": 23784, "Ġannouncements": 23785, "çĶ±": 23786, "зÑĭ": 23787, "Ġauc": 23788, "Ġsystemic": 23789, "ĠRM": 23790, "Ïĥα": 23791, "ĠÐĶж": 23792, "Ġyar": 23793, "ĠWard": 23794, "Ġpissed": 23795, "Ġcarn": 23796, "Ġautonomous": 23797, "ãħİãħİ": 23798, "sover": 23799, "æ²ĴéĮ¯": 23800, "å¾Ī好": 23801, "Ġreflex": 23802, "Ġgardens": 23803, "Ġdated": 23804, "ì±": 23805, "amiÄĻ": 23806, "Ġcontinuity": 23807, "Ġcitizenship": 23808, "Ġschwer": 23809, "Ġzak": 23810, "table": 23811, "ĠÑģÑĩ": 23812, "è§ģ": 23813, "ĠÏĥε": 23814, "Ġgenerates": 23815, "구ëĤĺ": 23816, "öh": 23817, "óm": 23818, "alam": 23819, "ĠJUDY": 23820, "ĠBug": 23821, "Ġãģ¦": 23822, "Ġdrones": 23823, "Ġágua": 23824, "acaks": 23825, "æļ": 23826, "ĠÐļон": 23827, "×ĸ×Ķ": 23828, "Ġstrive": 23829, "ĠAltern": 23830, "Ġnearest": 23831, "Ġproyect": 23832, "tera": 23833, "ĠASHLEY": 23834, "Ġworm": 23835, "Ġreplay": 23836, "Ġtara": 23837, "ĠIndians": 23838, "ãĤ°": 23839, "icaid": 23840, "ĠìĪľ": 23841, "Ġappealing": 23842, "ĠWes": 23843, "Ġmentions": 23844, "Ġделе": 23845, "Ġkw": 23846, "Ġfragile": 23847, "isz": 23848, "ków": 23849, "hang": 23850, "color": 23851, "Ġpresidente": 23852, "87": 23853, "еÑĦ": 23854, "çĪ¸": 23855, "Ġдобав": 23856, "ĠNelson": 23857, "áfic": 23858, "ĠMICHAEL": 23859, "Ġmechanic": 23860, "Ġmetres": 23861, "ĠoczywiÅĽcie": 23862, "ĠCind": 23863, "ĠogsÃ¥": 23864, "Ġlandsca": 23865, "ACE": 23866, "Ġheadlines": 23867, "Ġcatalyst": 23868, "ĠCatch": 23869, "inkles": 23870, "Ġpills": 23871, "ordo": 23872, "Ġimmigrant": 23873, "Ġexamination": 23874, "Ġaccidents": 23875, "zÄħd": 23876, "Ġquiere": 23877, "Ġnella": 23878, "Ġ67": 23879, "Ġpassa": 23880, "Ġsuperfic": 23881, "istor": 23882, "Ġnov": 23883, "ëĭµ": 23884, "Ġmandate": 23885, "isons": 23886, "ĠVirtual": 23887, "Ġselber": 23888, "Ġcounseling": 23889, "ĠNBA": 23890, "Ġsept": 23891, "Ġbeliever": 23892, "Ġmarvel": 23893, "ĠIntegr": 23894, "ĠмÑĸ": 23895, "Ġorph": 23896, "Ġbackward": 23897, "ĠGeneration": 23898, "ĠPict": 23899, "ĠÑĤоÑĤ": 23900, "Ġtapi": 23901, "prochen": 23902, "Ġhallway": 23903, "hte": 23904, "ĠÛģÛĴ": 23905, "ĠZum": 23906, "èĢģ師": 23907, "achment": 23908, "iquer": 23909, "folg": 23910, "ĠEddie": 23911, "ĠKil": 23912, "Ġwellness": 23913, "stock": 23914, "è¼ĥ": 23915, "Ġkaç": 23916, "Ġterrorism": 23917, "Ġpointer": 23918, "Of": 23919, "heric": 23920, "ĠUltimately": 23921, "Ġmeses": 23922, "ĠTrade": 23923, "Ġpint": 23924, "Ġtuition": 23925, "Ġdisagre": 23926, "Ġê²ĮìŀĦ": 23927, "Ġmanuscript": 23928, "Ġroomm": 23929, "Ġoutputs": 23930, "еÑĨи": 23931, "Ġries": 23932, "Ġsalud": 23933, "otzdem": 23934, "Ġmasses": 23935, "ĠbyÅĤa": 23936, "Ġclearing": 23937, "Ġdiscourse": 23938, "atson": 23939, "Ġfolded": 23940, "ĠJar": 23941, "ÙĦÙī": 23942, "900": 23943, "ĠÑĥÑģп": 23944, "Ġprophecy": 23945, "Ġinterfere": 23946, "иÑħод": 23947, "à¹Į": 23948, "Ġthri": 23949, "Ġ×ŀש": 23950, "Ġlazım": 23951, "Ġ1992": 23952, "Ġfuturo": 23953, "Ġlocking": 23954, "Ġembargo": 23955, "ĠNeither": 23956, "ivamente": 23957, "ĠmÃ¥ste": 23958, "Ġmik": 23959, "Ġcollector": 23960, "екоÑĤоÑĢ": 23961, "ĠGand": 23962, "Ġsentir": 23963, "ĠMight": 23964, "å¡Ķ": 23965, "Ġganzen": 23966, "UC": 23967, "Ġrelating": 23968, "SD": 23969, "Ġmosquito": 23970, "GR": 23971, "Ġhollow": 23972, "âĺħ": 23973, "ĠWalker": 23974, "Ġaffiliate": 23975, "Ġduplicate": 23976, "нем": 23977, "Ġgrape": 23978, "ĠOrganization": 23979, "Ġsynt": 23980, "Joe": 23981, "Ġgeg": 23982, "Ġrevealing": 23983, "ĠEthan": 23984, "outer": 23985, "Ġyay": 23986, "é«Ķ": 23987, "лаÑĢ": 23988, "Ġreportedly": 23989, "Ġihrer": 23990, "Ġrecognise": 23991, "Ġbumper": 23992, "ĠRandy": 23993, "ĠVenus": 23994, "tles": 23995, "Ġappetite": 23996, "Ġglucose": 23997, "Ġchodzi": 23998, "ĠFurthermore": 23999, "tir": 24000, "Ġconta": 24001, "Ġintuition": 24002, "Ġaltitude": 24003, "Ġchunks": 24004, "ĠJoshua": 24005, "ıģım": 24006, "rylic": 24007, "leans": 24008, "ĠíĶ¼ë": 24009, "LL": 24010, "Que": 24011, "Ġgor": 24012, "ĠзнаÑĩиÑĤ": 24013, "Ġpoems": 24014, "Ġexcel": 24015, "Ġexplored": 24016, "Ġpopul": 24017, "Ġincluso": 24018, "stä": 24019, "ĠGavin": 24020, "alling": 24021, "ĠÏĦον": 24022, "é©": 24023, "arbeit": 24024, "ĠGas": 24025, "Ġglorious": 24026, "rieben": 24027, "Ġspam": 24028, "Ġindoor": 24029, "Ġthrust": 24030, "ĠAld": 24031, "ĠPrior": 24032, "Ġonboard": 24033, "ãģłãģķãģĦ": 24034, "oca": 24035, "ASH": 24036, "£ł": 24037, "ĠChristine": 24038, "Ġdrawer": 24039, "Ġnoon": 24040, "Ġìŀĺë": 24041, "Ġpermanently": 24042, "æ·±": 24043, "ĠнапÑĢимеÑĢ": 24044, "Ġpodcasts": 24045, "erapeut": 24046, "prit": 24047, "Ġstainless": 24048, "ĠÚ©ÛĴ": 24049, "Ġfamilia": 24050, "ĠÑĢазÑĢ": 24051, "unto": 24052, "ĠÑģÑĤол": 24053, "Ġhä": 24054, "ĠHai": 24055, "ĠPB": 24056, "izon": 24057, "Ġkonnte": 24058, "Ġbüyük": 24059, "Ġutilizar": 24060, "ÚĨ": 24061, "Ġaquesta": 24062, "Ġmixer": 24063, "udent": 24064, "лекÑģ": 24065, "ÅĤu": 24066, "ĠÑģиÑģÑĤем": 24067, "ĠноÑĢм": 24068, "Ġfatal": 24069, "Ġconsiderations": 24070, "Ġvalidation": 24071, "Ġoli": 24072, "ĠkardeÅŁ": 24073, "ĠGLORIA": 24074, "Ġpall": 24075, "еÑģÑĤе": 24076, "Ġrectang": 24077, "Ġmedieval": 24078, "allahi": 24079, "asti": 24080, "ĠSyrian": 24081, "Ġshear": 24082, "Ġdebug": 24083, "ĠMai": 24084, "Ġknocking": 24085, "ĠLex": 24086, "ardan": 24087, "rov": 24088, "Ġmemorial": 24089, "æ°£": 24090, "ooky": 24091, "Ġstuffed": 24092, "Ġpassé": 24093, "Ġwig": 24094, "Ĥł": 24095, "Ġpróxima": 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26487, "Ġfilme": 26488, "ĠBereich": 26489, "ĠSaid": 26490, "Ġimposed": 26491, "Ġdiary": 26492, "ĠгоÑĢ": 26493, "ĠGates": 26494, "Ġalta": 26495, "å¸Į": 26496, "Ġchcia": 26497, "pleasant": 26498, "Ġë°Ŀ": 26499, "Ġmożemy": 26500, "ĠAustria": 26501, "Ġbroker": 26502, "Ġsucked": 26503, "èĢĥ": 26504, "Ġcompartment": 26505, "Ġclone": 26506, "Ġ×Ķ×¢": 26507, "ĠDanke": 26508, "Ġnochmal": 26509, "езд": 26510, "Ġadrenal": 26511, "Ġkleinen": 26512, "ãģ¾ãģĹãĤĩãģĨ": 26513, "Ġsubsequently": 26514, "Ġdecentral": 26515, "Ġgenetics": 26516, "Ġê´ij": 26517, "Ġmonitors": 26518, "ĠApplic": 26519, "ĠReporter": 26520, "wert": 26521, "Ġwiem": 26522, "ĠMovement": 26523, "Ġinterviewing": 26524, "Ġhairs": 26525, "Ġpuò": 26526, "ĠChelsea": 26527, "Ġcoher": 26528, "Ġcot": 26529, "Ġzas": 26530, "Ġpatches": 26531, "Ġlah": 26532, "Ñĥнк": 26533, "ĠReagan": 26534, "ĠMarco": 26535, "city": 26536, "Ġdefender": 26537, "Ġdecoration": 26538, "iji": 26539, "Ġlitter": 26540, "Ш": 26541, "Ġjego": 26542, "REW": 26543, "ĠPik": 26544, "ĠHee": 26545, "ĠIv": 26546, "Ġиде": 26547, "ĠTheater": 26548, "ĠÑĩаÑģÑĤо": 26549, "Ġsweater": 26550, "Ġhighlighting": 26551, "Ġainsi": 26552, "Ġdiplomatic": 26553, "ĠNevertheless": 26554, "å³": 26555, "ASON": 26556, "Ġpúblico": 26557, "Ġferm": 26558, "reated": 26559, "cod": 26560, "Ġ물ë": 26561, "Ġmister": 26562, "ĠVancouver": 26563, "Ġrecognizes": 26564, "ecd": 26565, "Ġcomplications": 26566, "encial": 26567, "ãģĹãģı": 26568, "Ġê°Ģì§Ģ": 26569, "ĠUltimate": 26570, "Ġvaig": 26571, "ĠMerry": 26572, "×ķ×Ĵ": 26573, "ĠMarcus": 26574, "總": 26575, "owego": 26576, "Ġmente": 26577, "Sm": 26578, "Ġaja": 26579, "ĠTao": 26580, "Ġjudicial": 26581, "Ġentrepreneurship": 26582, "Ġнемного": 26583, "Ġpis": 26584, "Ġerg": 26585, "Ġchrist": 26586, "ĠCurt": 26587, "ĠÑĢаÑģп": 26588, "λε": 26589, "ensch": 26590, "ÃŃre": 26591, "Ġfocal": 26592, "ĠDiamond": 26593, "avÃŃa": 26594, "Ġhanno": 26595, "ĠSquad": 26596, "Ġassociations": 26597, "ĠCreative": 26598, "Ġmessenger": 26599, "Ġbegging": 26600, "Ġdecimal": 26601, "ĠdÄ±ÅŁ": 26602, "Ġmetadata": 26603, "sels": 26604, "ĠÄ°ÅŁ": 26605, "ữa": 26606, "Ġdifficile": 26607, "dı": 26608, "Ġslaughter": 26609, "ĠVerg": 26610, "Ġ×Ĵ×Ŀ": 26611, "ç°¡": 26612, "æĮī": 26613, "ĠTea": 26614, "asses": 26615, "Ok": 26616, "Ġsynthes": 26617, "otiation": 26618, "Ġpainter": 26619, "Ġelbows": 26620, "Ġarchitectural": 26621, "ĠÑĢад": 26622, "Ġglor": 26623, "image": 26624, "ampa": 26625, "culiar": 26626, "ł¨": 26627, "Ġteve": 26628, "ĠStelle": 26629, "ĠBam": 26630, "Ġì´Ī": 26631, "asis": 26632, "ipedia": 26633, "ĠGI": 26634, "ĠActive": 26635, "çĦ¶åIJİ": 26636, "azi": 26637, "ãĤĮãģ¦": 26638, "ĠLucky": 26639, "íķ©": 26640, "ĠпÑĢиÑħод": 26641, "Ġrunway": 26642, "Ġauthentication": 26643, "Ġposible": 26644, "Ġsupplements": 26645, "Ġsurgical": 26646, "Gen": 26647, "Ġfeasible": 26648, "DO": 26649, "Ġoutlook": 26650, "Ġintervals": 26651, "Ġanecd": 26652, "Ãłng": 26653, "Ġstraps": 26654, "ĠShu": 26655, "udd": 26656, "issenschaft": 26657, "Ġporte": 26658, "Ġcommitting": 26659, "Ġalley": 26660, "Ġcovenant": 26661, "ĠPedro": 26662, "lessness": 26663, "ĠSolid": 26664, "ĠMolly": 26665, "ĠнекоÑĤоÑĢ": 26666, "Ġcooperate": 26667, "åĮĹ": 26668, "ollen": 26669, "Ġtuna": 26670, "Ġkindergarten": 26671, "ĠSiz": 26672, "Ġdużo": 26673, "ĠMBA": 26674, "ĠGEORGE": 26675, "ĠFisher": 26676, "å¿ĺ": 26677, "ĠCaesar": 26678, "ĠкÑĢаÑģив": 26679, "ĠDelhi": 26680, "zym": 26681, "Ġexplicar": 26682, "ê°Ģì§Ģ": 26683, "uns": 26684, "grow": 26685, "ĠпÑĢиÑģ": 26686, "Ġ86": 26687, "Ġstating": 26688, "Ġmassa": 26689, "chter": 26690, "Ġì»¬ëŁ¬": 26691, "Ġdeputy": 26692, "SM": 26693, "noc": 26694, "Ġgeography": 26695, "ĠEnterprise": 26696, "ĠCant": 26697, "öz": 26698, "Ġunpack": 26699, "ĠíĻĶë": 26700, "Ġsearches": 26701, "Ġpresidency": 26702, "Ġtrivial": 26703, "Ġpige": 26704, "oubt": 26705, "ãĤļ": 26706, "ì¼ĢìĿ´": 26707, "Ġbudgets": 26708, "Ġub": 26709, "Ġpne": 26710, "ĠYale": 26711, "ĠÅŁÃ¶yle": 26712, "regular": 26713, "Ġimperfect": 26714, "ARA": 26715, "ĠfamÃŃlia": 26716, "urm": 26717, "ĠAdventure": 26718, "ãĥĬ": 26719, "cis": 26720, "emark": 26721, "Ġnego": 26722, "Ġinappropriate": 26723, "ĠпÑĢиз": 26724, "ĠÑĢол": 26725, "Ġdreamed": 26726, "Bry": 26727, "Ġshuttle": 26728, "Ġpillars": 26729, "Ġbik": 26730, "inum": 26731, "ĠÑĥÑģ": 26732, "ĠNebr": 26733, "Ġperpendicular": 26734, "Ġbooked": 26735, "bery": 26736, "Ġvikt": 26737, "bear": 26738, "esus": 26739, "Ġвозможно": 26740, "¨¹": 26741, "Ġpresumably": 26742, "ĠMemphis": 26743, "Ġambulance": 26744, "×ķ×ŀר": 26745, "Ġthumbnail": 26746, "Ġmodification": 26747, "éĩı": 26748, "Ġinterpreted": 26749, "Ġpromo": 26750, "Ġκά": 26751, "ĠεÏĢ": 26752, "Ġacoustic": 26753, "ĠDB": 26754, "åĵİ": 26755, "Ġnonetheless": 26756, "oule": 26757, "Ġpequ": 26758, "Ġknob": 26759, "ãĤ£": 26760, "ĠëıĮìķĦ": 26761, "Ġpurchases": 26762, "ĠÃĩünkü": 26763, "Ġdividing": 26764, "perform": 26765, "raction": 26766, "healthy": 26767, "ĠTitle": 26768, "Ġuk": 26769, "Ġcerca": 26770, "Ġarguably": 26771, "Ġfale": 26772, "ë³µ": 26773, "Ġgamers": 26774, "Ġutilizing": 26775, "Ġoffended": 26776, "Ġtava": 26777, "alı": 26778, "Ġmedian": 26779, "Ġinfectious": 26780, "ĠAnnie": 26781, "Ġsmartphones": 26782, "Ġparole": 26783, "åĸĿ": 26784, "ĠEpic": 26785, "zza": 26786, "Ġunified": 26787, "Ġê·¸ëķĮ": 26788, "Ġcurtain": 26789, "ĠÄĥ": 26790, "Ġsexually": 26791, "Ġunserem": 26792, "ĠConvention": 26793, "Ġallegedly": 26794, "Ya": 26795, "ĠHoo": 26796, "enment": 26797, "æĢª": 26798, "íĽĦ": 26799, "Ġgigantic": 26800, "Ġnoting": 26801, "Ġrebo": 26802, "ĠJama": 26803, "ĠAlz": 26804, "Ġborrowed": 26805, "침": 26806, "Ġperipher": 26807, "оÑĤа": 26808, "ĠGB": 26809, "ĠGear": 26810, "Ġeconomically": 26811, "Ġtelefon": 26812, "Ġqueremos": 26813, "ĠдалÑĮÑĪе": 26814, "Ġras": 26815, "ĠTeach": 26816, "icios": 26817, "atos": 26818, "Ġpledge": 26819, "bau": 26820, "ĠHimself": 26821, "Link": 26822, "Ġespero": 26823, "Ġchromos": 26824, "ĠPER": 26825, "Ġerle": 26826, "Ġpodium": 26827, "ços": 26828, "Ġnieu": 26829, "Ġfen": 26830, "ĠGOD": 26831, "ĠChocolate": 26832, "werk": 26833, "Ġtừ": 26834, "Ġsuppress": 26835, "λη": 26836, "Ġ240": 26837, "Ġsitä": 26838, "Ġhonesty": 26839, "ĠBio": 26840, "ĠBard": 26841, "ĠобÑīем": 26842, "ĠмÑĥз": 26843, "Ġmarble": 26844, "ĠÑĨенÑĤ": 26845, "Ġprocure": 26846, "Ġrotor": 26847, "bern": 26848, "Ġtuh": 26849, "Ġheadset": 26850, "atem": 26851, "Ġwarranty": 26852, "à®´": 26853, "Ġfiling": 26854, "ιά": 26855, "Ġcomprendre": 26856, "Ġimpulse": 26857, "Ġsalv": 26858, "written": 26859, "Ġinstitute": 26860, "Kim": 26861, "ĠLGBTQ": 26862, "ficiente": 26863, "His": 26864, "ĠαÏħÏĦÏĮ": 26865, "Ġteenage": 26866, "orus": 26867, "ĠÑĢазб": 26868, "See": 26869, "ĠConserv": 26870, "á»ģn": 26871, "fulness": 26872, "Ġstrawberries": 26873, "ĠAbu": 26874, "ион": 26875, "Ġolla": 26876, "NOISE": 26877, "ĠEmploy": 26878, "Ġwiped": 26879, "urger": 26880, "Ġmodifications": 26881, "Ġíķĺì§Ģ": 26882, "Ġfootsteps": 26883, "Ġhonors": 26884, "Ġadul": 26885, "Ġflipping": 26886, "ĠHU": 26887, "ZY": 26888, "Ġintegrating": 26889, "بر": 26890, "ulla": 26891, "Ġnatuurlijk": 26892, "ĠíĹĪ": 26893, "ĠEthereum": 26894, "ÙĬÙĦ": 26895, "wed": 26896, "Ġpeaks": 26897, "ĠKes": 26898, "Ġbloom": 26899, "Ġcrashing": 26900, "Ġ911": 26901, "ĠоÑĤлиÑĩ": 26902, "Ġcontrollers": 26903, "ĠDod": 26904, "ĠвмеÑģÑĤе": 26905, "Ġsortir": 26906, "å¥ĩ": 26907, "ĠStraight": 26908, "ĠGracias": 26909, "Ġgroove": 26910, "Ġtogg": 26911, "Ġìĭ¶ìĿĢ": 26912, "éro": 26913, "Ġoutward": 26914, "ĠWA": 26915, "ĠRocky": 26916, "Ġscam": 26917, "Ġhayat": 26918, "ignty": 26919, "âĦ": 26920, "plings": 26921, "Ġantibiotics": 26922, "Ġä¸Ģ": 26923, "Ġnevertheless": 26924, "jang": 26925, "commerce": 26926, "Ġspoiler": 26927, "Ġglove": 26928, "Ġchatter": 26929, "ĠBY": 26930, "~?": 26931, "Ġíĺ¸": 26932, "Ġdemol": 26933, "wechsel": 26934, "imir": 26935, "Ġraid": 26936, "еÑĢÑħ": 26937, "ìŀIJ기": 26938, "enf": 26939, "Ġcommented": 26940, "Ġoptimized": 26941, "Ġconvicted": 26942, "Ġbats": 26943, "ĠSB": 26944, "ĠAur": 26945, "ĠTong": 26946, "Ġimplicit": 26947, "ĠJanet": 26948, "Ġreag": 26949, "ãģ²": 26950, "ĠAdvanced": 26951, "Ġimpose": 26952, "ש×Ķ": 26953, "Ġschemes": 26954, "ougher": 26955, "abolic": 26956, "Ġê±°ì£ł": 26957, "Ġslowing": 26958, "Ġwtedy": 26959, "Ġdestructive": 26960, "ĠопÑĢед": 26961, "Ġlandmark": 26962, "ĠëıĪ": 26963, "ĠWalking": 26964, "ẹ": 26965, "Ġtijd": 26966, "ĠKN": 26967, "ĠQuant": 26968, "ìĺ¤ë": 26969, "ĠкÑĢÑĥ": 26970, "Ġperder": 26971, "Ġnove": 26972, "ände": 26973, "ĠãģĹ": 26974, "bia": 26975, "Ġcustody": 26976, "Ġbiod": 26977, "æĿ±è¥¿": 26978, "Ġdirecting": 26979, "...âĢĭ": 26980, "Ġreloc": 26981, "Ġdemande": 26982, "ãĤĵãģł": 26983, "ĠoÄŁlum": 26984, "Ġодна": 26985, "ĠMilk": 26986, "åı·": 26987, "ĠKra": 26988, "ĠHonda": 26989, "Ġpue": 26990, "Ġelekt": 26991, "Ġbeginners": 26992, "Ġspear": 26993, "ÃŃnh": 26994, "ĠLuft": 26995, "Ġnig": 26996, "ĠSchools": 26997, "Ġforums": 26998, "ĠQin": 26999, "ppo": 27000, "Ġzag": 27001, "ĠЮ": 27002, "Ġtoothp": 27003, "ĠStyle": 27004, "ì´Ī": 27005, "Ġpunct": 27006, "Ġreps": 27007, "ĠAly": 27008, "Ġamendments": 27009, "Ġöz": 27010, "Ġdigits": 27011, "urai": 27012, "Ġchaotic": 27013, "ĠMasters": 27014, "eon": 27015, "ĠCash": 27016, "ĠCuz": 27017, "Ġbedeutet": 27018, "Ġscanning": 27019, "Ġжд": 27020, "неÑĤ": 27021, "Ġcertainty": 27022, "jek": 27023, "Ġdijo": 27024, "ĠClimate": 27025, "Ġrinse": 27026, "Ġkrij": 27027, "veland": 27028, "Ġsoundtrack": 27029, "ĠSafe": 27030, "ĠNova": 27031, "94": 27032, "Ġathe": 27033, "ĠVerb": 27034, "oler": 27035, "ìĿ´ì£ł": 27036, "Ġvin": 27037, "Ġrespiratory": 27038, "ĠStudy": 27039, "ĠCAM": 27040, "Ġavocado": 27041, "ĠZhen": 27042, "Ġlatency": 27043, "Ġfeathers": 27044, "Ġcontar": 27045, "ĠвеÑī": 27046, "Ġfark": 27047, "Ġblended": 27048, "Ġexploded": 27049, "ĠXX": 27050, "ĠBenim": 27051, "Ġalguém": 27052, "istoire": 27053, "Ġconfidential": 27054, "Ġmast": 27055, "Ġì¿": 27056, "geh": 27057, "Ġdisrespect": 27058, "ĠSystems": 27059, "Æ°a": 27060, "Ed": 27061, "Ġwys": 27062, "Ġexotic": 27063, "Ġglowing": 27064, "ùng": 27065, "ounge": 27066, "èĦ": 27067, "аниз": 27068, "Ġpalav": 27069, "ĠSword": 27070, "Ġgim": 27071, "ĠCrow": 27072, "Ġpotent": 27073, "bish": 27074, "Ġabused": 27075, "ĠJed": 27076, "Ġgambling": 27077, "ĠSpect": 27078, "Ġinvestigators": 27079, "æĻļ": 27080, "Ġratt": 27081, "Ġdob": 27082, "ĠDES": 27083, "hog": 27084, "ĠоÑĤкÑĢÑĭ": 27085, "íĮħ": 27086, "ĠденÑĮги": 27087, "Ġíĺ¹": 27088, "Ġ머리": 27089, "Ġsaturation": 27090, "Ġinherited": 27091, "ĠInnovation": 27092, "ìĹĪëįĺ": 27093, "Ġtangible": 27094, "Ġdepri": 27095, "hed": 27096, "Ġпомог": 27097, "Ġsliced": 27098, "à¥į": 27099, "Ġthế": 27100, "Å¥": 27101, "68": 27102, "Ġcorona": 27103, "Ġgifted": 27104, "Ġsoir": 27105, "Ġhumility": 27106, "ĠìĿ´ê±¸": 27107, "Ġflaws": 27108, "ĠпÑĢакÑĤи": 27109, "Ġkald": 27110, "waż": 27111, "yw": 27112, "ãĤĵãģ§ãģĻ": 27113, "irteen": 27114, "Ġcrochets": 27115, "¦¬ê°Ģ": 27116, "ĠìłĦìĹIJ": 27117, "Ġdese": 27118, "æ¥Ń": 27119, "Ġмаг": 27120, "ĠdziaÅĤ": 27121, "Ġlég": 27122, "changing": 27123, "Ġllev": 27124, "ÅĦsk": 27125, "çĶ»": 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28159, "ĠDylan": 28160, "ESE": 28161, "Ġvertex": 28162, "tı": 28163, "ĠFir": 28164, "Ġzaw": 28165, "ĠGed": 28166, "ĠÐĿап": 28167, "dz": 28168, "Ġcursor": 28169, "Ġswipe": 28170, "Ġinevitably": 28171, "Ġposters": 28172, "Ġinclined": 28173, "Ġgreeting": 28174, "Ġdisappointment": 28175, "ãģ¾ãģ§": 28176, "Ġrelação": 28177, "TT": 28178, "Ġrabb": 28179, "ĠMaine": 28180, "Ġanalyzed": 28181, "FE": 28182, "ĠÐŁÐ¾Ð»": 28183, "ĠSandra": 28184, "Ġplague": 28185, "ARE": 28186, "Ġvär": 28187, "ĠViv": 28188, "umed": 28189, "hando": 28190, "houette": 28191, "ĠBailey": 28192, "ä¸įéģİ": 28193, "yson": 28194, "Ġsemua": 28195, "Ġhardcore": 28196, "âĤ¬": 28197, "Ñĸм": 28198, "éra": 28199, "OTH": 28200, "Ġforeigners": 28201, "ĠPalestinian": 28202, "Ġproprio": 28203, "аний": 28204, "Ġmyths": 28205, "WH": 28206, "Ġninth": 28207, "ĠCreator": 28208, "лом": 28209, "ĠFlip": 28210, "Ġeman": 28211, "ĠkiÅŁ": 28212, "zieh": 28213, "ĠEarnest": 28214, "system": 28215, "ĸìĹIJ": 28216, "Ġarmies": 28217, "ĠOutside": 28218, 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"ĠMol": 28278, "Ġ`": 28279, "Ġchant": 28280, "Ġthereby": 28281, "Ġpenis": 28282, "Ġemoc": 28283, "wyn": 28284, "Ñĥже": 28285, "Ġtread": 28286, "óle": 28287, "Ġdeepest": 28288, "Ġmache": 28289, "ĠVent": 28290, "ĠAmsterdam": 28291, "ãĥĽ": 28292, "Ġrebel": 28293, "Ġ61": 28294, "ĠвкÑĥÑģ": 28295, "uffs": 28296, "ĠdoÄŁru": 28297, "ĠNapole": 28298, "ήÏĥ": 28299, "Ġworkouts": 28300, "ĠGlad": 28301, "неÑģ": 28302, "Ġtensions": 28303, "ĠShift": 28304, "ĠGuer": 28305, "íĮIJ": 28306, "Ġì¹ľêµ¬": 28307, "Ðĸ": 28308, "Ġimplant": 28309, "êu": 28310, "ê¸Ģ": 28311, "Ġauthorized": 28312, "CER": 28313, "ĠRV": 28314, "Ġhil": 28315, "lev": 28316, "cimento": 28317, "ĠUFO": 28318, "ìĥĪ": 28319, "è¨Ĥ": 28320, "wor": 28321, "Ġdances": 28322, "ĠPixel": 28323, "çľĭä¸Ģä¸ĭ": 28324, "Ġtrotzdem": 28325, "Ġobten": 28326, "ĠAlfred": 28327, "Ġcostly": 28328, "ĠStanley": 28329, "Ġterrorists": 28330, "ĠWid": 28331, "ħëĭĪëĭ¤": 28332, "Ġleicht": 28333, "ìĿ´ìĬ¤": 28334, "Ġdobrze": 28335, "Ġhesit": 28336, "Ġerzäh": 28337, 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28394, "Ġdeine": 28395, "Ġkarma": 28396, "Ġumm": 28397, "Ġentsche": 28398, "ĠHolocaust": 28399, "Ġdiscoveries": 28400, "aments": 28401, "Ġraison": 28402, "Ġburgers": 28403, "Back": 28404, "Ġgdy": 28405, "ĠAG": 28406, "ĠDaw": 28407, "ìķł": 28408, "headed": 28409, "ĠClar": 28410, "Inst": 28411, "ĠLieutenant": 28412, "ĠAfD": 28413, "ĠCes": 28414, "Ġpersonalized": 28415, "Ġinterfaces": 28416, "à¸Īะ": 28417, "ĠÑĢеж": 28418, "Ġsuic": 28419, "Ġstarving": 28420, "Ġoxide": 28421, "Ġdecorated": 28422, "ĠDU": 28423, "ĠìĺĪìģĺ": 28424, "Ġquo": 28425, "Ġdistortion": 28426, "段": 28427, "Ġ먹ìĸ´ë": 28428, "Ġstakes": 28429, "æĺİçĻ½": 28430, "Ġsyntax": 28431, "Ġbiết": 28432, "thy": 28433, "icie": 28434, "Ġbrasile": 28435, "isis": 28436, "RC": 28437, "Ġshook": 28438, "Ġdepths": 28439, "ĠCosta": 28440, "Ġvocals": 28441, "Ġcoaster": 28442, "Ġfalou": 28443, "ettle": 28444, "Ġkennen": 28445, "Ġderive": 28446, "Ġaids": 28447, "ĠÐĿик": 28448, "Ġentwic": 28449, "Ġvertically": 28450, "ĠÍ": 28451, "ĠSUV": 28452, 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"ĠMyst": 28510, "Ġmaison": 28511, "leb": 28512, "Ġbowls": 28513, "ĠBürger": 28514, "ĠRoosevelt": 28515, "RP": 28516, "ê°ĢìļĶ": 28517, "ĠDelicious": 28518, "erdings": 28519, "ĠAssociate": 28520, "ousse": 28521, "ĠCort": 28522, "ĠRepeat": 28523, "ĠGlory": 28524, "Ġcontag": 28525, "à¹Ģล": 28526, "ĠParad": 28527, "ĠKerry": 28528, "Ġê¿": 28529, "ĠWave": 28530, "å¿ħ": 28531, "Ġgateway": 28532, "çIJĥ": 28533, "!ãĢį": 28534, "Ġtranscend": 28535, "Ġdamages": 28536, "Ġtails": 28537, "Ġgravitational": 28538, "ĠShield": 28539, "Ġprimitive": 28540, "Ġcarriers": 28541, "ĠHuawei": 28542, "ÙĤد": 28543, "Ġfeliz": 28544, "ĠMia": 28545, "åĥķ": 28546, "ĠпÑĢÑıмо": 28547, "ĠпÑĢоиÑģÑħодиÑĤ": 28548, "ĠMurphy": 28549, "ĠActiv": 28550, "ãĥĥãĤ¯": 28551, "Ġdiscomfort": 28552, "×ij×Ķ": 28553, "ĠKell": 28554, "ĠCentury": 28555, "Ġspaghetti": 28556, "ĠDurch": 28557, "Ġcierto": 28558, "ĠEmpress": 28559, "Ġguts": 28560, "neg": 28561, "ĠдоÑģÑĤаÑĤоÑĩно": 28562, "Ġvoluntary": 28563, "失": 28564, "Ġsquirrel": 28565, "欢": 28566, "ãģ¡ãĤī": 28567, "ĠMaz": 28568, "´ìĭ¬": 28569, "Ġви": 28570, "ãĤ§": 28571, "ĠÑĤакиÑħ": 28572, "ĠSharon": 28573, "Ġenthusiastic": 28574, "irement": 28575, "Ġíŀĺëĵ¤": 28576, "Ġpotrze": 28577, "Ġinitiated": 28578, "ãĥ§": 28579, "ĠÅĽrod": 28580, "ĠìĿ´ë¦Ħ": 28581, "Ġremake": 28582, "Ġculmin": 28583, "Ġconfuse": 28584, "miyor": 28585, "urar": 28586, "CTOR": 28587, "Ġbunny": 28588, "Ġ大": 28589, "ä¸įèĥ½": 28590, "elp": 28591, "Ġvampire": 28592, "Ġillumin": 28593, "ĠHend": 28594, "ĠкаÑĩе": 28595, "ĠSalv": 28596, "Ġканал": 28597, "Ġporta": 28598, "Ġasshole": 28599, "Ġsupporter": 28600, "Ġskeptical": 28601, "Ġknead": 28602, "Ġìĺ¬": 28603, "eza": 28604, "Ġquê": 28605, "ĠDH": 28606, "Ġrodz": 28607, "owners": 28608, "Ġplots": 28609, "Ġdelays": 28610, "Ġbelonged": 28611, "Ġahh": 28612, "Ġcarved": 28613, "Ġrisen": 28614, "Ġorden": 28615, "phony": 28616, "issy": 28617, "!!!!!!!!": 28618, "ĠolduÄŁunu": 28619, "Ġroses": 28620, "Ġintrins": 28621, "ĠAngst": 28622, "Ġfinalement": 28623, "ì§Ŀ": 28624, 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29554, "Ġkara": 29555, "าà¸Ļ": 29556, "Ġfavorable": 29557, "ĠìķĬìķĺ": 29558, "ĠHispanic": 29559, "à¸ľ": 29560, "Ġ×Ķ׼": 29561, "Ġvalidate": 29562, "ĠResident": 29563, "Ġcomenz": 29564, "beiter": 29565, "erer": 29566, "ä¸Ģèµ·": 29567, "Ġdado": 29568, "atching": 29569, "metros": 29570, "ĠHin": 29571, "ĠDum": 29572, "Ġhazır": 29573, "ĠNatalie": 29574, "Ġencryption": 29575, "оÑĩка": 29576, "mma": 29577, "houses": 29578, "Ġanalytical": 29579, "ĠDang": 29580, "first": 29581, "æŃĮ": 29582, "çºĮ": 29583, "ĠEnc": 29584, "cando": 29585, "Ġludzi": 29586, "wart": 29587, "Ġstatistic": 29588, "ĠìĤ°": 29589, "Ġcommenting": 29590, "Ġcoordinated": 29591, "ĠHyper": 29592, "åļ": 29593, "ĠBert": 29594, "çľ¾": 29595, "ĠHip": 29596, "kem": 29597, "ünü": 29598, "Ġzal": 29599, "ĠíķĺëĬĶëį°": 29600, "ĠRobot": 29601, "éĸ±": 29602, "rawn": 29603, "Ġrhetoric": 29604, "ullah": 29605, "ĠDiet": 29606, "Ġtakich": 29607, "Ġpossessed": 29608, "ĵľëĬĶ": 29609, "Ġwakes": 29610, "ĠRaf": 29611, "Mart": 29612, "Ġecc": 29613, 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"ĠSlowly": 29674, "ĠBrig": 29675, "ĠTorah": 29676, "Ġache": 29677, "Ġ???": 29678, "ĠDob": 29679, "kannt": 29680, "Mary": 29681, "Ġstam": 29682, "ĠDemon": 29683, "pla": 29684, "ĠFreund": 29685, "ĠBenn": 29686, "Ġhighs": 29687, "Ġکر": 29688, "ĠPrepare": 29689, "Ġproxy": 29690, "Ġcampo": 29691, "ĠAugen": 29692, "£¨ë": 29693, "ĠChloe": 29694, "icularly": 29695, "young": 29696, "ĠãģĮ": 29697, "©Ķë": 29698, "Ġscratching": 29699, "Ġglac": 29700, "Ġgemeinsam": 29701, "anal": 29702, "acaksın": 29703, "ĠForum": 29704, "ennial": 29705, "ĠResources": 29706, "ãģ¨æĢĿãģĦãģ¾ãģĻ": 29707, "Ġmeisten": 29708, "ĠFell": 29709, "Ġunanim": 29710, "ĠTB": 29711, "ĠSelbst": 29712, "æĨ": 29713, "Ġintimidating": 29714, "ĠGefühl": 29715, "Ġì½Ķë¡ľ": 29716, "æĭī": 29717, "idor": 29718, "iciones": 29719, "arsa": 29720, "]..": 29721, "azo": 29722, "Ġkendi": 29723, "ĠTage": 29724, "termin": 29725, "ĠProzent": 29726, "Maybe": 29727, "lé": 29728, "Ġquesti": 29729, "Ġmemes": 29730, "Ġcorre": 29731, "ĠVIP": 29732, "ĠGallery": 29733, "Ġurgency": 29734, "Ġnoche": 29735, "Ġkindly": 29736, "ĠMeredith": 29737, "ĠváºŃy": 29738, "ĠاÙĦب": 29739, "ĠEstado": 29740, "åĩºä¾Ĩ": 29741, "zug": 29742, "oque": 29743, "Ġobesity": 29744, "Off": 29745, "ĠEuropeans": 29746, "öd": 29747, "ì¹´ë": 29748, "Ġhoop": 29749, "Ġenjoys": 29750, "ĠChip": 29751, "patient": 29752, "Ġmicroscope": 29753, "Ġlegitim": 29754, "ĠÑıвлÑıеÑĤÑģÑı": 29755, "Ïĥι": 29756, "argent": 29757, "Ġsham": 29758, "Ġlicensing": 29759, "olia": 29760, "Sorry": 29761, "rama": 29762, "Ġaccelerated": 29763, "Ġwym": 29764, "Ġfairness": 29765, "ĠReading": 29766, "Ġslack": 29767, "ĠDok": 29768, "ziÄĻkujÄĻ": 29769, "Ġrubbing": 29770, "аÑĤÑĥ": 29771, "Ġallocated": 29772, "jung": 29773, "Ġpains": 29774, "Ġwinding": 29775, "Ġgeliyor": 29776, "ĠCU": 29777, "mot": 29778, "cock": 29779, "ĠPosition": 29780, "bros": 29781, "Ġlivestream": 29782, "ĠBrain": 29783, "ì°©": 29784, "Ġprzek": 29785, "ĠEi": 29786, "ĠCoco": 29787, "ба": 29788, "Ġshovel": 29789, "ãĥıãĥı": 29790, "ea": 29791, "Ġchocol": 29792, "Ġrebellion": 29793, "Ġshowc": 29794, "ĠHalo": 29795, "Ġdividend": 29796, "mission": 29797, "Ġusando": 29798, "Ġ[\"": 29799, "Ġfalei": 29800, "æĽ¸": 29801, "Black": 29802, "ĠSurely": 29803, "ĠÅ»": 29804, "Ġphilosopher": 29805, "ä½łä»¬": 29806, "Ġoverhe": 29807, "ĠBorn": 29808, "Ġobjetivo": 29809, "Ġ128": 29810, "scheid": 29811, "ĠNazis": 29812, "Ġsolche": 29813, "lift": 29814, "cede": 29815, "adors": 29816, "Ġmarshm": 29817, "ĠLORD": 29818, "ĶìĿ´íģ¬": 29819, "Ġowning": 29820, "Cont": 29821, "Ġlandscapes": 29822, "Ġlending": 29823, "ĠAuthority": 29824, "овой": 29825, "oqu": 29826, "ĠSes": 29827, "ĠFerrari": 29828, "Ġresponsabil": 29829, "Ġvários": 29830, "Ġdelic": 29831, "Ġembark": 29832, "Ġembroider": 29833, "Ġframeworks": 29834, "Ġsimmer": 29835, "Ġnacional": 29836, "Ġremainder": 29837, "ĠVielleicht": 29838, "Ġquieres": 29839, "ìĹĶ": 29840, "Ġtestoster": 29841, "ihen": 29842, "ĠOz": 29843, "èle": 29844, "Ġportrayed": 29845, "κε": 29846, "ĠPolitik": 29847, "Ġaperture": 29848, "Ġbland": 29849, "indust": 29850, "ĠобÑĢаÑĤ": 29851, "ĠThous": 29852, "Bay": 29853, "Ġdando": 29854, "Ġsher": 29855, "Ġadmissions": 29856, "ĠCrew": 29857, "ĠÑĸн": 29858, "SINGING": 29859, "Ġounce": 29860, "Ġiy": 29861, "Ġbasil": 29862, "Ġovertime": 29863, "Ġthreaten": 29864, "Ġpartnered": 29865, "ĠCann": 29866, "avana": 29867, "ĠзнаеÑĤе": 29868, "éĢĻäºĽ": 29869, "ĠоÑĤÑģ": 29870, "ĠTudo": 29871, "ì½Ķ": 29872, "ĠëĨĢë": 29873, "fel": 29874, "Ġrearr": 29875, "Ġinward": 29876, "ĠRogers": 29877, "à¹ĥห": 29878, "Ġtweak": 29879, "Ġdryer": 29880, "cession": 29881, "Ġrigorous": 29882, "ĠDaar": 29883, "omics": 29884, "Ġfats": 29885, "vad": 29886, "Ġzipper": 29887, "acceptable": 29888, "Ġdemonstrating": 29889, "ĠYum": 29890, "Ġbeau": 29891, "Ġroster": 29892, "Ġpredominantly": 29893, "еÑĢÑĥ": 29894, "ningar": 29895, "Ġtriangles": 29896, "Ġtexting": 29897, "Ġberries": 29898, "ĠìĤ¬ì§Ħ": 29899, "éĶĻ": 29900, "adder": 29901, "Ġfaites": 29902, "ĠImage": 29903, "lere": 29904, "Ġbounds": 29905, "ĠLaur": 29906, "ĠìķĦ무ë": 29907, "Ġmio": 29908, "Ġusa": 29909, "ĠØ°": 29910, "Ġtoen": 29911, "ĠJang": 29912, "že": 29913, "chod": 29914, "anan": 29915, "ĠобÑĢазом": 29916, "Ġpersever": 29917, "ĠSwe": 29918, "Ġaugment": 29919, "ä¸ĥ": 29920, "uggling": 29921, "ièrement": 29922, "istles": 29923, "acjÄĻ": 29924, "91": 29925, "Ġmah": 29926, "ĠKIR": 29927, "Die": 29928, "Ġdownhill": 29929, "Ġ1968": 29930, "оÑĢоÑĪо": 29931, "å¹¹": 29932, "ographics": 29933, "Ġtässä": 29934, "ê²łì£ł": 29935, "ĠлиÑĩ": 29936, "AUDIO": 29937, "ĠплоÑħ": 29938, "Ġproposing": 29939, "éł»": 29940, "Ġtempted": 29941, "Ġconverting": 29942, "ĠLehr": 29943, "Ġpersone": 29944, "ĠFeeling": 29945, "ìĸ´ì£¼": 29946, "ombres": 29947, "Ġ׾×Ļ": 29948, "Ġguru": 29949, "Ġdement": 29950, "низ": 29951, "иÑĤелей": 29952, "Ġcompañ": 29953, "æľª": 29954, "å¸ĮæľĽ": 29955, "Ġredo": 29956, "Ġconductor": 29957, "mia": 29958, "Ġidols": 29959, "ĠMul": 29960, "Ġinex": 29961, "Ġtämä": 29962, "Ġimpacting": 29963, "Ġdaylight": 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"ãħĭãħĭãħĭ": 30021, "ĠOriginal": 30022, "×Ļ×Ĺ": 30023, "Ġbroadcasting": 30024, "ĠпеÑĢвÑĭй": 30025, "uchi": 30026, "Ġheure": 30027, "Ġgrabs": 30028, "ĠWOR": 30029, "ĠPlaid": 30030, "Min": 30031, "Ġpaz": 30032, "ĠPuis": 30033, "umu": 30034, "itates": 30035, "Ġcoats": 30036, "Ġbuen": 30037, "Ġheir": 30038, "Ġpneum": 30039, "שר": 30040, "enser": 30041, "ĠJUDGE": 30042, "Ġblonde": 30043, "á¹Ľ": 30044, "Ġgak": 30045, "Ġsık": 30046, "Ġquoted": 30047, "Ġequipo": 30048, "Ġwishing": 30049, "ÃŃcia": 30050, "Ġverbs": 30051, "çµĦ": 30052, "ĠCanadians": 30053, "Ġgoverning": 30054, "ĠEvans": 30055, "Euro": 30056, "Ġgenres": 30057, "Ġunterschied": 30058, "ĠBecky": 30059, "³¼ê²ĮìļĶ": 30060, "Ġeinge": 30061, "ĠRaise": 30062, "oland": 30063, "ĠStrateg": 30064, "Ġeres": 30065, "ĠVeterans": 30066, "Ġbreakout": 30067, "Ġsanté": 30068, "Ġadel": 30069, "Ġinvestigated": 30070, "Ġpeur": 30071, "Ġagile": 30072, "Ġrailroad": 30073, "anska": 30074, "Ġей": 30075, "Ġexpos": 30076, "atories": 30077, "ĠContent": 30078, "Ġtruths": 30079, "ĠTrail": 30080, "Ġgua": 30081, "Ġpores": 30082, "Ġwritings": 30083, "ĠUhr": 30084, "ĠThats": 30085, "Ġicing": 30086, "OC": 30087, "ĠProduction": 30088, "Ġcarne": 30089, "ISS": 30090, "Ġninguém": 30091, "non": 30092, "Ġvicious": 30093, "×ķ×Ķ": 30094, "Ġreconnect": 30095, "Ġcentres": 30096, "ĠKem": 30097, "Ġcrease": 30098, "ĠìĿ´ë¯¸": 30099, "айÑĤеÑģÑĮ": 30100, "ĠбоÑĢ": 30101, "ĠHayır": 30102, "ĠÑģÑĥд": 30103, "Ġúnica": 30104, "owaÅĤ": 30105, "Ġadher": 30106, "hua": 30107, "ZZ": 30108, "Ġpreciso": 30109, "Ġcurrents": 30110, "Ġseasoned": 30111, "ĠIoT": 30112, "ĠBishop": 30113, "è¨Ī": 30114, "sted": 30115, "ĠBernard": 30116, "ì¤ĺ": 30117, "æ²»": 30118, "ĠGlenn": 30119, "Ġktórym": 30120, "ืà¹Ī": 30121, "Ġastrolog": 30122, "ĠKot": 30123, "å¤ľ": 30124, "Ġparfois": 30125, "Ġforwards": 30126, "ĠWiÄĻ": 30127, "ĠÎĺ": 30128, "Ġnano": 30129, "è»į": 30130, "sub": 30131, "ĠBrill": 30132, "Ġgrit": 30133, "Ġcited": 30134, "gado": 30135, "Ġmelts": 30136, "Ġforcé": 30137, 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30196, "Ġextraction": 30197, "ĠAV": 30198, "ĠGibbs": 30199, "Ġíľ": 30200, "Ġsı": 30201, "IAM": 30202, "View": 30203, "ĠGRANT": 30204, "Ġ몸": 30205, "Ġverification": 30206, "Ġdepicted": 30207, "ĠMoz": 30208, "oux": 30209, "Ġtul": 30210, "Ġscanner": 30211, "Ġcomedian": 30212, "ĠVolks": 30213, "ĠJEFF": 30214, "è¨Ĥéĸ±": 30215, "§Ħ": 30216, "Ġdistraction": 30217, "rá": 30218, "ĠINTER": 30219, "Ġsincer": 30220, "Ġ×ŀת": 30221, "Ġש׳": 30222, "Ġconstructive": 30223, "arf": 30224, "ĠëĪĦë": 30225, "Ġeco": 30226, "ramos": 30227, "Ġrenewed": 30228, "inement": 30229, "ĠUb": 30230, "ĠPepper": 30231, "ì§Ģê°Ģ": 30232, "ĠDarwin": 30233, "Ġmerchand": 30234, "Ġvárias": 30235, "èce": 30236, "NG": 30237, "ĠìľĦíķ´ìĦľ": 30238, "ĠакÑĤив": 30239, "ĠUnters": 30240, "عÙĦ": 30241, "Ġintric": 30242, "omma": 30243, "ieving": 30244, "ĠCaroline": 30245, "åĵģ": 30246, "ĠPRES": 30247, "Ġperformer": 30248, "Ġautour": 30249, "ãģ¾ãģĽãĤĵ": 30250, "Ġutterly": 30251, "Ġsynthesis": 30252, "Ġlesbian": 30253, "Ġretrieve": 30254, "Ġmaneira": 30255, "Ġimpair": 30256, "Ġmentoring": 30257, "ĠSouls": 30258, "ĠGoPro": 30259, "ÑĢаÑĤÑĮ": 30260, "Ġcose": 30261, "ĠSSD": 30262, "IRE": 30263, "Ġupfront": 30264, "ĠAun": 30265, "Ġgamer": 30266, "Ġlitt": 30267, "Ġaggression": 30268, "ĠLikewise": 30269, "ĠBetty": 30270, "ĠDart": 30271, "ĠDLC": 30272, "ishment": 30273, "ìŀ¥ìĿĦ": 30274, "Ġ对": 30275, "ç»ı": 30276, "cream": 30277, "ĠBabylon": 30278, "Ġnug": 30279, "brar": 30280, "Ġaynı": 30281, "amily": 30282, "bike": 30283, "ahahaha": 30284, "loyd": 30285, "Ġmira": 30286, "Ġperme": 30287, "ĠGaming": 30288, "Ġfirmware": 30289, "Ma": 30290, "Ġassisted": 30291, "atics": 30292, "Ġìķŀìľ¼ë¡ľ": 30293, "ĠMental": 30294, "niejs": 30295, "ĠIz": 30296, "owÄħ": 30297, "Ġtougher": 30298, "Ġdeed": 30299, "èĭ¦": 30300, "Ġstylish": 30301, "ĠTools": 30302, "ĠHamp": 30303, "Ġsunscreen": 30304, "Ġarticulate": 30305, "iye": 30306, "иÑĦ": 30307, "ĠSpread": 30308, "ĠHAVE": 30309, "Ġswirl": 30310, "Ġsponsoring": 30311, "ä»ĭ": 30312, "iovascular": 30313, "mesi": 30314, "Ġrelaxation": 30315, "ĠÑģвоиÑħ": 30316, "Ġmargins": 30317, "ĠsaÄŁ": 30318, "ĠPride": 30319, "ĠÏĦοÏħÏĤ": 30320, "иÑĨи": 30321, "enci": 30322, "Does": 30323, "Ġcorpse": 30324, "Ġendurance": 30325, "Ġíŀĺ": 30326, "ì¹´": 30327, "Ġhaircut": 30328, "Ġinterrupted": 30329, "Ġwindy": 30330, "ĠCaleb": 30331, "ÏģÏĩ": 30332, "ĠPourquoi": 30333, "Ġholistic": 30334, "uclear": 30335, "ĠWhole": 30336, "士": 30337, "Act": 30338, "Ġgallon": 30339, "cade": 30340, "ĠRegional": 30341, "roads": 30342, "ĠSchne": 30343, "áng": 30344, "Ġизмен": 30345, "ãĤĪãģŃ": 30346, "Ġmenus": 30347, "Ġsplitting": 30348, "Ġpriced": 30349, "ĠÎĵ": 30350, "Ġusername": 30351, "ĠÐŀÑĩ": 30352, "Ġcompressed": 30353, "yin": 30354, "Ġguardian": 30355, "Ġgoof": 30356, "Ġchecklist": 30357, "Ġinterchange": 30358, "Ġexpedition": 30359, "Ġextern": 30360, "Ġinfrared": 30361, "engo": 30362, "Ġdenying": 30363, "Ġpackets": 30364, "onent": 30365, "BB": 30366, "ĠIncre": 30367, "Ġsini": 30368, "ÃŁer": 30369, "èg": 30370, "maal": 30371, "generation": 30372, "Ġminorities": 30373, "Ġllevar": 30374, "Ġnomination": 30375, "Ġconsid": 30376, "Ġ×ľ×¢": 30377, "muÅŁ": 30378, "ĠEsc": 30379, "Ġnumerator": 30380, "Ġkaik": 30381, "Ġktórych": 30382, "iesen": 30383, "Ġvê": 30384, "ĠUSS": 30385, "ĠPrivate": 30386, "Ġодно": 30387, "Ġalém": 30388, "ÃŃtulo": 30389, "Ġlimb": 30390, "Ġforgiven": 30391, "Ġdisclosure": 30392, "ÏĦί": 30393, "Ġningún": 30394, "Ġtherapeutic": 30395, "Ġnegotiating": 30396, "ĠNike": 30397, "enseful": 30398, "Ġincap": 30399, "Ġflagship": 30400, "town": 30401, "âĪ": 30402, "ĠÏĢολ": 30403, "Ġwolves": 30404, "Ġviolations": 30405, "ĠArnold": 30406, "Ġintervene": 30407, "Ġheater": 30408, "Ġrecursos": 30409, "Ġmaid": 30410, "ê²¼": 30411, "ĠдавайÑĤе": 30412, "ĠCelebr": 30413, "Ġcape": 30414, "ĠSty": 30415, "ainen": 30416, "site": 30417, "bij": 30418, "ĠполÑĮз": 30419, "Ġframed": 30420, "Ġpublishers": 30421, "ĠÑĩÑĥÑĤÑĮ": 30422, "Ġtemptation": 30423, "Ġcerteza": 30424, "Ġexempt": 30425, "ìĬ¹": 30426, "selling": 30427, "ĠTask": 30428, "hoon": 30429, "ĠCoc": 30430, "ĠParks": 30431, "Ġrepetition": 30432, "ĠÑĤÑĥда": 30433, "Ġensl": 30434, "ĠdeÄŁiÅŁ": 30435, "ĠOrlando": 30436, "ĠMainten": 30437, "æŃ¢": 30438, "ocument": 30439, "ĠHC": 30440, "Ġscooter": 30441, "ĠнапиÑģ": 30442, "Ġtighter": 30443, "Ġtease": 30444, "Ġremoves": 30445, "Ġkijken": 30446, "ĠÑģÑĥÑīеÑģÑĤв": 30447, "Ġthé": 30448, "ĠвÑĭглÑıд": 30449, "Ġrelieve": 30450, "Ġmitä": 30451, "Ġstationary": 30452, "öff": 30453, "pable": 30454, "Ġarter": 30455, "Ġdéf": 30456, "rative": 30457, "Ġconect": 30458, "Ġsaddle": 30459, "ĠDiane": 30460, "Ġcommemor": 30461, "fendim": 30462, "SÃŃ": 30463, "Ġíģ´ë": 30464, "Ġmange": 30465, "atte": 30466, "Ġarrogant": 30467, "Ġrobotic": 30468, "ĠgiÃł": 30469, "æĺ¯çļĦ": 30470, "Ġneighbourhood": 30471, "isson": 30472, "Ġдвиж": 30473, "ĠRI": 30474, "ĠNorman": 30475, "brand": 30476, "amation": 30477, "Ġrazor": 30478, "Ġmurders": 30479, "ĠÑĤÑĥ": 30480, "Ġwszystkim": 30481, "Ġutilities": 30482, "Ġmicroscop": 30483, "ê¿": 30484, "Ġdaqui": 30485, "ollar": 30486, "ĠÐĶавайÑĤе": 30487, "Ġannée": 30488, "Ġkilometres": 30489, "Ġhomosexual": 30490, "Ġarchitects": 30491, "ãģ¡ãģ¯": 30492, "Ġniye": 30493, "LER": 30494, "Ġmicrophones": 30495, "ĠStunden": 30496, "Ġconsecutive": 30497, "ienda": 30498, "vänd": 30499, "DER": 30500, "Ġlifts": 30501, "ĠMeat": 30502, "Ġsavez": 30503, "íĸĪëįĺ": 30504, "Men": 30505, "Ġdismant": 30506, "거를": 30507, "Ġinsulation": 30508, "Ġscall": 30509, "Ġspooky": 30510, "Ġparc": 30511, "Ġballet": 30512, "ĠWhatsApp": 30513, "Ġfranc": 30514, "Ġdeliberate": 30515, "ĠíħĮ": 30516, "Ġmars": 30517, "ĠZur": 30518, "Pr": 30519, "disciplinary": 30520, "Ġobsession": 30521, "ме": 30522, "Ġmarching": 30523, "ĠEmergency": 30524, "iguous": 30525, "Ġszy": 30526, "ĠLands": 30527, "Ġboarding": 30528, "ĠпоÑĩÑĤи": 30529, "Ġenvy": 30530, "Ġcompassionate": 30531, "Ġmerci": 30532, "Ġdesirable": 30533, "dale": 30534, "Ġcanım": 30535, "ĠAntar": 30536, "temps": 30537, "Ġconfigured": 30538, "ĠCompared": 30539, "neh": 30540, "icating": 30541, "Ġnickel": 30542, "ÙĪÙĤ": 30543, "ÙĥÙĪÙĨ": 30544, "opes": 30545, "Ġformulas": 30546, "ĠÐķÑģÑĤÑĮ": 30547, "Ġpobl": 30548, "ĠPJ": 30549, "ĠLud": 30550, "ä»ĬåĽŀ": 30551, "ĠBrid": 30552, "ĠHog": 30553, "ĠBris": 30554, "Jen": 30555, "Ġshading": 30556, "ĠYas": 30557, "Ġdisturbed": 30558, "Ġrecommending": 30559, "Ġcé": 30560, "ĠHOW": 30561, "ìĹĪìĸ´": 30562, "Ġreversed": 30563, "ĠInterestingly": 30564, "ioxid": 30565, "åħŃ": 30566, "Ġìĺ¤ì¼ĢìĿ´": 30567, "ếu": 30568, "xx": 30569, "Ġouais": 30570, "ĠYouTubers": 30571, "ĠRosa": 30572, "ĠHaupt": 30573, "jadi": 30574, "Ġvlogs": 30575, "Ġcultura": 30576, "ĠLeadership": 30577, "ĠHep": 30578, "Ġillum": 30579, "´ëıĻ": 30580, "Ġcustomized": 30581, "Ġmarca": 30582, "Ġquatro": 30583, "Ġнаг": 30584, "ĠSpaceX": 30585, "ĠEigen": 30586, "asting": 30587, "ĠolduÄŁu": 30588, "Ġforts": 30589, "ãģī": 30590, "riment": 30591, "iencia": 30592, "Ġtenir": 30593, "roffen": 30594, "Ġ1979": 30595, "Ġcie": 30596, "ĠëIJĺê³ł": 30597, "Ġescri": 30598, "ÏĮÏĤ": 30599, "íı¬": 30600, "uzzy": 30601, "Cong": 30602, "ìĿ¸ìĿ´": 30603, "Great": 30604, "sil": 30605, "éch": 30606, "ãģ¨ãģĭ": 30607, "Ġmultic": 30608, "ĠDisk": 30609, "²ķ": 30610, "Ġfazla": 30611, "Ġlevant": 30612, "Ġabajo": 30613, "urry": 30614, "stru": 30615, "Ġ먹ëĬĶ": 30616, "Ġaccessory": 30617, "Ġдвиг": 30618, "ĠRid": 30619, "2019": 30620, "Ġdownstream": 30621, "æķ¸": 30622, "Ġkaz": 30623, "utan": 30624, "Ġcharcoal": 30625, "Ġafect": 30626, "wu": 30627, "Ġcontexts": 30628, "Ġfeared": 30629, "ĠìĦ¤": 30630, "Ġhistories": 30631, "Ġfas": 30632, "ensible": 30633, "Ġcocoa": 30634, "illar": 30635, "geons": 30636, "Ġspirituality": 30637, "ĠPew": 30638, "Ġpharmacy": 30639, "Ġpassions": 30640, "Ġbos": 30641, "Ġallá": 30642, "Ġthriving": 30643, "ĠReact": 30644, "Ġoccupy": 30645, "Ġwithdrawal": 30646, "Ġallowance": 30647, "ĠFraktion": 30648, "Ġbuddies": 30649, "Ġidle": 30650, "Ġdissolved": 30651, "Ġprevalent": 30652, "Ġmilitar": 30653, "Ġsensing": 30654, "Ġpojaw": 30655, "Ġancora": 30656, "Ġabundant": 30657, "Ġhairst": 30658, "ãģĤãĤĮ": 30659, "Ġtwee": 30660, "Ġnächste": 30661, "ĠMöglichkeit": 30662, "Ġhoo": 30663, "ufficient": 30664, "Ġfantast": 30665, "Ġedible": 30666, "Ġëĸ¨ìĸ´ì": 30667, "ìĽĥ": 30668, "Ġvein": 30669, "ucci": 30670, "Ġdevotion": 30671, "Ġconcealer": 30672, "income": 30673, "Ġrecycled": 30674, "ĠìĬ¤íĥĢ": 30675, "Ġpontos": 30676, "Ġdessus": 30677, "Ġvérit": 30678, "Ġreflections": 30679, "ĠAA": 30680, "Ġtakeaway": 30681, "bare": 30682, "ĠContact": 30683, "eil": 30684, "ĠHear": 30685, "Ġmirac": 30686, "ĠGerilim": 30687, "ĠÑģамÑĭй": 30688, "Ġvivo": 30689, "Ġkilograms": 30690, "ĠCrim": 30691, "ût": 30692, "78": 30693, "Ġsincerely": 30694, "raz": 30695, "Ġë³µ": 30696, "Ġarriv": 30697, "Ġconception": 30698, "ĠPersian": 30699, "Ġsjäl": 30700, "Ġstarring": 30701, "ĠìķĦ무": 30702, "ĠForever": 30703, "еÑģÑĤÑĮ": 30704, "Ġveil": 30705, "Ġsubtit": 30706, "odka": 30707, "ĠоÑĤноÑĪ": 30708, "Ġcooks": 30709, "енÑı": 30710, "Kay": 30711, "Ġniños": 30712, "ĠPhone": 30713, "Ġstitching": 30714, "Ġfingerprint": 30715, "é¢ĺ": 30716, "λά": 30717, "Ġdedicate": 30718, "ĠLob": 30719, "Ġblacks": 30720, "ĠBle": 30721, "bout": 30722, "ĠÄijang": 30723, "Ġeks": 30724, "Ġsquash": 30725, "ĠKü": 30726, "odi": 30727, "ĠnÆ°á»Ľc": 30728, "Ġvoyage": 30729, "Ġplayful": 30730, "ĠØ¥ÙĦÙī": 30731, "anic": 30732, "Ġcondemn": 30733, "ĠBöyle": 30734, "ĠPolize": 30735, "ãĤ¿ãĥ¼": 30736, "Ġayuda": 30737, "Ġpam": 30738, "à¹Ħà¸Ľ": 30739, "ĠKathy": 30740, "един": 30741, "нова": 30742, "Ġbrig": 30743, "eger": 30744, "Ġeagle": 30745, "Ġvisions": 30746, "ĠíķŃìĥģ": 30747, "Ġshitty": 30748, "Ġhott": 30749, "ĠBritt": 30750, "utors": 30751, "ENTE": 30752, "æĽ²": 30753, "Ġphon": 30754, "ĠBing": 30755, "ĠподдеÑĢж": 30756, "spring": 30757, "æĸ¯": 30758, "etten": 30759, "Ġpilgr": 30760, "Ġediyor": 30761, "енÑĤÑĭ": 30762, "aggio": 30763, "Ġjul": 30764, "Ġcomprend": 30765, "teil": 30766, "Ġز": 30767, "Ġperformers": 30768, "Ġinfamous": 30769, "ĠMK": 30770, "çª": 30771, "æ³ģ": 30772, "otle": 30773, "eff": 30774, "ĠHash": 30775, "Ġcoward": 30776, "ĠBRA": 30777, "ĠDD": 30778, "Ġcomida": 30779, "Ġplata": 30780, "Ġflap": 30781, "ĠMehr": 30782, "ribution": 30783, "ĠYemen": 30784, "Ġmysteries": 30785, "ĠÄ°yi": 30786, "Ġstell": 30787, "Ġeyeliner": 30788, "Ġdeles": 30789, "Ġnailed": 30790, "Ġillnesses": 30791, "Ġstacks": 30792, "Ġtrabajar": 30793, "flower": 30794, "ciu": 30795, "Ġcrude": 30796, "Ġsubstantially": 30797, "Ġhomem": 30798, "Ġnephew": 30799, "Ġstamps": 30800, "Ġcarbs": 30801, "ÑĮÑĤе": 30802, "mooth": 30803, "Ġtunnels": 30804, "acie": 30805, "æ³¢": 30806, "ĠSeñ": 30807, "ĠHera": 30808, "ĠìķĦëĭĪìĹIJìļĶ": 30809, "ĠWyoming": 30810, "ĠHDMI": 30811, "ĠLis": 30812, "ución": 30813, "Ġsteer": 30814, "оÑİ": 30815, "иÑĤа": 30816, "NT": 30817, "Ġìĸ¼êµ´": 30818, "Ġpalms": 30819, "Ġneon": 30820, "ованиÑı": 30821, "Ġfiltering": 30822, "Ġjouer": 30823, "ĠHö": 30824, "ĠнеÑģ": 30825, "ê²łìĸ´ìļĶ": 30826, "Ġ81": 30827, "Ġstoryline": 30828, "Ġprzep": 30829, "Ġthanking": 30830, "ĠBoeing": 30831, "Ġsoftly": 30832, "jem": 30833, "алÑĮнÑĭÑħ": 30834, "Ġflashlight": 30835, "ĠпÑĥ": 30836, "ĠWOMAN": 30837, "ắc": 30838, "ÃŃch": 30839, "Ġluxurious": 30840, "Ġwün": 30841, "Ġimpactful": 30842, "Ġconson": 30843, "reu": 30844, "irring": 30845, "ifter": 30846, "Ġconstituents": 30847, "èIJ½": 30848, "Ġ94": 30849, "ĠTou": 30850, "gom": 30851, "ĠìĥĿê°ģìĿĦ": 30852, "Ġstereotypes": 30853, "Ġmożli": 30854, "åĪĨ享": 30855, "Ĥ¨": 30856, "Ġpencils": 30857, "ĠÑģлож": 30858, "Ġihrem": 30859, "ĠBesch": 30860, "ĠKoh": 30861, "ĠEntscheid": 30862, "Ġlek": 30863, "Ġförs": 30864, "Ġtotalmente": 30865, "Ġlively": 30866, "Ġentropy": 30867, "Ġdiscern": 30868, "ĠÐĹна": 30869, "Ġdov": 30870, "Ġmythology": 30871, "è¨ĺå¾Ĺ": 30872, "apanese": 30873, "Ġapproximate": 30874, "аÑĤив": 30875, "ifiable": 30876, "ĠSeo": 30877, "åĢĴ": 30878, "´ìĭ¬íŀĪ": 30879, "Ġìĺ·": 30880, "Ġtemporal": 30881, "ĠiT": 30882, "Ġestat": 30883, "ким": 30884, "Ġsprink": 30885, "Ġgrund": 30886, "Ġinfantry": 30887, "Ġschaffen": 30888, "ç´Ħ": 30889, "Ġank": 30890, "riages": 30891, "ĠYeon": 30892, "ĠMoroc": 30893, "Ġinvasive": 30894, "ģĶ": 30895, "Ġparenting": 30896, "ĠRis": 30897, "ibile": 30898, "Ġmods": 30899, "å½¢": 30900, "ĠпÑĢовеÑĢ": 30901, "ĠThing": 30902, "ĠWherever": 30903, "Ġacknowledging": 30904, "Ġpawn": 30905, "ummer": 30906, "orb": 30907, "69": 30908, "Ġretrouve": 30909, "Ġrelies": 30910, "ĠHighway": 30911, "Ġawe": 30912, "ãģ§ãģĻãģĭ": 30913, "itaire": 30914, "Ġapplicant": 30915, "Ġaisle": 30916, "worm": 30917, "Ġpayload": 30918, "Ġcarre": 30919, "ĠBach": 30920, "æł¼": 30921, "Ġì¹ľêµ¬ë": 30922, "ние": 30923, "ĠitÃŃs": 30924, "onnaise": 30925, "sol": 30926, "èı¯": 30927, "algia": 30928, "Ġrocking": 30929, "Ġbesten": 30930, "rites": 30931, "^^": 30932, "иной": 30933, "Ġbaixo": 30934, "Ġ기ìĸµ": 30935, "оÑĤÑĢи": 30936, "sim": 30937, "Ġincarn": 30938, "ëĭ¤ìĿĮ": 30939, "Ġlick": 30940, "sided": 30941, "Ġ71": 30942, "forder": 30943, "Ġresonance": 30944, "Ġtegen": 30945, "Ġmetaph": 30946, "owser": 30947, "Ġ×IJ׳×Ĺ׳×ķ": 30948, "?ãĢį": 30949, "Ġspielen": 30950, "Ġvolley": 30951, "ĶìĿ´íģ¬ìĹħ": 30952, "looked": 30953, "Ġsentenced": 30954, "Ġmultiplying": 30955, "Ġideals": 30956, "Ġwahrscheinlich": 30957, "Ġdeposits": 30958, "bilir": 30959, "Ġeffet": 30960, "illon": 30961, "Īë§Į": 30962, "Ġtestimon": 30963, "Ġzawsze": 30964, "ĠпÑĢоÑĨеÑģÑģ": 30965, "ĠLav": 30966, "ä¸įéĮ¯": 30967, "Ġtravailler": 30968, "Ġlaisse": 30969, "ĠMountains": 30970, "ĠÑĢоб": 30971, "Ġexamined": 30972, "itus": 30973, "Was": 30974, "лÑĭ": 30975, "Ġattributed": 30976, "ĠìĬ¹": 30977, "ĠBaron": 30978, "Ġgep": 30979, "Ġattent": 30980, "ĠCollection": 30981, "Ġtheat": 30982, "ĠCai": 30983, "Ġwells": 30984, "Ġhumano": 30985, "çĹħ": 30986, "ĠHast": 30987, "ĠÑħоÑĤÑı": 30988, "czas": 30989, "Ġpermits": 30990, "Ġlegg": 30991, "Ġepo": 30992, "ĠFen": 30993, "Ġthi": 30994, "ĠFoi": 30995, "Ġélect": 30996, "Ġ83": 30997, "Ġoverth": 30998, "Ġè¬Ŀè¬Ŀ": 30999, "Ġtenant": 31000, "è²·": 31001, "Next": 31002, "Ġpraised": 31003, "security": 31004, "ĠImpact": 31005, "为ä»Ģä¹Ī": 31006, "Ġvouch": 31007, "Ġnegó": 31008, "Ġunve": 31009, "Ġcriticize": 31010, "ĠKenya": 31011, "Ġtactic": 31012, "Ġlogr": 31013, "Ġpois": 31014, "Ġpapa": 31015, "speaks": 31016, "ðŁij": 31017, "ispers": 31018, "Ġsurplus": 31019, "Ġcolder": 31020, "åįĹ": 31021, "åIJ¬": 31022, "plets": 31023, "ĠVienna": 31024, "ĠLead": 31025, "Ġaerial": 31026, "ĠTah": 31027, "енÑĤов": 31028, "ĠGreeks": 31029, "Cam": 31030, "Ġmáxim": 31031, "Ġkuin": 31032, "chio": 31033, "Ġdemonstrates": 31034, "anos": 31035, "ĠCert": 31036, "ĠÑįн": 31037, "Ġblogs": 31038, "ĠìĦľìļ¸": 31039, "Ġbeams": 31040, "иков": 31041, "Ġprompted": 31042, "Ġfrightening": 31043, "ĠPorsche": 31044, "ãģĪãģ¦": 31045, "larını": 31046, "Ġchilling": 31047, "isphere": 31048, "Ġflashing": 31049, "ĠKard": 31050, "bread": 31051, "Ġexh": 31052, "Ġtycker": 31053, "Ġecological": 31054, "ĠMae": 31055, "Ġ×ŀ×IJ×ķ×ĵ": 31056, "ĠëĤĺëıĦ": 31057, "лон": 31058, "yss": 31059, "Ġpergunt": 31060, "Ġprix": 31061, "izzard": 31062, "Ġcancers": 31063, "Ġ91": 31064, "susp": 31065, "ĠItem": 31066, "ÅŁa": 31067, "Ġpest": 31068, "ĠtakÄħ": 31069, "Ġlymph": 31070, "ĠPatri": 31071, "fill": 31072, "Ġreconna": 31073, "Ġoptimism": 31074, "Ġmimic": 31075, "Ġì²ľ": 31076, "ĠMadame": 31077, "ocy": 31078, "lining": 31079, "åijĬ訴": 31080, "erme": 31081, "Ġfolders": 31082, "ĠczÅĤ": 31083, "uchar": 31084, "Ġcurso": 31085, "Ġbreach": 31086, "ниÑĤÑĮ": 31087, "ĠpamiÄĻ": 31088, "Ġelig": 31089, "Ġautop": 31090, "Flow": 31091, "Ġprogrammed": 31092, "ĠProcess": 31093, "Ġfigur": 31094, "ĠSF": 31095, "ĠEles": 31096, "Ġprogrammes": 31097, "Ġdizzy": 31098, "ìĭľê°Ħ": 31099, "Ġлибо": 31100, "Ġsniff": 31101, "ĠSebastian": 31102, "ĠHye": 31103, "Ġ4000": 31104, "Ġpermite": 31105, "æ¢Ŀ": 31106, "ĠзаÑī": 31107, "Ġguit": 31108, "ĠDais": 31109, "Ġaccordance": 31110, "Ġmodular": 31111, "ogeneous": 31112, "æĭį": 31113, "Ġpouquinho": 31114, "Ġartillery": 31115, "Ġlubric": 31116, "Ġvolcan": 31117, "ĠNH": 31118, "ðŁ¤": 31119, "Ġdean": 31120, "Rh": 31121, "Ġministre": 31122, "åĿIJ": 31123, "ĠInv": 31124, "ĠBulgar": 31125, "ĠDaten": 31126, "èİ": 31127, "Im": 31128, "Ġoriginated": 31129, "ĠNixon": 31130, "integr": 31131, "Ġlacks": 31132, "ĠNacht": 31133, "ìĸ´ëĤĺ": 31134, "camera": 31135, "Ġradish": 31136, "kiye": 31137, "Ġanges": 31138, "Ġpréf": 31139, "juk": 31140, "ĠBee": 31141, "ĠBU": 31142, "ĠвоÑģп": 31143, "ĠBT": 31144, "êmes": 31145, "ĠStück": 31146, "ĠInk": 31147, "æĪĸèĢħ": 31148, "ĠSergeant": 31149, "ĠMultip": 31150, "Ġhiçbir": 31151, "ĠСам": 31152, "ĠDé": 31153, "olph": 31154, "ìĸ¸": 31155, "Ġimpat": 31156, "ĠìķĬê³ł": 31157, "ĠÑĤакого": 31158, "ĠнавеÑĢное": 31159, "Ġunpredictable": 31160, "Ġmend": 31161, "ĠìĹĨìĸ´ìļĶ": 31162, "ĠjakieÅĽ": 31163, "Ġanni": 31164, "Ġdonné": 31165, "ĠKirsty": 31166, "Ġrectangular": 31167, "Ġempezar": 31168, "ĠExchange": 31169, "ê°Ķ": 31170, "Ġéconom": 31171, "ãģĵãĤĵ": 31172, "elin": 31173, "reibt": 31174, "Ġ×Ķפ": 31175, "Ġcemetery": 31176, "Ġespañol": 31177, "olin": 31178, "лÑİд": 31179, "Ġgrâce": 31180, "allen": 31181, "ĠPhilos": 31182, "ĠErst": 31183, "ĠìĥĪ": 31184, "ĠVid": 31185, "Give": 31186, "OH": 31187, "μο": 31188, "ĠPare": 31189, "Ġmetabolism": 31190, "Ġmaple": 31191, "Ġaxle": 31192, "ĠDy": 31193, "Ġkomme": 31194, "Ïİν": 31195, "Ġgreatness": 31196, "Ġverified": 31197, "Ġspé": 31198, "ĠFahrenheit": 31199, "ĠBren": 31200, "ĠConfeder": 31201, "Ġhistoire": 31202, "Ġeliminating": 31203, "ĠAdding": 31204, "ĠAbi": 31205, "æĿİ": 31206, "Ġhospitality": 31207, "tim": 31208, "Ġbonito": 31209, "Ġpartes": 31210, "ĠдÑĢÑĥгиÑħ": 31211, "ĠShay": 31212, "ĠSed": 31213, "Ġregrets": 31214, "Ñıми": 31215, "Ġtenants": 31216, "éĢŁ": 31217, "ĠPTS": 31218, "Ġdevi": 31219, "ĠLate": 31220, "uez": 31221, "Ġsöyl": 31222, "ãĤ»": 31223, "Ġìŀ¬ë°Į": 31224, "Ġtoggle": 31225, "Ġmasking": 31226, "алÑĮного": 31227, "Ġpersön": 31228, "Ġamerican": 31229, "fik": 31230, "ĠRGB": 31231, "enson": 31232, "ĠKA": 31233, "wwww": 31234, "ĠÑĢег": 31235, "metics": 31236, "Ġeducator": 31237, "ãĤ·ãĥ«ãĤ¯": 31238, "park": 31239, "елÑĮзÑı": 31240, "arus": 31241, "ÑĢеÑĤ": 31242, "Ġfeito": 31243, "Ġchoir": 31244, "Ġlargo": 31245, "Ġeens": 31246, "Ġwatts": 31247, "ĠSingle": 31248, "Ġsusceptible": 31249, "icer": 31250, "ĠвклÑİÑĩ": 31251, "Ġpus": 31252, "íĻĺ": 31253, "Eng": 31254, "Ġfantas": 31255, "Ġspecification": 31256, "Ġconfronted": 31257, "ĠColumbus": 31258, "ивеÑĤ": 31259, "arım": 31260, "Ġcaffeine": 31261, "munition": 31262, "Ġmigrants": 31263, "lide": 31264, "itations": 31265, "ĠGeme": 31266, "ẫ": 31267, "Ġplanner": 31268, "Ġstimulate": 31269, "Ġaproxim": 31270, "ceu": 31271, "ĠNom": 31272, "Ġvog": 31273, "ĠÑĢаÑģÑĤ": 31274, "Ġenseñ": 31275, "Ġsellers": 31276, "Ġguten": 31277, "zd": 31278, "Cal": 31279, "Ġdescript": 31280, "Ġreconciliation": 31281, "zinho": 31282, "á¹ĩa": 31283, "ãģĺãĤĥãģĤ": 31284, "acyj": 31285, "ĠCOL": 31286, "saw": 31287, "ĠíĻķìĿ¸": 31288, "Ġvarit": 31289, "Ġpartnering": 31290, "Ġdetention": 31291, "Ġbombing": 31292, "clapping": 31293, "iencies": 31294, "ondu": 31295, "AME": 31296, "Ġê°ĻìĬµëĭĪëĭ¤": 31297, "cÃŃa": 31298, "ĠпоÑģÑĤо": 31299, "ĠASMR": 31300, "Ġhomepage": 31301, "Ġsiè": 31302, "antha": 31303, "ĠPoll": 31304, "Ġigen": 31305, "cych": 31306, "Ġê°ijìŀIJ기": 31307, "Ġconsiderably": 31308, "ä»ĸçļĦ": 31309, "ĠArist": 31310, "Ġwithstand": 31311, "Ġqualitative": 31312, "ĠKraft": 31313, "ĠÑįлекÑĤ": 31314, "ĠBead": 31315, "екÑĤив": 31316, "Ġcrushing": 31317, "ì³IJ": 31318, "Ġnavy": 31319, "ÙĪÚº": 31320, "sho": 31321, "Ġoak": 31322, "ippers": 31323, "Ġsoils": 31324, "Ġpigment": 31325, "Ġevitar": 31326, "ãĥĩ": 31327, "Ġfuse": 31328, "ĠDale": 31329, ":\"": 31330, "Ġcomplètement": 31331, "Ġkel": 31332, "à¹Ĩ": 31333, "Ġquatre": 31334, "ĠUM": 31335, "Ġë§IJë": 31336, "æł¹": 31337, "ÃŃr": 31338, "Ġleisure": 31339, "ĠHousing": 31340, "Ġfolds": 31341, "estion": 31342, "ARS": 31343, "Ġmash": 31344, "urpose": 31345, "Ġaccumulated": 31346, "ĠStuff": 31347, "èªŀ": 31348, "Ġtapes": 31349, "ĠÑģилÑĮно": 31350, "ĠLOVE": 31351, "Ġ1982": 31352, "Ġscars": 31353, "Ġcapitalist": 31354, "ĠNed": 31355, "Ġsoften": 31356, "Ġnotably": 31357, "Ġforcément": 31358, "ĠRaum": 31359, "ĠнеобÑħод": 31360, "Ġtrademark": 31361, "Ġfertig": 31362, "Ġ?!": 31363, "æĹł": 31364, "Ġreinforced": 31365, "Ġrecharge": 31366, "ĠPutting": 31367, "Ġvillains": 31368, "Ġhandic": 31369, "Ġadvertisement": 31370, "تÙĬ": 31371, "ĠÑģÑĥм": 31372, "ĠRiley": 31373, "×ķ×ij×": 31374, "京": 31375, "Os": 31376, "از": 31377, "Boy": 31378, "Ġsquish": 31379, "ocket": 31380, "Ġtestify": 31381, "æ¼Ķ": 31382, "Ġ׾×ŀ×": 31383, "ĠмаÑģÑģ": 31384, "manuel": 31385, "ĠArkansas": 31386, "iffe": 31387, "Ġanalysts": 31388, "ĠDeaf": 31389, "Ġjó": 31390, "Ġgroceries": 31391, "ĠWheel": 31392, "ĠÑĢиÑģ": 31393, "Ġcòn": 31394, "ĠCob": 31395, "Ġprisons": 31396, "ève": 31397, "ĠCabinet": 31398, "Ġposed": 31399, "Ġguerre": 31400, "ĠLloyd": 31401, "Ġclerk": 31402, "Ġcrises": 31403, "ĠSho": 31404, "ĠOre": 31405, "ĠFootball": 31406, "ĠAdvis": 31407, "ĠZheng": 31408, "èį": 31409, "ĠAMY": 31410, "Ġunfor": 31411, "Ġmonaster": 31412, "Ġcompile": 31413, "Ġimmortal": 31414, "atable": 31415, "Ġparano": 31416, "Ġtiver": 31417, "ĠSteph": 31418, "ĠFuÃŁ": 31419, "Ġdiscontin": 31420, "Ġripe": 31421, "Ġhacking": 31422, "Ġsiendo": 31423, "Ġseguro": 31424, "altres": 31425, "Ġanderes": 31426, "Ġ리ë": 31427, "Ġexports": 31428, "æŃ¥": 31429, "Ġtabii": 31430, "Ġ기ëĭ¤ë": 31431, "Ġbothering": 31432, "Ġpickle": 31433, "ĠBRIAN": 31434, "Ġaltar": 31435, "ĠпÑĢиб": 31436, "Ġtransferring": 31437, "ĠVors": 31438, "ĠÙĩÙĪ": 31439, "ĠZa": 31440, "ĠFrances": 31441, "Ġbrowse": 31442, "emit": 31443, "Ġchewing": 31444, "ĠFreddy": 31445, "Ġeditors": 31446, "älle": 31447, "ĠíĮĢ": 31448, "ĠSque": 31449, "ĠCultural": 31450, "awk": 31451, "ĠSache": 31452, "ĠCarbon": 31453, "ắt": 31454, "FL": 31455, "ĠNGO": 31456, "peÅĤ": 31457, "ĠSou": 31458, "Ġhvor": 31459, "unintelligible": 31460, "Ġë²ķ": 31461, "Ġ°": 31462, "iin": 31463, "Ġ×¢×Ŀ": 31464, "Ġderrière": 31465, "Ġczym": 31466, "ĠApost": 31467, "Ġregarder": 31468, "Ġagrade": 31469, "ĠCandy": 31470, "Ġmare": 31471, "Ġintroduces": 31472, "birds": 31473, "Ġuniquely": 31474, "Ġmuk": 31475, "Ġcooker": 31476, "Ġcrews": 31477, "Ġjeito": 31478, "ERT": 31479, "¶Ħë": 31480, "nisse": 31481, "Ġef": 31482, "Ġcarte": 31483, "ĠYak": 31484, "ĠPAT": 31485, "ино": 31486, "bokki": 31487, "Ġmates": 31488, "Ġdistint": 31489, "Ġì½Ķë¡ľëĤĺ": 31490, "Ġyıl": 31491, "Ġκάν": 31492, "Ġconfigurations": 31493, "enga": 31494, "recht": 31495, "Happy": 31496, "ãĤĦãģ£ãģ¦": 31497, "invest": 31498, "Ġreconstruct": 31499, "ĠÑįÑĤомÑĥ": 31500, "Ġmosque": 31501, "raum": 31502, "Ġvoyez": 31503, "ĠNBC": 31504, "ĠìŀIJìĭł": 31505, "Ġsturdy": 31506, "Ġкап": 31507, "Ġansch": 31508, "alid": 31509, "Ġmasih": 31510, "ĠREP": 31511, "Ġì½Ķë": 31512, "Ġdeduct": 31513, "Ġsalir": 31514, "wurf": 31515, "ilot": 31516, "ĠMutter": 31517, "olds": 31518, "ĠFEMA": 31519, "ĠBib": 31520, "Ġneighboring": 31521, "Ġbliss": 31522, "Ġíĺ¼": 31523, "лиÑģÑĮ": 31524, "ĠÑĤÑĢеб": 31525, "Ġå°±æĺ¯": 31526, "Ġgrenade": 31527, "Ġegal": 31528, "Ġfinely": 31529, "Ġpetals": 31530, "Ġkeer": 31531, "Ġchyba": 31532, "Ġskipping": 31533, "Ġthirteen": 31534, "Ġgravy": 31535, "ĠSAT": 31536, "61": 31537, "Ġног": 31538, "Ġmins": 31539, "ITE": 31540, "Ġsozial": 31541, "íķĺë©´ìĦľ": 31542, "ruktur": 31543, "Ġвозмож": 31544, "ĠопÑıÑĤÑĮ": 31545, "Ġarth": 31546, "ĠCuban": 31547, "Ġtreasures": 31548, "Ġfertilizer": 31549, "Ġawakening": 31550, "Ġë°±ìĭł": 31551, "Ġrall": 31552, "Ġdepict": 31553, "ĠPablo": 31554, "Ġnineteen": 31555, "Ġwatt": 31556, "Ġentirety": 31557, "KS": 31558, "ĠWoods": 31559, "Sch": 31560, "ĠÚ©ÙĪ": 31561, "ĠDry": 31562, "ãģŀ": 31563, "uve": 31564, "Ġreconstruction": 31565, "Ġanatomy": 31566, "Ī를": 31567, "Ġbaba": 31568, "Ġlistener": 31569, "Ġsharpen": 31570, "ĠPeru": 31571, "ĠвÑĭз": 31572, "Ġrecreation": 31573, "Ġinitiate": 31574, "Ġcalor": 31575, "ĠNaj": 31576, "gee": 31577, "ĠFeels": 31578, "ĠSnapchat": 31579, "ĠTet": 31580, "ĠNest": 31581, "ĠDaf": 31582, "ĠFinish": 31583, "ĠÑĤаким": 31584, "úc": 31585, "izens": 31586, "Ġspins": 31587, "Ġembry": 31588, "Ġpassages": 31589, "Ġcient": 31590, "Ġjustification": 31591, "ä»ĸ說": 31592, "Ġolmaz": 31593, "Ġflooded": 31594, "Ġemoji": 31595, "Ġembracing": 31596, "Ġdiscard": 31597, "ĠBasic": 31598, "agog": 31599, "ĠìľĦíķ´": 31600, "Ġasylum": 31601, "erin": 31602, "Ġfim": 31603, "Ġninja": 31604, "Ġautomate": 31605, "Ġallergic": 31606, "ÿÿÿÿ": 31607, "amam": 31608, "ĠмаÑĢ": 31609, "ĠOi": 31610, "äus": 31611, "Ġinduct": 31612, "ĠBEN": 31613, "ĠzÅĤ": 31614, "Ġkażdy": 31615, "ĠAMP": 31616, "nÄĽ": 31617, "Sure": 31618, "Ġquil": 31619, "Ġespec": 31620, "rok": 31621, "BSCRI": 31622, "Ġliebe": 31623, "pus": 31624, "achsen": 31625, "Ġcricket": 31626, "ëĬIJ": 31627, "ĠFrame": 31628, "ekkür": 31629, "arb": 31630, "ĠpÅĻ": 31631, "иÑģÑģ": 31632, "Ġzeggen": 31633, "Ġdoubles": 31634, "ĠDre": 31635, "test": 31636, "insp": 31637, "boys": 31638, "Ġmão": 31639, "ĠVerse": 31640, "Ġmuscular": 31641, "ĠMALE": 31642, "Ġdulu": 31643, "Ġoccasional": 31644, "Lo": 31645, "conomic": 31646, "Ġvak": 31647, "Ġremedy": 31648, "å¤ł": 31649, "ĠâĻªâĻªâĻª": 31650, "vem": 31651, "Ġönem": 31652, "ĠkarÅŁÄ±": 31653, "ĠSharp": 31654, "hur": 31655, "Ġë°©ë²ķ": 31656, "Ġgrandson": 31657, "Ġaktiv": 31658, "ĠThrones": 31659, "ĠìķĪìĹIJ": 31660, "Ġtots": 31661, "Ġsubd": 31662, "ĠPaula": 31663, "Ġgraves": 31664, "ĠBrent": 31665, "ĠникÑĤо": 31666, "Ġsöz": 31667, "Ġcrec": 31668, "ĠVladimir": 31669, "çĸ«": 31670, "Ġпой": 31671, "Ġ\"-": 31672, "Ġpsy": 31673, "atri": 31674, "idan": 31675, "Ġaún": 31676, "Ġstandardized": 31677, "ì¹ĺë": 31678, "ĠкÑĢов": 31679, "ĠZhu": 31680, "something": 31681, "Ġ750": 31682, "Ġmujeres": 31683, "Ġait": 31684, "éĹ´": 31685, "agu": 31686, "Ġcorrected": 31687, "ikka": 31688, "eled": 31689, "ĠCareer": 31690, "owym": 31691, "Ġroommate": 31692, "Ġdescendants": 31693, "ĠNapoleon": 31694, "ĠÐĶо": 31695, "íĸĪìĸ´ìļĶ": 31696, "Ġbunun": 31697, "ĠMicha": 31698, "ç·ļ": 31699, "Ġdescob": 31700, "PI": 31701, "Ġpalabra": 31702, "Ġtracked": 31703, "Ġdependence": 31704, "ĠBarack": 31705, "åģĩ": 31706, "Ġfertility": 31707, "ĠSouthwest": 31708, "Ġincomplete": 31709, "Ġcomunic": 31710, "Ġcompris": 31711, "ĠRestaur": 31712, "Ġacron": 31713, "κα": 31714, "Ġapprentices": 31715, "Ġmusst": 31716, "ĠAbr": 31717, "Ġpentru": 31718, "ĠConsort": 31719, "ĠAvec": 31720, "Ġdumplings": 31721, "LR": 31722, "Ġwszystkie": 31723, "Ġswamp": 31724, "нев": 31725, "uggle": 31726, "Ġwatercolor": 31727, "Ġproton": 31728, "ĠEspaña": 31729, "ocking": 31730, "овал": 31731, "Ġtakim": 31732, "Very": 31733, "Ġdementia": 31734, "ĠÅŁeyi": 31735, "Jac": 31736, "ĠMacBook": 31737, "ĠLiv": 31738, "fficients": 31739, "ĠHunt": 31740, "Ġoverlay": 31741, "æĦŁè¦º": 31742, "ĠSkype": 31743, "punkt": 31744, "Ġconfined": 31745, "ĠAdrian": 31746, "رÙĥ": 31747, "ĠJeep": 31748, "Ġenquanto": 31749, "Ġanest": 31750, "оÑĤвеÑĤ": 31751, "ĠменÑĮ": 31752, "Ġirrigation": 31753, "á»ijn": 31754, "Ġeighteen": 31755, "ĠPon": 31756, "Ġrescued": 31757, "Ġ1983": 31758, "rü": 31759, "jae": 31760, "ĠJeong": 31761, "Ġamazingly": 31762, "ĠFDP": 31763, "Ġbackstage": 31764, "cue": 31765, "ĠÏĥÏĦην": 31766, "ĠاÙĦص": 31767, "Ġlivestock": 31768, "ĠWarner": 31769, "Ġmajors": 31770, "ãĥģãĥ£": 31771, "Ġcooperative": 31772, "ĠBrady": 31773, "rained": 31774, "rieb": 31775, "Ġ×ij×ŀ×": 31776, "ĠдоволÑĮно": 31777, "ĠFE": 31778, "Ġleaked": 31779, "ĠMercury": 31780, "Ġpersuade": 31781, "Ġtransformer": 31782, "ĠNorweg": 31783, "ĠìŬ룬": 31784, "ĠzrobiÄĩ": 31785, "Ġcardiovascular": 31786, "ĠCrash": 31787, "Ġgossip": 31788, "аÑģÑĤÑĮ": 31789, "Ġ쪽": 31790, "Ġswept": 31791, "ĠHorn": 31792, "ĠAté": 31793, "Ġbukan": 31794, "ĠKaw": 31795, "KY": 31796, "ĠStories": 31797, "Gary": 31798, "Ġgardening": 31799, "ĠQuickly": 31800, "ĠFalcon": 31801, "Ġovat": 31802, "cı": 31803, "ĠComplet": 31804, "ĠDate": 31805, "ĠпÑĢим": 31806, "Ġläuft": 31807, "ĠAudrey": 31808, "ĠWent": 31809, "ĠpelÃŃcul": 31810, "Ġcarriage": 31811, "Ġunacceptable": 31812, "nymi": 31813, "ĠÑģлÑĭÑĪ": 31814, "Ġterre": 31815, "uellement": 31816, "EEEE": 31817, "Ġpharmac": 31818, "hões": 31819, "Ġzich": 31820, "Ġmigrate": 31821, "ĠFry": 31822, "ñana": 31823, "ĠMuito": 31824, "EOVER": 31825, "Ġfortress": 31826, "ĠCompan": 31827, "ĠJSON": 31828, "ordnung": 31829, "Ġwarto": 31830, "Ġungef": 31831, "ìħĶìĦľ": 31832, "ĠÑĢок": 31833, "Ġpaddle": 31834, "Jared": 31835, "Ġsubmitting": 31836, "Ġlatch": 31837, "Ġfug": 31838, "ĠкоÑģ": 31839, "ĠEf": 31840, "Ġlaunches": 31841, "Ġft": 31842, "otechn": 31843, "Ġtravelled": 31844, "اÙģ": 31845, "éģķ": 31846, "Ġproch": 31847, "Ġdedim": 31848, "83": 31849, "Ġrebound": 31850, "ĠLU": 31851, "path": 31852, "ĠÑģпÑĢав": 31853, "Ġöl": 31854, "ĠíĤ¤": 31855, "Ġprivat": 31856, "Ġtractor": 31857, "ĠAttention": 31858, "Ser": 31859, "Ġcoses": 31860, "ária": 31861, "pal": 31862, "ĠìĿĢ": 31863, "Ġsuccessor": 31864, "Ġconnectors": 31865, "ĠÑĥÑģÑĤанов": 31866, "Ġgenocide": 31867, "Ġsufficiently": 31868, "ĠAixò": 31869, "Ġstabilize": 31870, "Ġcongest": 31871, "Ġcarving": 31872, "Ġzost": 31873, "ĠбÑĭÑģÑĤÑĢо": 31874, "Ġshortest": 31875, "Ġlivel": 31876, "Ġ89": 31877, "éģĬ": 31878, "Ġerk": 31879, "Ġportraits": 31880, "à¥Ģ": 31881, "èĺ": 31882, "boat": 31883, "llah": 31884, "ANC": 31885, "Ġempirical": 31886, "ĠEcho": 31887, "ĠNederland": 31888, "è¿Ļä¹Ī": 31889, "Net": 31890, "Ġcuidado": 31891, "ĠRoma": 31892, "Ġcalf": 31893, "Ġgiants": 31894, "ĠExplorer": 31895, "ĠCollect": 31896, "alition": 31897, "ĠDestiny": 31898, "Ġausge": 31899, "ĠEdu": 31900, "ĠClo": 31901, "Ġearrings": 31902, "ĠTrack": 31903, "ĠROS": 31904, "ĠBelle": 31905, "çĻ¾": 31906, "Ġpueda": 31907, "Ġdaytime": 31908, "Ġsupplier": 31909, "ĠSV": 31910, "ĠExhale": 31911, "Ġgalera": 31912, "course": 31913, "Ġcentimeter": 31914, "ĠBast": 31915, "mud": 31916, "Ġsangat": 31917, "ĠPhysical": 31918, "Ġprivately": 31919, "Ġtrata": 31920, "lynn": 31921, "illi": 31922, "Ġë©ĶìĿ´íģ¬ìĹħ": 31923, "Ġcrystall": 31924, "Ġpods": 31925, "ản": 31926, "inator": 31927, "ĠRecords": 31928, "å®ĺ": 31929, "ÄŁimiz": 31930, "issement": 31931, "hare": 31932, "hadow": 31933, "ĠDK": 31934, "ĠìķĮê³ł": 31935, "Ġwyn": 31936, "Ġrequesting": 31937, "ĠDonna": 31938, "ĠìĹ´ìĭ¬íŀĪ": 31939, "inea": 31940, "Ġexert": 31941, "ĠDuncan": 31942, "ĠвеÑĩ": 31943, "ĠHah": 31944, "à¤Ĥ": 31945, "ĠLif": 31946, "ĠFinding": 31947, "ĠNov": 31948, "Ġзнак": 31949, "ĠоÑĦ": 31950, "ĠQuè": 31951, "Ġquarterback": 31952, "ĠÑĦак": 31953, "Ġbipartisan": 31954, "ÄŁin": 31955, "Ġnécess": 31956, "Ġreferendum": 31957, "Ġcompiler": 31958, "Ġprobabil": 31959, "еди": 31960, "Ġtrader": 31961, "æĺĵ": 31962, "ĠRum": 31963, "geme": 31964, "Ġdio": 31965, "ĠbÄĻdziemy": 31966, "ĠÏĢά": 31967, "꾸": 31968, "×ķ×ĺ": 31969, "Ġà¤ķ": 31970, "Ġблаг": 31971, "Ġscalp": 31972, "ĠPause": 31973, "Ġcaption": 31974, "Ġendanger": 31975, "Ġenlar": 31976, "Ġrotten": 31977, "ãĥĥãĥĪ": 31978, "Ġwah": 31979, "èĤī": 31980, "Ġdzi": 31981, "ĠInstall": 31982, "Ay": 31983, "Ġcrear": 31984, "енÑĤа": 31985, "Ġweighing": 31986, "Ġbutterflies": 31987, "ĠGast": 31988, "äºķ": 31989, "horn": 31990, "warz": 31991, "ICEOVER": 31992, "ĠнайÑĤи": 31993, "Ġcoefficients": 31994, "ç°¡åĸ®": 31995, "ĠSpencer": 31996, "ĠHigher": 31997, "Ġcowork": 31998, "å¨ĺ": 31999, "ĠкоÑĤоÑĢое": 32000, "Ġmonit": 32001, "Ġdysfunction": 32002, "ĠÑģÑĤанов": 32003, "Ġtournaments": 32004, "Ġoyster": 32005, "BN": 32006, "Ġtrud": 32007, "slow": 32008, "ĠPenny": 32009, "ĠOdys": 32010, "ær": 32011, "Ġfou": 32012, "Ġenjoyment": 32013, "аÑĤÑĭ": 32014, "ĠwyglÄħda": 32015, "алÑĮнаÑı": 32016, "ĠProtect": 32017, "Ġmoy": 32018, "Ġclaw": 32019, "Ġsuspicion": 32020, "Ġsacrificed": 32021, "Ġgosto": 32022, "Big": 32023, "Ġaggressively": 32024, "Ġvorne": 32025, "ãĥł": 32026, "Ġblamed": 32027, "ĠSehr": 32028, "פר": 32029, "cito": 32030, "Ġseals": 32031, "Ġmujer": 32032, "ĠWeird": 32033, "Ġforens": 32034, "Ġcontributes": 32035, "estra": 32036, "Ġpog": 32037, "LOL": 32038, "Ġhacerlo": 32039, "оÑĤÑĮ": 32040, "fiction": 32041, "79": 32042, "λο": 32043, "大æ¦Ĥ": 32044, "声": 32045, "ĠÑĤоб": 32046, "ĠGS": 32047, "ĠClara": 32048, "itez": 32049, "Ġadvocating": 32050, "ĠíĶĦë": 32051, "sung": 32052, "Ġvertices": 32053, "Ġnavigating": 32054, "Ġeuropé": 32055, "çļĨ": 32056, "Ġslowed": 32057, "Ġforeground": 32058, "ĠIndustrial": 32059, "Ġadore": 32060, "ìĭŃ": 32061, "Ġcréer": 32062, "æŀĹ": 32063, "chnitt": 32064, "Ġunaware": 32065, "Ġcurly": 32066, "entar": 32067, "Ġler": 32068, "Ġprohibited": 32069, "ĠHeroes": 32070, "ĠReed": 32071, "uca": 32072, "Ġsmok": 32073, "Ġkunna": 32074, "zeitig": 32075, "immen": 32076, "ĠLun": 32077, "ĠабÑģолÑİÑĤ": 32078, "Ġdegli": 32079, "Ġvillagers": 32080, "Ġpreset": 32081, "zept": 32082, "uds": 32083, "Ġemit": 32084, "ä½łè¦ģ": 32085, "Ġëī": 32086, "ëĬĶì§Ģ": 32087, "нако": 32088, "Ġosób": 32089, "Ġ1969": 32090, "ĠÐIJÑĢ": 32091, "Ġmanchmal": 32092, "ĠBrock": 32093, "Ġmantra": 32094, "ĠWIL": 32095, "bach": 32096, "inä": 32097, "elas": 32098, "keln": 32099, "Ġdisciple": 32100, "Ġqualc": 32101, "Ġdehyd": 32102, "ìĿ´ëĿ¼ëĬĶ": 32103, "Af": 32104, "ìĦ±ìĿ´": 32105, "Ryan": 32106, "Ġpuppet": 32107, "ĠдÑĢÑĥгие": 32108, "Ġrud": 32109, "Ġpending": 32110, "Plus": 32111, "ĠìķĬìĿĦ": 32112, "Ġbá»ĭ": 32113, "ĠSega": 32114, "çe": 32115, "Ġprogrammer": 32116, "bli": 32117, "Ġunl": 32118, "Ġenslaved": 32119, "Ġsociété": 32120, "Äģh": 32121, "Ġinheritance": 32122, "ĠBangl": 32123, "ermaid": 32124, "Ġpractitioner": 32125, "ĠStalin": 32126, "ĠUser": 32127, "cible": 32128, "Ġcardiac": 32129, "ĠKoreans": 32130, "Ġdumped": 32131, "Ġ×Ķ×Ļ×Ķ": 32132, "áis": 32133, "Ġhydraulic": 32134, "oubtedly": 32135, "ĠPit": 32136, "Ġpicnic": 32137, "Ġbehöver": 32138, "ĠÑģмог": 32139, "Ġbraking": 32140, "é»ij": 32141, "utar": 32142, "ĠìĦ¸ë": 32143, "ubl": 32144, "Ġüz": 32145, "Ġmajesty": 32146, "Ġbers": 32147, "utable": 32148, "Ġhotter": 32149, "çħ§": 32150, "ÛĮÙĨ": 32151, "Ġbiases": 32152, "Ġsubjected": 32153, "Ġnaughty": 32154, "Ġcircus": 32155, "ãģĹãģĭ": 32156, "ĠImmedi": 32157, "ĠStefan": 32158, "ĠTriple": 32159, "enk": 32160, "Ġwit": 32161, "Ġrecycle": 32162, "emie": 32163, "dated": 32164, "Ġunload": 32165, "Ġpopula": 32166, "chin": 32167, "Ġyields": 32168, "Ġenglish": 32169, "ĠBonnie": 32170, "Ġspiders": 32171, "Ãģ": 32172, "Ġerosion": 32173, "éĥ¨åĪĨ": 32174, "ĠNICK": 32175, "иÑıÑħ": 32176, "Ġimpart": 32177, "Ġкни": 32178, "Ġresolutions": 32179, "Ġlithium": 32180, "Ġconvergence": 32181, "ĠTara": 32182, "Ġдве": 32183, "ths": 32184, "ĠCindy": 32185, "æĪijè¦ģ": 32186, "幫": 32187, "ĠDIE": 32188, "Ġassurance": 32189, "ĠопиÑģ": 32190, "Ġbuckets": 32191, "Ġcues": 32192, "ĠQuiet": 32193, "Ġsimilarity": 32194, "Ġfoundational": 32195, "ĠMinist": 32196, "滿": 32197, "Ġpian": 32198, "Ġcentr": 32199, "Ġnumb": 32200, "Ġmonks": 32201, "ujourd": 32202, "enzie": 32203, "Ġskateboard": 32204, "Ġdlatego": 32205, "ĠÑģоÑĤ": 32206, "ĠAE": 32207, "Ġmasterpiece": 32208, "ĠSolomon": 32209, "ĠReddit": 32210, "Ġriot": 32211, "abl": 32212, "ĠJazz": 32213, "Ġelectromagnetic": 32214, "Ġinsecure": 32215, "ĠCompet": 32216, "geries": 32217, "обод": 32218, "ł×ķ": 32219, "ðŁĴ": 32220, "Ġsenators": 32221, "ĠBrisbane": 32222, "ĠAlb": 32223, "uttering": 32224, "ĠAllow": 32225, "zero": 32226, "Ġpai": 32227, "ĠÐIJлекÑģ": 32228, "ĠDisplay": 32229, "ĠBlade": 32230, "ĠApps": 32231, "Ġpä": 32232, "ĠдеÑģÑı": 32233, "Ġquella": 32234, "ĠGao": 32235, "еннÑĭÑħ": 32236, "Ġspoilers": 32237, "Ġgallons": 32238, "ĠÙĦÙĬ": 32239, "ĠZion": 32240, "æľīä¸Ģ": 32241, "onie": 32242, "ragt": 32243, "ĠChand": 32244, "Ġë³ij": 32245, "Ġblunt": 32246, "Ġusu": 32247, "ĠKad": 32248, "rakt": 32249, "Ġcinematic": 32250, "Ġammunition": 32251, "rene": 32252, "Ġfourteen": 32253, "ĠCarn": 32254, "crit": 32255, "Ġtenure": 32256, "vu": 32257, "Ġprincipalmente": 32258, "Ġalleen": 32259, "éĢĻä¸Ģ": 32260, "Ġkomplett": 32261, "Ġdüny": 32262, "James": 32263, "Ġreceptor": 32264, "Ġoneself": 32265, "guru": 32266, "Ġmerchant": 32267, "liness": 32268, "Ġoverlooked": 32269, "Ġharmonic": 32270, "éķ¿": 32271, "ieso": 32272, "×ķ×ŀ": 32273, "colm": 32274, "ĠпÑĢоекÑĤ": 32275, "ĠAda": 32276, "اس": 32277, "Tim": 32278, "Ġrecurring": 32279, "Ġproceeds": 32280, "ĠParticularly": 32281, "ĠDownload": 32282, "etrical": 32283, "Ġmatrices": 32284, "Ġproyecto": 32285, "ancies": 32286, "ĠUhm": 32287, "Ġcaves": 32288, "Ġìĸ´ëł¤": 32289, "ĠLeaf": 32290, "ĠобÑĭÑĩ": 32291, "ĠìĿ´ìľł": 32292, "Europe": 32293, "ĠtÄħ": 32294, "Ġpuls": 32295, "Ġtakiego": 32296, "ÐĿе": 32297, "GU": 32298, "Ġfors": 32299, "Ïģγ": 32300, "Ġfotos": 32301, "Ġ))": 32302, "Ġ멤ë": 32303, "Ġaquilo": 32304, "ĠKurd": 32305, "ï¸ı": 32306, "ptic": 32307, "ĠDort": 32308, "Ġmisery": 32309, "auso": 32310, "åĬŁ": 32311, "chuckling": 32312, "ĠRidge": 32313, "ĠíĸĪìĬµëĭĪëĭ¤": 32314, "Ġ***": 32315, "客": 32316, "ĠHmmm": 32317, "Ġgeographic": 32318, "Ġanys": 32319, "Ġtalvez": 32320, "Ġskelet": 32321, "Ġsignatures": 32322, "Ġliters": 32323, "IJë©´": 32324, "ĠÑģвоего": 32325, "Ġskiing": 32326, "ĠÐľÐ¾Ñģ": 32327, "Ġadopting": 32328, "Ġhaft": 32329, "Ġsymmetric": 32330, "ĠLiqu": 32331, "Ġthyroid": 32332, "Ġmisin": 32333, "lude": 32334, "Ġhull": 32335, "ĠXD": 32336, "ĠGust": 32337, "zeich": 32338, "Ġvibrations": 32339, "Ġesemp": 32340, "ĠвÑģÑİ": 32341, "ĠQuem": 32342, "Ġübrig": 32343, "ĠSke": 32344, "ĠLynch": 32345, "rooms": 32346, "artet": 32347, "fest": 32348, "Ġfrüher": 32349, "Ġlure": 32350, "ä¸į好æĦıæĢĿ": 32351, "ĠìķĮìķĦ": 32352, "ĠWIN": 32353, "ĠRYAN": 32354, "ĠкоÑĤоÑĢÑĥÑİ": 32355, "ĠKash": 32356, "Ġ×Ķ×ŀ": 32357, "Ġsafeg": 32358, "ĠHallelujah": 32359, "ĠдвÑĥÑħ": 32360, "Ġstaple": 32361, "Ġsediment": 32362, "ĠActs": 32363, "Ġblaming": 32364, "Ġmainland": 32365, "Ġsporting": 32366, "Ġdecorations": 32367, "Ġexecuting": 32368, "Ġparan": 32369, "ĠDollar": 32370, "Ġprojections": 32371, "Ġcommissioned": 32372, "Ġbour": 32373, "öm": 32374, "Ġsteamed": 32375, "ĠëŃĺ": 32376, "Ġpetrol": 32377, "Ġcelular": 32378, "帶": 32379, "ĠHungary": 32380, "Ġrented": 32381, "ĠваÑĢи": 32382, "bbie": 32383, "Ġsécur": 32384, "üll": 32385, "Ġswings": 32386, "between": 32387, "ĠиÑĤ": 32388, "estro": 32389, "Ġniemand": 32390, "ĠìĤ¼": 32391, "ĠPardon": 32392, "esses": 32393, "ĠMID": 32394, "Ġcentralized": 32395, "ĠAlien": 32396, 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33328, "หà¸Ļ": 33329, "Ġappli": 33330, "ா?": 33331, "üyorum": 33332, "ĠинÑĤеÑĢеÑģно": 33333, "Ġdisinfect": 33334, ">-": 33335, "Ġchampagne": 33336, "Ġkla": 33337, "opers": 33338, "Trans": 33339, "ĠDesert": 33340, "Ġcultivate": 33341, "ĠFucking": 33342, "idelity": 33343, "ĠÑĤан": 33344, "Ġincub": 33345, "Ġtemu": 33346, "Ġlearner": 33347, "founder": 33348, "ĠSyl": 33349, "ãĤĢ": 33350, "Ġfato": 33351, "zier": 33352, "ĠìĹĨìĿ´": 33353, "ĠìĪ¨": 33354, "Ġpsycho": 33355, "ĠÑĤелеÑĦ": 33356, "Ġregarde": 33357, "Ġrepresentations": 33358, "Ġlitigation": 33359, "Ġspann": 33360, "ults": 33361, "bior": 33362, "è¦ĭãģ¦": 33363, "ä¸įå¤ļ": 33364, "ĠSurvey": 33365, "ĠLEDs": 33366, "Ġträ": 33367, "Ġlên": 33368, "Ġantioxid": 33369, "еÑĢом": 33370, "Ġinduction": 33371, "Ġfooled": 33372, "ätzlich": 33373, "ĠговоÑĢÑıÑĤ": 33374, "ĠFact": 33375, "umbai": 33376, "Ġwiggle": 33377, "NOUN": 33378, "Ġdévelopp": 33379, "ĠClaro": 33380, "Ġì¸": 33381, "ë¬": 33382, "ãģªãĤĵãģł": 33383, "Ġaccumulate": 33384, "Ġmaintains": 33385, "ëĦ": 33386, "ĠFighter": 33387, "íĨł": 33388, "Ġmatin": 33389, "Ġcoupon": 33390, "Ġstunt": 33391, "Ġdebuted": 33392, "å¾ħãģ£ãģ¦": 33393, "Ġprag": 33394, "иваем": 33395, "73": 33396, "Ġexpres": 33397, "Ġìĺ¤ë¹ł": 33398, "ĠпеÑĢÑģон": 33399, "Ġcalculus": 33400, "Ġabrupt": 33401, "ĠInspector": 33402, "ourt": 33403, "æĸĻ": 33404, "źniej": 33405, "intense": 33406, "Ba": 33407, "Ġlounge": 33408, "Ġasthma": 33409, "ĠHiç": 33410, "ª»": 33411, "Ġeditorial": 33412, "Ġseize": 33413, "Ġkır": 33414, "Ġmouve": 33415, "Ġtierra": 33416, "Ġtestosterone": 33417, "Ġrh": 33418, "ĠKingston": 33419, "ELLE": 33420, "ĠRepresentative": 33421, "Ġ1974": 33422, "Ġiba": 33423, "Ts": 33424, "Ġsorta": 33425, "Ġ(?)": 33426, "ĠتÙĪ": 33427, "ĠëĤ´ëł¤": 33428, "Ġbekommt": 33429, "Ġspiritually": 33430, "Ġdistorted": 33431, "Mad": 33432, "Ġreim": 33433, "ánh": 33434, "ĠOttoman": 33435, "ĠRelig": 33436, "ĠEls": 33437, "Ġretained": 33438, "ĠLaughs": 33439, "æĢ»": 33440, "ĠSAS": 33441, "ĠколиÑĩеÑģÑĤво": 33442, "×ķתר": 33443, "Ġinnovate": 33444, "Ġkork": 33445, "ĠÑĢаÑģÑģказÑĭв": 33446, "ondere": 33447, "ivi": 33448, "aye": 33449, "ounty": 33450, "ĠполÑĥÑĩаеÑĤÑģÑı": 33451, "Ġbuns": 33452, "åħ«": 33453, "Ġyüzden": 33454, "Ġsurgeries": 33455, "Ø£ÙĨ": 33456, "Ġbankruptcy": 33457, "welt": 33458, "Ġsiamo": 33459, "Ġdarkest": 33460, "ĠHann": 33461, "gga": 33462, "Ġformas": 33463, "ĠDj": 33464, "named": 33465, "Ġshields": 33466, "ueller": 33467, "ĠFew": 33468, "Ġlace": 33469, "Ġfurious": 33470, "ĠYU": 33471, "Ġsocietal": 33472, "Ġjudgement": 33473, "ĠDos": 33474, "Ġjab": 33475, "laws": 33476, "Ġreinvent": 33477, "ĠKatherine": 33478, "ĠChoi": 33479, "adows": 33480, "Ġrans": 33481, "oden": 33482, "ĠMidwest": 33483, "nın": 33484, "Ġdeport": 33485, "ĠDip": 33486, "ç´ħ": 33487, "Ġatención": 33488, "ĠCourtney": 33489, "ividad": 33490, "ĠÚ©Ûģ": 33491, "Ġefficacy": 33492, "ĠBrooks": 33493, "Ġreferral": 33494, "ĠконÑĨ": 33495, "Ġmalicious": 33496, "Ġkir": 33497, "ĠGoddess": 33498, "Ġfunky": 33499, "Ġinterim": 33500, "ĠKörper": 33501, "Ġìĸ¼ë§": 33502, "kur": 33503, "Ġкли": 33504, "Ġtrucs": 33505, "gesetz": 33506, "Ġzug": 33507, "ĠGlück": 33508, "ĠMinute": 33509, "Ġprestigious": 33510, "Ġniez": 33511, "Ġconcentrations": 33512, "лаÑģÑĤи": 33513, "ĠSis": 33514, "ĠVitamin": 33515, "kov": 33516, "ĠPBS": 33517, "Ġнее": 33518, "Ġretailers": 33519, "Ġconventions": 33520, "ĠSamantha": 33521, "Ġproudly": 33522, "Jordan": 33523, "ĠJASON": 33524, "atk": 33525, "Ġtriste": 33526, "Ġstär": 33527, "Ġreiterate": 33528, "Ġposterior": 33529, "Ġ1973": 33530, "ĠPine": 33531, "ĠJuliet": 33532, "Ġpedir": 33533, "kil": 33534, "Ġoverlapping": 33535, "Ġexclude": 33536, "Ġeconóm": 33537, "Ġaccepts": 33538, "ĠSter": 33539, "決": 33540, "Ġìļ´ëıĻ": 33541, "estab": 33542, "Ġtug": 33543, "arg": 33544, "Ġlivro": 33545, "اص": 33546, "Ġseams": 33547, "Ġburaya": 33548, "Ġello": 33549, "ĠTM": 33550, "ĠPaw": 33551, "ĠIndex": 33552, "Exc": 33553, "Ġinspirational": 33554, "Ġdunk": 33555, "è°ģ": 33556, "akter": 33557, "Ġconditioner": 33558, "ĠSalut": 33559, "ÅĤec": 33560, "Ġìī½": 33561, "ĠÑĥзна": 33562, "ĠRomeo": 33563, "fruit": 33564, "ĠYO": 33565, "Ġchá»ī": 33566, "бÑĥ": 33567, "bons": 33568, "Ġreproductive": 33569, "Ġorada": 33570, "Ġíļ¨": 33571, "Ġtentar": 33572, "Ġmañana": 33573, "ãĤ¬": 33574, "Ġsolvent": 33575, "Jessica": 33576, "ĠLegal": 33577, "Ġtua": 33578, "Ġsic": 33579, "ĠEQ": 33580, "aukee": 33581, "ìĭľëĭ¤": 33582, "ĠÅŀu": 33583, "Ġadhere": 33584, "ĠTul": 33585, "Ġà®Ĩ": 33586, "Ġtextbooks": 33587, "ĠFifth": 33588, "Ġexperi": 33589, "Ġchic": 33590, "Ġheap": 33591, "inely": 33592, "atra": 33593, "Two": 33594, "Ġhelemaal": 33595, "Ġfren": 33596, "æݨ": 33597, "Ġbisher": 33598, "اش": 33599, "ĠìĦłìĥĿ": 33600, "ĠTages": 33601, "Ġsá»±": 33602, "Ġbullied": 33603, "ؤ": 33604, "Ġbenefited": 33605, "ĠPreviously": 33606, "ĠÑįÑĦÑĦ": 33607, "Ùį": 33608, "Ġsenate": 33609, "ĠMorm": 33610, "ijke": 33611, "ĠFlu": 33612, "Ġincorporating": 33613, "jack": 33614, "ĠпиÑĤ": 33615, "Ġimply": 33616, "Ġhacks": 33617, "ĠRICH": 33618, "ĠкваÑĢ": 33619, "ĠпÑĢекÑĢаÑģ": 33620, "Ġdependency": 33621, "Ġìļ©": 33622, "Ġì±ħ": 33623, "Ġwährend": 33624, "Ġsulla": 33625, "ĠPittsburgh": 33626, "Ġesempio": 33627, "¼ë¡ľ": 33628, "prot": 33629, "ĠRosen": 33630, "ĠIndependence": 33631, "Ġparsley": 33632, "iegen": 33633, "Ġhaw": 33634, "Ġaquell": 33635, "ĠCAP": 33636, "ĠÑĢабоÑĤаÑĤÑĮ": 33637, "ĠCliff": 33638, "ionar": 33639, "Ġsecuring": 33640, "æĪijåĢijçļĦ": 33641, "νε": 33642, "Ġutilis": 33643, "Ġcoule": 33644, "ĠPing": 33645, "Ġtrek": 33646, "Ġfak": 33647, "Ġenorme": 33648, "Ġìĭ«": 33649, "让": 33650, "Ġdoubling": 33651, "ĠнÑĢавиÑĤÑģÑı": 33652, "Ġhed": 33653, "hoven": 33654, "ĠStanding": 33655, "ĠmÃŃn": 33656, "ĠJimin": 33657, "Ġmonarch": 33658, "Ġcoke": 33659, "Ġmr": 33660, "Ġclic": 33661, "Ãį": 33662, "Ġimpeachment": 33663, "Ġdurability": 33664, "Ġvarios": 33665, "Ġcommercials": 33666, "Ġgreetings": 33667, "ĠRi": 33668, "ĠAppreci": 33669, "ìŀĪëĬĶ": 33670, "Ġrésult": 33671, "ért": 33672, "Ġsalute": 33673, "Ġpoderia": 33674, "Ġsunrise": 33675, "veck": 33676, "Ġreluctant": 33677, "Ġcommissioner": 33678, "念": 33679, "âte": 33680, "ĠKenny": 33681, "ĠSiri": 33682, "ãĥĥãĥĹ": 33683, "ĠëĬĺ": 33684, "ĠEE": 33685, "Ġunch": 33686, "кон": 33687, "ĠاÙĦØ¥": 33688, "Ġbelts": 33689, "Ġhass": 33690, "ĠмоÑı": 33691, "Ġdisplaced": 33692, "Ġabra": 33693, "ÎŃλ": 33694, "Ġscratches": 33695, "Ġcomet": 33696, "Ġauthorization": 33697, "ĠLLC": 33698, "Ġproduk": 33699, "Ġrehabilitation": 33700, "åŀ": 33701, "ÑĸÑĩ": 33702, "uding": 33703, "olit": 33704, "Ġ105": 33705, "Ġexpands": 33706, "Ġaltri": 33707, "ĠKomment": 33708, "Ġanf": 33709, "Pl": 33710, "ĠMana": 33711, "fed": 33712, "Ġbri": 33713, "Ġora": 33714, "Gs": 33715, "ĠGur": 33716, "uckland": 33717, "Ġjunction": 33718, "Ġironic": 33719, "ĠFeed": 33720, "Ġprakt": 33721, "ĠHammer": 33722, "ĮëıĦ": 33723, "ĠTracy": 33724, "çµ±": 33725, "ĠAside": 33726, "него": 33727, "ĠиÑģполÑĮзоваÑĤÑĮ": 33728, "Ġzaj": 33729, "Ġequitable": 33730, "Ġcurb": 33731, "ĠãģĵãĤĮ": 33732, "Ġderivatives": 33733, "Ġpuppies": 33734, "ĠKenneth": 33735, "ĠCompl": 33736, "igram": 33737, "ĠGarcia": 33738, ")\"": 33739, "ĠHarbor": 33740, "estial": 33741, "Ġä¾Ĩ": 33742, "Ġers": 33743, "æ¹": 33744, "Ġunwanted": 33745, "Ġbelang": 33746, "аго": 33747, "emb": 33748, "dos": 33749, "ĠìĻľë": 33750, "ĠBudget": 33751, "Ġbattling": 33752, "ØŃت": 33753, "kok": 33754, "наÑĩала": 33755, "Ġplag": 33756, "Ġcantidad": 33757, "Ġgrupos": 33758, "Ġplugins": 33759, "lerini": 33760, "ĠимееÑĤ": 33761, "Ġsozusagen": 33762, "olics": 33763, "Ġpueblo": 33764, "Ġreminis": 33765, "rän": 33766, "ĠMorrison": 33767, "Ġlinha": 33768, "Ġbreaths": 33769, "ĠTaste": 33770, "Ġenfrent": 33771, "ĠDocker": 33772, "Ġден": 33773, "Ġethnicity": 33774, "Ġwob": 33775, "Ġsuffers": 33776, "Ġtransitioning": 33777, "ĠRange": 33778, "ÄĻdzy": 33779, "ĠкаÑĤ": 33780, "Ġsyner": 33781, "Ġdonut": 33782, "Ġprobabilities": 33783, "ĠOmar": 33784, "Which": 33785, "uish": 33786, "isin": 33787, "Ġdemos": 33788, "ĠìłĢ기": 33789, "Ġëĺijê°Ļ": 33790, "Ġедин": 33791, "Ġcerve": 33792, "Ġjoka": 33793, "IAN": 33794, "Ġkilometer": 33795, "Ġhorizontally": 33796, "ĠBhag": 33797, "Ġ->": 33798, "ĠMonitor": 33799, "Ġknowledgeable": 33800, "Ġfav": 33801, "Ġpinned": 33802, "ĠeBay": 33803, "icker": 33804, "Ġìŀłê¹IJë§Į": 33805, "ĠXiaomi": 33806, "Ġcapit": 33807, "Ġnp": 33808, "Ġ1965": 33809, "hoe": 33810, "Ġnok": 33811, "ĠSage": 33812, "ĠнелÑĮзÑı": 33813, "ĠTow": 33814, "gam": 33815, "Ġdicen": 33816, "ĠSUBSCRIBE": 33817, "Ġreboot": 33818, "Ġpaj": 33819, "Ġë³´ìŬë": 33820, "Ġthicken": 33821, "ĠReality": 33822, "idän": 33823, "Na": 33824, "Ġê²ĥìĿĢ": 33825, "!!)": 33826, "Ġroutines": 33827, "Ġодного": 33828, "Ġexting": 33829, "Ġì¦Ŀ": 33830, "Ġsulfur": 33831, "Ġcarve": 33832, "Ġasteroid": 33833, "ĠWarrior": 33834, "Ġphotographers": 33835, "Ġpell": 33836, "Ġcrossover": 33837, "æĪijçŁ¥éģĵ": 33838, "Ġhacemos": 33839, "ĠNej": 33840, "Ġsettling": 33841, "Ġirm": 33842, "ĠBooks": 33843, "ientôt": 33844, "Ġespacio": 33845, "ĠScholars": 33846, "Ġdoomed": 33847, "ĠIRS": 33848, "wohl": 33849, "Ġsegue": 33850, "ĠëĪĦê°Ģ": 33851, "Ġpratic": 33852, "BT": 33853, "ĠConsidering": 33854, "ĠBuffalo": 33855, "Ġtrainings": 33856, "Ġgebru": 33857, "ĠGleich": 33858, "Ġpirates": 33859, "Ġenvelop": 33860, "Ġreopen": 33861, "imat": 33862, "Ġtee": 33863, "Ġsued": 33864, "feh": 33865, "Ġ×Ķק": 33866, "Ġdiets": 33867, "Ġjuntos": 33868, "asto": 33869, "Ġmisunderstood": 33870, "Ġruim": 33871, "Ġclassify": 33872, "ĠпÑĢодÑĥк": 33873, "Ġinse": 33874, "Ġillustrated": 33875, "Ġcorrosion": 33876, "Ġaccred": 33877, "ĠAuntie": 33878, "ĠпÑĢивеÑĤ": 33879, "ĠLIVE": 33880, "Ġrek": 33881, "Ġreceipt": 33882, "åĪ°åºķ": 33883, "ĠBarbie": 33884, "ĠSnake": 33885, "turn": 33886, "Jeff": 33887, "ãģĬãģĬ": 33888, "ķĦ": 33889, "VOICEOVER": 33890, "coll": 33891, "Ġrunners": 33892, "ìłľë": 33893, "osos": 33894, "moon": 33895, "Ġkeynote": 33896, "ĠInstit": 33897, "SPEAK": 33898, "Ġplugs": 33899, "Ġcurv": 33900, "ĠYuri": 33901, "ĠTheres": 33902, "ĠPs": 33903, "ĠμÏĢο": 33904, "Ġconverter": 33905, "Ġrefine": 33906, "Ġbadass": 33907, "Ġοι": 33908, "Ġregen": 33909, "azzi": 33910, "ÙĬÙģ": 33911, "Ġseized": 33912, "Ġiçer": 33913, "ilee": 33914, "Ġupstream": 33915, "Ġbuds": 33916, "Ġpim": 33917, "Ġíķĺ루": 33918, "Ġalluded": 33919, "Ġthemed": 33920, "Ġconsisting": 33921, "Ġbons": 33922, "unuz": 33923, "ĠпÑĢовод": 33924, "ĠLovely": 33925, "à¥ĭ": 33926, "Ġparach": 33927, "ĠStaats": 33928, "éļĬ": 33929, "Ġselective": 33930, "Ġfase": 33931, "ĠGeorget": 33932, "Ġcocaine": 33933, "Ġreproduction": 33934, "ĠLara": 33935, "ĠLD": 33936, "Ġgh": 33937, "Jon": 33938, "ĠlÃ¥": 33939, "ĠëijIJë": 33940, "Ġtyped": 33941, "ĠBana": 33942, "ëĵľë": 33943, "Ġsavory": 33944, "ĠZomb": 33945, "standen": 33946, "Ġpedestrian": 33947, "Ġdifférents": 33948, "Ġìĭ¸": 33949, "èī¯": 33950, "Ġcomplained": 33951, "ç¦ı": 33952, "ĠÐļÑĤо": 33953, "Ġ׾פ": 33954, "aliÅĽmy": 33955, "Ġmortar": 33956, "Ġverdict": 33957, "Ġsuficiente": 33958, "ĠMillion": 33959, "mittel": 33960, "inals": 33961, "ĠاÙĦØ®": 33962, "аÑİÑģÑĮ": 33963, "ĠmiÄĻdzy": 33964, "ĠOle": 33965, "Ġinvert": 33966, "czyÄĩ": 33967, "озможно": 33968, "starter": 33969, "Ġauditor": 33970, "ĠScout": 33971, "chien": 33972, "ĠSverige": 33973, "uffled": 33974, "Ġzehn": 33975, "ĠAuckland": 33976, "Ġargent": 33977, "Ġ1976": 33978, "ĠHoe": 33979, "Ġbothers": 33980, "Ġsocialist": 33981, "Ġpliers": 33982, "Ġemergen": 33983, "ĠXP": 33984, "еÑĢов": 33985, "More": 33986, "ĠLevi": 33987, "ĠAnders": 33988, "ibilidad": 33989, "ĠParents": 33990, "Ġinduced": 33991, "ìĸ´ì¤": 33992, "Ġbalances": 33993, "ĠвÑĭÑĪ": 33994, "Ġsubmarine": 33995, "Start": 33996, "Ġdries": 33997, "Ġvolver": 33998, "Ġticking": 33999, "cott": 34000, "Ġfaj": 34001, "prés": 34002, "ĠSabb": 34003, "ĠзаÑĩ": 34004, "ĠпокÑĥп": 34005, "Ġbaptized": 34006, "ĠBrilliant": 34007, "ĠÐijог": 34008, "Ġmots": 34009, "bits": 34010, "Ġlattice": 34011, "æĪijè·Łä½ł": 34012, "Ġcoriander": 34013, "Ġresidency": 34014, "ync": 34015, "Ġpierwszy": 34016, "ĠKnock": 34017, "ĠZap": 34018, "ĠÐķв": 34019, "견": 34020, "å°ıå¿ĥ": 34021, "Ġuneven": 34022, "ĠJas": 34023, "odor": 34024, "ç¿Ĵ": 34025, "74": 34026, "ĠSite": 34027, "Ġaconteceu": 34028, "ympt": 34029, "Ġtrilogy": 34030, "Ġlantern": 34031, "ĠZucker": 34032, "vari": 34033, "welling": 34034, "ĠPotato": 34035, "gomery": 34036, "Ġreacted": 34037, "ĠChron": 34038, "Ġjede": 34039, "beeld": 34040, "Ġtwent": 34041, "Ġlact": 34042, "æ¨Ĥ": 34043, "Ġrése": 34044, "Ġrelent": 34045, "Ġfurnace": 34046, "Ġwidget": 34047, "Ġearthquakes": 34048, "ĠAdjust": 34049, "ilit": 34050, "ĠØ£ÙĪ": 34051, "Ġhearings": 34052, "Ġdefendant": 34053, "irsiniz": 34054, "Ġbask": 34055, "cja": 34056, "ľ¨": 34057, "Ġrifles": 34058, "Ġinstal": 34059, "ĠForgive": 34060, "pical": 34061, "ĠÐŀÑĩенÑĮ": 34062, "Ġpetites": 34063, "Ġhp": 34064, "Ġrenowned": 34065, "ĠInn": 34066, "Ġ주ìĦ¸ìļĶ": 34067, "Ġemphasized": 34068, "éĹ®é¢ĺ": 34069, "ĠìŀĪì£ł": 34070, "Ġê²ĥìľ¼ë¡ľ": 34071, "ãĤĨ": 34072, "Åĵ": 34073, "gili": 34074, "Dave": 34075, "Ġexhausting": 34076, "ÅĤug": 34077, "Ġschema": 34078, "μά": 34079, "cycl": 34080, "Ġautant": 34081, "Ġparcel": 34082, "Ġmateria": 34083, "ĠBerry": 34084, "ĠÑģами": 34085, "Ġextracted": 34086, "ĠSaying": 34087, "ismatic": 34088, "ĠпопÑĢоб": 34089, "Ġneuron": 34090, "graph": 34091, "ľë©´": 34092, "Ġenclosure": 34093, "ĠJohann": 34094, "Ġaftermath": 34095, "ÑĤоб": 34096, "Ġuży": 34097, "Ġsamp": 34098, "360": 34099, "ĠMei": 34100, "Ġtaco": 34101, "Ġreceptors": 34102, "Ġpunches": 34103, "ĠHoje": 34104, "ĠÙĩÙĨا": 34105, "=\"#": 34106, "ĠAngular": 34107, "Ġmusique": 34108, "Ġrol": 34109, "Ġñ": 34110, "sterreich": 34111, "Ġclam": 34112, "ĠTreasury": 34113, "chemical": 34114, "Ġapar": 34115, "Ġappend": 34116, "Ġforbid": 34117, "ĠHamburg": 34118, "аков": 34119, "Ġê¸Ī": 34120, "ilda": 34121, "Ġpreparations": 34122, "ĠmogÄħ": 34123, "Ġcamino": 34124, "Eric": 34125, "ĠBlind": 34126, "èĪĩ": 34127, "å¹´çļĦ": 34128, "ĠDiscovery": 34129, "ì¸ł": 34130, "çĪ¶": 34131, "Ġinterpreter": 34132, "Ġbred": 34133, "ĠPsalm": 34134, "Ġdefended": 34135, "ìī¬": 34136, "ĠErfahr": 34137, "ĠPeach": 34138, "Ġmoons": 34139, "ĠOst": 34140, "Ġspécial": 34141, "Ġarriver": 34142, "ĠWis": 34143, "uci": 34144, "Ġrobotics": 34145, "IVE": 34146, "Ġsiege": 34147, "arla": 34148, "Ġseparates": 34149, "ĠTC": 34150, "íı°": 34151, "quisite": 34152, "Ġparentheses": 34153, "ике": 34154, "ç«Ļ": 34155, "Ġtrous": 34156, "建": 34157, "ĠÑģилÑĮ": 34158, "Ġbeers": 34159, "ĠплаÑĤ": 34160, "ãģĻãģĶãģĦ": 34161, "Ġsola": 34162, "Ġdès": 34163, "mingham": 34164, "ikte": 34165, "Ġoops": 34166, "Ġtwitch": 34167, "å°ĩ": 34168, "ÏĪ": 34169, "ĠShouldn": 34170, "uvre": 34171, "Ġleer": 34172, "criptions": 34173, "Ġeyeshadow": 34174, "ĠGuo": 34175, "ĠPowell": 34176, "Ġsupuesto": 34177, "Ġana": 34178, "rals": 34179, "ĠMontreal": 34180, "Ġsurfing": 34181, "ĠÐŁÐµÑĢв": 34182, "×ŀ×ķ": 34183, "Ġmilliseconds": 34184, "Ġsuburbs": 34185, "Ġplaneta": 34186, "ÑĥÑĪка": 34187, "hrlich": 34188, "ĠHY": 34189, "ĠسÛĴ": 34190, "ĠMM": 34191, "ĠEff": 34192, "åı¯æĦĽ": 34193, "ĠHS": 34194, "anson": 34195, "Ġì§ģìłij": 34196, "Ġsuo": 34197, "Ġdeploying": 34198, "Ġkunt": 34199, "tering": 34200, "Ġerect": 34201, "ìŀ¥ìĿ´": 34202, "ĠìĿĮìĭĿ": 34203, "Ġspecimen": 34204, "!...": 34205, "æĪij說": 34206, "Ġligne": 34207, "Ġkonst": 34208, "adequ": 34209, "Ġìĥģíĥľ": 34210, "Ġaccessed": 34211, "ĠPole": 34212, "kill": 34213, "Ġë²Ħë": 34214, "Ġauthenticity": 34215, "Ġappelle": 34216, "ulle": 34217, "Ġrevision": 34218, "Ġgoats": 34219, "гли": 34220, "Ġpau": 34221, "ĠRanger": 34222, "ĠImag": 34223, "author": 34224, "Ġeve": 34225, "ĠMessenger": 34226, "Ġnay": 34227, "Ġwholes": 34228, "ätte": 34229, "Ġonwards": 34230, "ĠDepois": 34231, "ĠíijľíĺĦ": 34232, "ĠSARS": 34233, "Ġwszystkich": 34234, "Ġdestru": 34235, "umbing": 34236, "Ġcompatibility": 34237, "Ġmisinformation": 34238, "odore": 34239, "ĠFavor": 34240, "eko": 34241, "ıĮ": 34242, "waukee": 34243, "ĠTeaching": 34244, "ĠKO": 34245, "Ġbetting": 34246, "Ġquests": 34247, "Ġvivre": 34248, "ĠмÑĥзÑĭ": 34249, "Ġsaga": 34250, "Ġswell": 34251, "Ġgehe": 34252, "æĢİ麼樣": 34253, "ĠоÑĢганиз": 34254, "Ġgide": 34255, "ĠGross": 34256, "Ġdalej": 34257, "Ġclaws": 34258, "á»Ļc": 34259, "Ġprejudice": 34260, "Ġinsign": 34261, "ihood": 34262, "Ġpled": 34263, "Ġdónde": 34264, "ĠPolitical": 34265, "Ġpremises": 34266, "undert": 34267, "عت": 34268, "onnen": 34269, "Ġespaço": 34270, "Ġfé": 34271, "ĠHarrison": 34272, "ĠCensus": 34273, "Ġcardio": 34274, "Ġdiy": 34275, "Ġmilieu": 34276, "Ġjournée": 34277, "ĠRelease": 34278, "NIE": 34279, "ĠMuk": 34280, "idée": 34281, "á»įi": 34282, "Ġiçinde": 34283, "ŀĻ": 34284, "Ġresonate": 34285, "Ġmoles": 34286, "ĠFlying": 34287, "ĠGloria": 34288, "ĠPastor": 34289, "ĠArena": 34290, "好ä¸į好": 34291, "NON": 34292, "олов": 34293, "ĠallÃŃ": 34294, "omat": 34295, "ìĸ´ëıĦ": 34296, "ĠcaracterÃŃst": 34297, "Ġdeclining": 34298, "ÑĸÑı": 34299, "anco": 34300, "ĠInform": 34301, "Ġbargain": 34302, "Ġbushes": 34303, "ĠNaturally": 34304, "Ġrechts": 34305, "ĠTensor": 34306, "ĠPatricia": 34307, "Ġprincipio": 34308, "ĠMumbai": 34309, "Ġwomb": 34310, "Ġnostra": 34311, "Ġdilemma": 34312, "Ġirgendwann": 34313, "Ġ1964": 34314, "ĠenergÃŃa": 34315, "ĠнаÑĢ": 34316, "Ġsegregation": 34317, "ĠAthlet": 34318, "Ġ»,": 34319, "Ġyeni": 34320, "ĠSeit": 34321, "Ġvenom": 34322, "Ġdakika": 34323, "ĠëıĮë": 34324, "ĠÃīl": 34325, "Ġfus": 34326, "ĠMog": 34327, "¦½ëĭĪëĭ¤": 34328, "Ġremar": 34329, "ĠTeddy": 34330, "Ġbreasts": 34331, "icans": 34332, "æĶ¶çľĭ": 34333, "kap": 34334, "ĠhÆ¡n": 34335, "ĠJP": 34336, "ãĥ³ãĤ¿": 34337, "Ġresurrect": 34338, "ĠìĿ¸ë": 34339, "herical": 34340, "Ġfotograf": 34341, "ĠJosé": 34342, "Ġlivelihood": 34343, "Ġbibli": 34344, "teri": 34345, "Ġvorstellen": 34346, "ĠAAA": 34347, "Ġassessing": 34348, "YA": 34349, "Ġsplend": 34350, "Ġexcav": 34351, "Ġbaptism": 34352, "yll": 34353, "wow": 34354, "Mac": 34355, "Ġplastics": 34356, "teokbokki": 34357, "Ġintéressant": 34358, "Ġcommanded": 34359, "Ġfamously": 34360, "ĠÐĺли": 34361, "ĠManuel": 34362, "Ġsouthwest": 34363, "Ġdeformation": 34364, "ÃŃculo": 34365, "ĠнаÑħодиÑĤÑģÑı": 34366, "ĠPatter": 34367, "degree": 34368, "ĠczÄĻsto": 34369, "\"-": 34370, "Ġìħĭ": 34371, "Ġmanger": 34372, "ĠTrustee": 34373, "Ģ리": 34374, "Ġpuntos": 34375, "ivable": 34376, "Ġvolatile": 34377, "ĠëĬIJ": 34378, "Ġinstability": 34379, "Ġciel": 34380, "ciÄħ": 34381, "Ġpurity": 34382, "ноÑģÑĤ": 34383, "Sil": 34384, "edar": 34385, "åĻ¨": 34386, "NOUNCER": 34387, "Ġspelled": 34388, "GER": 34389, "Ġsanctuary": 34390, "Ġaccelerating": 34391, "Ġscout": 34392, "ĠпÑĢев": 34393, "fahren": 34394, "ãģĵãģ¡ãĤī": 34395, "ĠëĤĺìĺ¨": 34396, "ĠpoczÄħt": 34397, "ĠMeu": 34398, "kaar": 34399, "³´ê³ł": 34400, "akra": 34401, "Down": 34402, "ĠÃĦr": 34403, "ĠElite": 34404, "Ġallons": 34405, "Ġmayonnaise": 34406, "ĠSustain": 34407, "prisingly": 34408, "Ġsupervis": 34409, "Ġê·¸ëłĩì£ł": 34410, "Ġunemployed": 34411, "Ġfreshly": 34412, "Ġ×ŀ×¢": 34413, "ĠDh": 34414, "Ġtackling": 34415, "Ġogr": 34416, "Ġì´Īë": 34417, "ãĤĪãĤį": 34418, "Ġloft": 34419, "arah": 34420, "ĠAirl": 34421, "ĠDir": 34422, "ĠÐľÐ¾Ð¶Ð½Ð¾": 34423, "Ġbooking": 34424, "ĠCRA": 34425, "Ġhttps": 34426, "Ġchoke": 34427, "Ġgown": 34428, "Ġnoite": 34429, "Ġzac": 34430, "istol": 34431, "Ġsecre": 34432, "Ġresembles": 34433, "Ġcuad": 34434, "ìĤ¬ê°Ģ": 34435, "show": 34436, "Ġblanc": 34437, "Ġagu": 34438, "ĠPrint": 34439, "asted": 34440, "ĠWeather": 34441, "ipl": 34442, "Ġobscure": 34443, "Ġconte": 34444, "oughs": 34445, ");": 34446, "ĠDame": 34447, "ä¸Ģ缴": 34448, "Ġclarification": 34449, "Ġintimacy": 34450, "Ġuphold": 34451, "ĠMirror": 34452, "Ġwagon": 34453, "xide": 34454, "Ġclog": 34455, "apper": 34456, "ĠImmediately": 34457, "úde": 34458, "Ġtouchdown": 34459, "Ġrooft": 34460, "аÑĪа": 34461, "Ġçıkt": 34462, "Ġlaisser": 34463, "ĠUnreal": 34464, "ensitive": 34465, "Ġ123": 34466, "Ġplaster": 34467, "Ġducks": 34468, "Ġetme": 34469, "Ġbishop": 34470, "brevi": 34471, "Ġbic": 34472, "ä¸ĭåİ»": 34473, "Ġruntime": 34474, "Ġambitions": 34475, "маÑĤ": 34476, "ĠWein": 34477, "ĠMari": 34478, "ĠíĬ¸ë": 34479, "Ġresolver": 34480, "ĠngÃły": 34481, "ĠRise": 34482, "ãĤĪãģĨãģ«": 34483, "ĠCrus": 34484, "Ġmerchandise": 34485, "Ġeli": 34486, "Ġstatewide": 34487, "Ġowl": 34488, "éģł": 34489, "æĶ¹": 34490, "Ġtwisting": 34491, "Ġcontaminated": 34492, "ĠCommerce": 34493, "hythm": 34494, "ĠÃĪ": 34495, "Ġìĭ¤ë": 34496, "Ġmusste": 34497, "uir": 34498, "Ġsums": 34499, "ĠSomewhere": 34500, "ãĥİ": 34501, "Ġkami": 34502, "Ġaired": 34503, "ĠANDREW": 34504, "Ġêº": 34505, "Ġviendo": 34506, "Ġantibody": 34507, "Ġabsolument": 34508, "Ġprotesters": 34509, "ĠQuébec": 34510, "stadt": 34511, "Shaun": 34512, "Ġchambers": 34513, "ĠWear": 34514, "ĠEffects": 34515, "Ġhazards": 34516, "Ġnei": 34517, "Ġcorazón": 34518, "Ġá¼": 34519, "ĠSG": 34520, "Ķ©": 34521, "ĠìĹŃìĭľ": 34522, "Ġcomfy": 34523, "ĠCody": 34524, "Ġpensando": 34525, "Ġganska": 34526, "ĠAcross": 34527, "öllig": 34528, "abyte": 34529, "Ġwedge": 34530, "Ġkalian": 34531, "Ġsigue": 34532, "endes": 34533, "ĠGroÃŁ": 34534, "Ġutiliser": 34535, "Ġflown": 34536, "аниÑİ": 34537, "Ġlevar": 34538, "restrial": 34539, "Ġillustrations": 34540, "Ġaslında": 34541, "BLEEP": 34542, "ĠдоÑģÑĤ": 34543, "Ġturret": 34544, "Ġsuitcase": 34545, "ziÄĻki": 34546, "Ġsketches": 34547, "Ġacred": 34548, "ĠRei": 34549, "Ġtsun": 34550, "ĠSag": 34551, "Ġthirds": 34552, "ĠKIRBY": 34553, "rai": 34554, "Ġhumanos": 34555, "Ġrecommends": 34556, "Ġextraordinarily": 34557, "Ġcommencement": 34558, "KN": 34559, "opez": 34560, "Ġ×ijש": 34561, "Ġlethal": 34562, "ĠEstamos": 34563, "Ġinspector": 34564, "ĠSeok": 34565, "eun": 34566, "Ġoffshore": 34567, "Ġgettin": 34568, "years": 34569, "ĠSilence": 34570, "ĠNatur": 34571, "upun": 34572, "Ġtrzy": 34573, "Ġnoget": 34574, "Ġhamburger": 34575, "ĠPraise": 34576, "énd": 34577, "Ġ1971": 34578, "ylie": 34579, "krit": 34580, "ĠìĥĿê°ģìĿ´": 34581, "çļ®": 34582, "Ġmomentos": 34583, "Ġesté": 34584, "Ġdissemin": 34585, "Ġgigs": 34586, "Ġdesaf": 34587, "Ġavis": 34588, "ĠZoo": 34589, "ĠìķĬìĿĢ": 34590, "häng": 34591, "åı¥": 34592, "hake": 34593, "ĠBism": 34594, "Ġrethink": 34595, "ĠMalcolm": 34596, "Ġidentifies": 34597, "lower": 34598, "ixel": 34599, "ĠtvÃ¥": 34600, "ked": 34601, "ierz": 34602, "Ġöffentlich": 34603, "Ġproclaim": 34604, "soon": 34605, "lol": 34606, "Ġloi": 34607, "Ġbitten": 34608, "rollo": 34609, "Ġsermon": 34610, "Ġesqu": 34611, "Ġjackets": 34612, "Ġgráfic": 34613, "ĠпоказÑĭв": 34614, "Ġcabeza": 34615, "chodzi": 34616, "Ġpelvis": 34617, "Ġnostalgia": 34618, "Ġbrew": 34619, "Ġshortcuts": 34620, "ĠAdemás": 34621, "Ġsuperficial": 34622, "åħ©åĢĭ": 34623, "Ġboca": 34624, "ĠæĪijæĺ¯": 34625, "imentos": 34626, "åĽłä¸º": 34627, "Ġsprouts": 34628, "é£Ľ": 34629, "ĠJonas": 34630, "ĠFlorence": 34631, "static": 34632, "daughter": 34633, "*)": 34634, "ÅĤby": 34635, "fashion": 34636, "ĠGinger": 34637, "Ġ매ë": 34638, "Ġhustle": 34639, "utos": 34640, "ĠÑĤÑıж": 34641, "ĠLös": 34642, "ש×Ļ×Ŀ": 34643, "anych": 34644, "tuber": 34645, "Ġtidy": 34646, "Ġfrontal": 34647, "Ġwhiskey": 34648, "Ġhumid": 34649, "ĠÎŁ": 34650, "Ġridge": 34651, "Ġmarin": 34652, "Ġbientôt": 34653, "ĠCarrie": 34654, 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34712, "ĠExtremadura": 34713, "Ġdemonstrations": 34714, "ĠMontgomery": 34715, "íķ´ìĦ¤": 34716, "ĠGandhi": 34717, "ãĥĿ": 34718, "ç½®": 34719, "Ġreunion": 34720, "ĠjakiÅĽ": 34721, "ĠZug": 34722, "OUGH": 34723, "lifting": 34724, "Ġà²": 34725, "á¹Ľá¹£": 34726, "eb": 34727, "ĠWOW": 34728, "ĠShiva": 34729, "ometry": 34730, "Ġwildly": 34731, "Ġtended": 34732, "Ġmegap": 34733, "ì²ĺ": 34734, "Ġnause": 34735, "Ġgerek": 34736, "ãĥĭ": 34737, "ĠMarcel": 34738, "Ġneste": 34739, "خر": 34740, "Ġfeh": 34741, "åĨħ": 34742, "suspenseful": 34743, "ĠWrestle": 34744, "ĠPalestinians": 34745, "ĠGORD": 34746, "iyet": 34747, "ĠÑĢади": 34748, "Ġversuchen": 34749, "Ġtransistor": 34750, "ĠÐŁÑĢоÑģÑĤо": 34751, "ĠпонÑĢав": 34752, "Ġrhyme": 34753, "ĠVermont": 34754, "platz": 34755, "è®°": 34756, "ĠÄ°ÅŁte": 34757, "ĠHag": 34758, "ĠÐĺм": 34759, "ĠÑĢаÑģÑģказ": 34760, "Ġmetros": 34761, "ĠInfinity": 34762, "wolf": 34763, "ibal": 34764, "ftig": 34765, "ĠÚĨ": 34766, "Ġíĺ¹ìĭľ": 34767, "Ġoggi": 34768, "Ġdisposit": 34769, 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"ĠjÄħ": 35692, "éĤ£ä¹Ī": 35693, "Ġrespecto": 35694, "Ġcomunidad": 35695, "Ġcomigo": 35696, "agna": 35697, "Ġintrinsic": 35698, "ĠAlumni": 35699, "Ġsesleri": 35700, "Ġestimation": 35701, "âĢĶâĢĶ": 35702, "Ġproduit": 35703, "ãĢĤãĢį": 35704, "ĠвÑĢ": 35705, "Ġwhirl": 35706, "Ġacces": 35707, "çu": 35708, "Ġvariability": 35709, "Ġvodka": 35710, "itsu": 35711, "Ġinternships": 35712, "Ġallocate": 35713, "RR": 35714, "íĽĪ": 35715, "Ġinstructional": 35716, "tant": 35717, "Ġà®ħத": 35718, "Ġinvites": 35719, "Ġhak": 35720, "Ġscares": 35721, "Ġeclipse": 35722, "пов": 35723, "колÑĮ": 35724, "ativas": 35725, "Ġstabbed": 35726, "ĠDOM": 35727, "ä¸įåĪ°": 35728, "roots": 35729, "ĠPicture": 35730, "íĺ¼": 35731, "ĠCHA": 35732, "iec": 35733, "ıı": 35734, "hanol": 35735, "Ġmisunderstand": 35736, "Ray": 35737, "Ġroadmap": 35738, "ocumented": 35739, "izione": 35740, "ĠOlive": 35741, "rift": 35742, "Ġ×Ķ׳": 35743, "æ¯į": 35744, "lest": 35745, ";;": 35746, "ĠEA": 35747, "éľĢè¦ģ": 35748, "одÑĥ": 35749, "Ġhobbies": 35750, "Ġburial": 35751, "ãģ«ãģ¡ãģ¯": 35752, "Ф": 35753, "lege": 35754, "ĠHJ": 35755, "Ġobjection": 35756, "ĠãģŃ": 35757, "ctory": 35758, "Ġincremental": 35759, "Ġgymn": 35760, "Ġepidemi": 35761, "ÑģÑĭл": 35762, "Ãij": 35763, "Ġadvancement": 35764, "Ġparch": 35765, "News": 35766, "Ġayr": 35767, "лам": 35768, "Ġ׾ש": 35769, "Ġdiploma": 35770, "ãģ¡ãĤĥãĤĵ": 35771, "Ġrobbed": 35772, "Only": 35773, "Ġincur": 35774, "Ġchanting": 35775, "Ġíķ´ëıĦ": 35776, "Ġriches": 35777, "ĠCarmen": 35778, "Ġnostro": 35779, "λÎŃ": 35780, "ĠPowder": 35781, "à¹Ģห": 35782, "ĠìŀĪìľ¼ë©´": 35783, "Ġgerçekten": 35784, "ĠPikachu": 35785, "емон": 35786, "OLL": 35787, "Ġplanetary": 35788, "Ġslows": 35789, "Ġclockwise": 35790, "alion": 35791, "ĠìĮ": 35792, "Ġvern": 35793, "Ġhomme": 35794, "Ġendpoint": 35795, "Ġinnocence": 35796, "Ġelementos": 35797, "Ġsophomore": 35798, "Ġnotions": 35799, "ĠCouldn": 35800, "pur": 35801, "Ġzat": 35802, "Ġobsess": 35803, "Ġmotivo": 35804, "ĠKub": 35805, "ĠDrug": 35806, "Ant": 35807, "ĠPlayers": 35808, "ĠHumans": 35809, "Ġmelee": 35810, "ĠWildlife": 35811, "ĠVP": 35812, "Ġvolcanic": 35813, "Ġcomin": 35814, "ĠGuang": 35815, "ĠÏĦιÏĤ": 35816, "ĠоÑģобенно": 35817, "ĠSize": 35818, "Listen": 35819, "ĠAaa": 35820, "appro": 35821, "Ġbarbar": 35822, "ĠParkinson": 35823, "нÑıÑĤÑĮ": 35824, "åį°": 35825, "Ġunderestimate": 35826, "Ġsubstitution": 35827, "Ġcosmetic": 35828, "ä¸ĭ次": 35829, "Ġwillen": 35830, "Ġbeide": 35831, "anni": 35832, "Ġconditioned": 35833, "ĠDebbie": 35834, "Ġisto": 35835, "ĠEdwards": 35836, "ìĽĮìļĶ": 35837, "ĠÑĤов": 35838, "Ġabbrevi": 35839, "ĠMün": 35840, "ĠPrinc": 35841, "ĠLiang": 35842, "Ġstink": 35843, "Ġradioactive": 35844, "ãģĨãĤı": 35845, "Ġacontec": 35846, "Ġuncon": 35847, "ĠTurbo": 35848, "ãģIJ": 35849, "Ġkisses": 35850, "æĺ¯ä»Ģ麼": 35851, "еÑĤÑĢов": 35852, "Ġfrontier": 35853, "ĠSpy": 35854, "ĠBelarus": 35855, "ĠCBS": 35856, "á»Ĺ": 35857, "amoto": 35858, "íķľëį°": 35859, "ĠÑģÑĤÑĢо": 35860, "ĠEnfin": 35861, "Ġbreadth": 35862, "éĺ²": 35863, "ĠCafe": 35864, "ĠDafür": 35865, "ĠBour": 35866, "aras": 35867, "Ġblueprint": 35868, "anı": 35869, "Ġconstants": 35870, "Ġattacker": 35871, "ĠFormula": 35872, "zaÄĩ": 35873, "Ġsowie": 35874, "Ġeyebrow": 35875, "obook": 35876, "Ġsetzen": 35877, "第ä¸ī": 35878, "onsider": 35879, "awning": 35880, "Ġsöyleye": 35881, "Ġinvaded": 35882, "Ġpronouns": 35883, "Ġdobry": 35884, "Si": 35885, "ĠХоÑĤ": 35886, "Ġvolleyball": 35887, "Ġlament": 35888, "isches": 35889, "arme": 35890, "api": 35891, "ĠWiki": 35892, "лиÑĪ": 35893, "Ġkasih": 35894, "Ġpess": 35895, "ĠÑĦоÑĤ": 35896, "ĠSul": 35897, "å¾·": 35898, "Ġpseudo": 35899, "Ġmemo": 35900, "ĠìĹ°ìĬµ": 35901, "ĠдоллаÑĢов": 35902, "ĠпеÑĢем": 35903, "ĠReach": 35904, "miral": 35905, "alted": 35906, "Ġstatut": 35907, "reading": 35908, "Ġsöyled": 35909, "ĠLindsey": 35910, "ĠAhmad": 35911, "ë¶Ģë": 35912, "ĠСегоднÑı": 35913, "Ġprzygot": 35914, "Ġhyster": 35915, "URE": 35916, "ĠNeigh": 35917, "Reporter": 35918, "ĠBunu": 35919, "ĠTreaty": 35920, "ĠRank": 35921, "ĠFame": 35922, "inished": 35923, "Ġgeared": 35924, "Ġcompose": 35925, "odia": 35926, "ĠLon": 35927, "ĠjesteÅĽmy": 35928, "ĠDIRECTOR": 35929, "Ġelkaar": 35930, "ĠViel": 35931, "×IJש": 35932, "ynthia": 35933, "並": 35934, "Ġmère": 35935, "ĠTomato": 35936, "Ġexatamente": 35937, "niÄĻ": 35938, "ĠFrei": 35939, "ĠDif": 35940, "Ġopenings": 35941, "Ġgraphical": 35942, "ĠÑĥдоб": 35943, "ĠвÑģп": 35944, "ĠWeekly": 35945, "ева": 35946, "Ġhangs": 35947, "Ġunsafe": 35948, "Ġemblem": 35949, "ĠKolleginnen": 35950, "alay": 35951, "Ġksi": 35952, "Ġhides": 35953, "Ġolmay": 35954, "Ġentste": 35955, "Ġarthritis": 35956, "ÃŁerdem": 35957, "Ġbinnen": 35958, "Ġlistens": 35959, "ĠHess": 35960, "åĨįä¾Ĩ": 35961, "ĠLouise": 35962, "lden": 35963, "енÑģ": 35964, "ĠVersion": 35965, "ĠAgriculture": 35966, "ìĬ¤ë¥¼": 35967, "ман": 35968, "ëĦ¤ìļĶ": 35969, "Ġwines": 35970, "ĠINF": 35971, "rul": 35972, "ĠJK": 35973, "ıyorlar": 35974, "shield": 35975, "reath": 35976, "Ġterus": 35977, "ĠLum": 35978, "Ġanticipation": 35979, "Ġaccustomed": 35980, "ĠMina": 35981, "Ġwield": 35982, "ioè": 35983, "mera": 35984, "Ġcountdown": 35985, "Ġcling": 35986, "Ġcommend": 35987, "Ġfaktiskt": 35988, "Ġdefenses": 35989, "Ġcockpit": 35990, "Ġкоманд": 35991, "Ġdishwas": 35992, "ĠThanos": 35993, "Ġkidneys": 35994, "Ġsehe": 35995, "Ġmicrobes": 35996, "Ġcuff": 35997, "ĠвÑĭÑģок": 35998, "ĠSpicy": 35999, "çŃīçŃī": 36000, "வர": 36001, "culus": 36002, "orc": 36003, "ç¾ħ": 36004, "ixes": 36005, "ĠCredit": 36006, "Ġraj": 36007, "Ġbringt": 36008, "ĠNiss": 36009, "Ġgrim": 36010, "ĠSOL": 36011, "Ġtenim": 36012, "ĠSudan": 36013, "ĠSpart": 36014, "Ġpromotes": 36015, "ĠNossa": 36016, "ĠÑģоÑģÑĤоÑıни": 36017, "Ġì°©": 36018, "Ġuncont": 36019, "ĠLiberal": 36020, "ĠТолÑĮко": 36021, "ĠViele": 36022, "Ġktórej": 36023, "Ġ****": 36024, "Max": 36025, "ĠЧÑĤобÑĭ": 36026, "350": 36027, "Ġíĺ¼ìŀIJ": 36028, "Ġë¶Ħëĵ¤ìĿ´": 36029, "Ġwarp": 36030, "Ġtenga": 36031, "Ġsympathetic": 36032, "Ġbizi": 36033, "ĠZack": 36034, "iedo": 36035, "Ġëī´ì": 36036, "piel": 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"Ġdeform": 36094, "Ġcontracting": 36095, "ĠTIME": 36096, "irse": 36097, "Ġneben": 36098, "Ġcerc": 36099, "ĠArmstrong": 36100, "Ġtester": 36101, "Ġparfait": 36102, "Ġjealousy": 36103, "Ġtoxins": 36104, "Ġdisbel": 36105, "ÑĥÑĢÑĭ": 36106, "impression": 36107, "Ġprostate": 36108, "Ġfirewall": 36109, "Ġclassics": 36110, "еÑĩÑĮ": 36111, "Ġsocialism": 36112, "Ġgracious": 36113, "ĠÑģнова": 36114, "ĠднÑı": 36115, "Ġburner": 36116, "ĠMinor": 36117, "Ġìļ°ë¦¬ë": 36118, "Ġjedes": 36119, "Ġcontinuum": 36120, "Ġhots": 36121, "Ġoccurrence": 36122, "Ġadministered": 36123, "ĠзамеÑĤ": 36124, "Ġhesitation": 36125, "Ġdrills": 36126, "erca": 36127, "ĠвÑĤоÑĢой": 36128, "Ġsteadily": 36129, "Ġinsanlar": 36130, "Ġihan": 36131, "íij": 36132, "Ġhelper": 36133, "ĠSenin": 36134, "åģľ": 36135, "ование": 36136, "ĠERIC": 36137, "bla": 36138, "ĠAcademic": 36139, "Ġhumanities": 36140, "black": 36141, "umpy": 36142, "ortex": 36143, "ĠìłĪë": 36144, "ĠØ¥ÙĨ": 36145, "Ġdisclose": 36146, "ĠElijah": 36147, "ĠλÎŃ": 36148, 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36209, "ĠNatomiast": 36210, "Ġemergence": 36211, "rays": 36212, "Ġcrafted": 36213, "gary": 36214, "ãģłãģij": 36215, "üng": 36216, "-\"": 36217, "Ġhacked": 36218, "Ġstray": 36219, "encie": 36220, "emo": 36221, "Ġcomen": 36222, "ĠKız": 36223, "ĠJasmine": 36224, "ĠHindi": 36225, "manas": 36226, "Ġinfinitely": 36227, "emon": 36228, "ìĿ¸ëį°ìļĶ": 36229, "jak": 36230, "Ġroaring": 36231, "érique": 36232, "sweise": 36233, "ĠRolex": 36234, "åł±å°İ": 36235, "ĠStuart": 36236, "bnb": 36237, "Ġdiagnose": 36238, "Ġcoherent": 36239, "ĠMJ": 36240, "æºĸåĤĻ": 36241, "Ġpike": 36242, "lav": 36243, "Ġorchestral": 36244, "аÑģÑĤи": 36245, "Ġterminar": 36246, "Ġgatherings": 36247, "Ġcompliant": 36248, "Ġupgrading": 36249, "Ġregulator": 36250, "Ġlanç": 36251, "éĢ£": 36252, "Ġmerchants": 36253, "tawa": 36254, "Ġmonitored": 36255, "Ġrendre": 36256, "两": 36257, "Ġunterwegs": 36258, "anguard": 36259, "gard": 36260, "ĠBelow": 36261, "duino": 36262, "ĠЦе": 36263, "Ġimpedance": 36264, "ìľ¡": 36265, "份": 36266, 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36323, "scenes": 36324, "Ġobviamente": 36325, "икÑģ": 36326, "Ġש×ŀ×": 36327, "Ġsmoothie": 36328, "ŀĪë": 36329, "Ġdime": 36330, "ĠíĸĪìĸ´ìļĶ": 36331, "Ġappel": 36332, "ĠCatholics": 36333, "Ġsingles": 36334, "Ġlaten": 36335, "Ġçünkü": 36336, "ĠVader": 36337, "æıĽ": 36338, "Ġvardı": 36339, "ĠIstanbul": 36340, "gré": 36341, "ĠElsa": 36342, "ël": 36343, "Ġinvece": 36344, "Ġcrane": 36345, "Ġobe": 36346, "ĠShark": 36347, "Ġsmack": 36348, "Ġrestoring": 36349, ".\\": 36350, "Ġë¹łë": 36351, "Ġfaded": 36352, "umbers": 36353, "Singing": 36354, "Ġdepressing": 36355, "thest": 36356, "ĠWahr": 36357, "Ġmultitude": 36358, "ÑĢавÑģÑĤвÑĥйÑĤе": 36359, "rijk": 36360, "eka": 36361, "Ġcompletes": 36362, "ĠWells": 36363, "Ġroy": 36364, "ĠPray": 36365, "ĠKalau": 36366, "izin": 36367, "iaÅĤem": 36368, "Ġlocom": 36369, "ĠNashville": 36370, "ĠPentagon": 36371, "미": 36372, "ĠNEW": 36373, "ÄħÄĩ": 36374, "ÃŃss": 36375, "Ġmarrying": 36376, "Ġfeud": 36377, "íĻķ": 36378, "æĢ¥": 36379, ")!": 36380, "ĠOperations": 36381, "ÑĥÑĶ": 36382, "Ġmoje": 36383, "Ġinstructed": 36384, "ĠëĪĦ구": 36385, "Ġ×Ķ×Ĵ": 36386, "ĠпомоÑīÑĮÑİ": 36387, "Ġsabia": 36388, "ìķĺìĸ´ìļĶ": 36389, "plane": 36390, "pri": 36391, "ĠполноÑģÑĤÑĮÑİ": 36392, "ĠKitty": 36393, "Ġpróprio": 36394, "edere": 36395, "Ġinteresante": 36396, "Ġде": 36397, "Ġcondensed": 36398, "Ġavent": 36399, "TOR": 36400, "Ġgreasy": 36401, "ARK": 36402, "orta": 36403, "AJ": 36404, "Ġdisreg": 36405, "Ġcorrections": 36406, "Ġstero": 36407, "Ġinfluenza": 36408, "Ġdesses": 36409, "Ġballots": 36410, "Ġmeget": 36411, "Ġmafia": 36412, "Ġböl": 36413, "nost": 36414, "ĠÑģÑĤаÑĤÑĮ": 36415, "Ġresponder": 36416, "Ġhinten": 36417, "grav": 36418, "à¸Ńะ": 36419, "ynchron": 36420, "Ġviens": 36421, "Ġsamo": 36422, "Ġdt": 36423, "pannt": 36424, "ĠÅĽwiat": 36425, "ĠзапиÑģ": 36426, "Ġmerged": 36427, "Ġkep": 36428, "Ġmisleading": 36429, "Ġdigamos": 36430, "Ġammon": 36431, "è¾Ľ": 36432, "chet": 36433, "Ġê°Ģìł¸": 36434, "Ġuni": 36435, "ĠëIJĺëĬĶëį°": 36436, "ĠнапÑĢав": 36437, "ĠкоÑĤоÑĢого": 36438, "Ġanimate": 36439, "×ķ×IJ×": 36440, "еÑĢв": 36441, "Ġminced": 36442, "Ġkaum": 36443, "ãģĤãģģ": 36444, "ÏĢε": 36445, "лег": 36446, "existing": 36447, "Ġplataform": 36448, "ĠKRIS": 36449, "ìĽł": 36450, "ĠFamilien": 36451, "ĠLibya": 36452, "Ġbiodiversity": 36453, "Ġidiots": 36454, "irdi": 36455, "Ġszyb": 36456, "ĠRolling": 36457, "ücht": 36458, "ĠÑĥдив": 36459, "ÑģÑĥд": 36460, "Ġrealizar": 36461, "Ġcanned": 36462, "ĠÑĢан": 36463, "Ġmetabolic": 36464, "ĠBeef": 36465, "Ġkilka": 36466, "лÑİÑģ": 36467, "Ġregistry": 36468, "моÑĤÑĢиÑĤе": 36469, "Ġvielä": 36470, "Ġodc": 36471, "Ġcondemned": 36472, "æ©ĭ": 36473, "fal": 36474, "ĠDil": 36475, "woÅĽci": 36476, "Aw": 36477, "Ġstatistically": 36478, "Ġsogen": 36479, "ĠBETH": 36480, "Ġshaving": 36481, "幸": 36482, "ocal": 36483, "ĠFunny": 36484, "Ġpeacefully": 36485, "Ġaddictive": 36486, "ĠInsert": 36487, "lauf": 36488, "Ġexperiencia": 36489, "é¦ĸåħĪ": 36490, "иÑĤелÑı": 36491, "ÃŃgen": 36492, "ágina": 36493, "Ġabdomen": 36494, "íķľëĭ¤": 36495, "icus": 36496, "imana": 36497, "ìį¨": 36498, "arching": 36499, "Ġkonkret": 36500, "ìķĺë": 36501, "ека": 36502, "oufl": 36503, "ivel": 36504, "Ġnude": 36505, "ètres": 36506, "Ġmonsieur": 36507, "Ġclash": 36508, "Ġtherapists": 36509, "Ġcubed": 36510, "Ġretrouver": 36511, "Ġwaveform": 36512, "Ġpotem": 36513, "ĠFormer": 36514, "isión": 36515, "åºľ": 36516, "Ġ×IJ×Ŀ": 36517, "undos": 36518, "ĠMeinung": 36519, "صÙĦ": 36520, "ĠJude": 36521, "ĠnÃ¥r": 36522, "ĠLeonardo": 36523, "ĠCristo": 36524, "ĠGOT": 36525, "ÑģÑĤÑĢÑĥк": 36526, "LAN": 36527, "ĠgÃ¥ng": 36528, "Ġdéb": 36529, "ĠFrankfurt": 36530, "Ġcrappy": 36531, "Ġlil": 36532, "année": 36533, "ĠмеÑģÑĤе": 36534, "RET": 36535, "ĠNer": 36536, "ĠCOSTA": 36537, "Ġjedem": 36538, "Ġcurtains": 36539, "Ġiterations": 36540, "Ġunav": 36541, "Ġplaque": 36542, "orum": 36543, "Ġζ": 36544, "Ġnúmeros": 36545, "Ġdesap": 36546, "²½": 36547, "Ġcompiled": 36548, "Ġrefle": 36549, "Ġrankings": 36550, "Ġrepaired": 36551, "ĠÐĿапÑĢ": 36552, "Ġdownloads": 36553, "Ġarmour": 36554, "Ġ×Ļ×ķתר": 36555, "Ġlongevity": 36556, "ĠTONER": 36557, "ĠкомменÑĤаÑĢ": 36558, "Ġczego": 36559, "Ġnotify": 36560, "Ġairports": 36561, "Ġenduring": 36562, "lette": 36563, "Ġapparat": 36564, "Ġhabil": 36565, "á»ĩc": 36566, "nad": 36567, "ICO": 36568, "ĠBrah": 36569, "Ġsegún": 36570, "Ġgovernors": 36571, "kaha": 36572, "ĠSchluss": 36573, "Ġodpowied": 36574, "irting": 36575, "Ġrempl": 36576, "ĠAboriginal": 36577, "identally": 36578, "Ġenhancing": 36579, "licting": 36580, "ĠHawaiian": 36581, "Ġstriving": 36582, "ĠNiet": 36583, "Ġznaczy": 36584, "Ġobedience": 36585, "ĠnÃ¥got": 36586, "Ġexpired": 36587, "Ġ1918": 36588, "presented": 36589, "Ġprowad": 36590, "ĠTerr": 36591, "ĠPrinceton": 36592, "Ġmorgen": 36593, "Ġattracting": 36594, "ĠSigma": 36595, "igner": 36596, "ĠRechts": 36597, "ĠPeki": 36598, "Ġmethy": 36599, "Ġhamm": 36600, "Ġdireito": 36601, "Ġdelegation": 36602, "иваÑİÑĤ": 36603, "Ġgin": 36604, "Young": 36605, "Ġdependencies": 36606, "ĠBradley": 36607, "buds": 36608, "Ġfis": 36609, "Ġpytanie": 36610, "Ġinterconnected": 36611, "Ġembaixo": 36612, "ĠSas": 36613, "Ġruh": 36614, "ĠSicht": 36615, "Sur": 36616, "Ġsuperb": 36617, "ĠSabbath": 36618, "ĠDanger": 36619, "kol": 36620, "Ġhou": 36621, "supp": 36622, "ĠNacional": 36623, "Ġsuccession": 36624, "Ġvá": 36625, "ĠMaÃŁnahmen": 36626, "ĠJessie": 36627, "ĠIdaho": 36628, "forest": 36629, "ħĺ": 36630, "Ġ×ŀ×ĵ": 36631, "ĠØ£ÙĬ": 36632, "Ġsweetheart": 36633, "Ġneatly": 36634, "ĠEvangel": 36635, "곡": 36636, "ĠSuite": 36637, "ública": 36638, "ĠÑĥли": 36639, "ĠAnnouncer": 36640, "ligh": 36641, "Ġsensations": 36642, "Ġshelters": 36643, "Ġhart": 36644, "Ġsqueezing": 36645, "ĠRivers": 36646, "ĠCooking": 36647, "ì±ħ": 36648, "personal": 36649, "Ġmanos": 36650, "ÑijÑĤÑģÑı": 36651, "wij": 36652, "Ġgogg": 36653, "ĠMilli": 36654, "ĠFP": 36655, "ünst": 36656, "ĠLS": 36657, "Ġspraying": 36658, "Ġfaux": 36659, "Ġautograph": 36660, "ologic": 36661, "Ġtorment": 36662, "Ġencrypted": 36663, "á»ħ": 36664, "Ġestre": 36665, "ç¹¼": 36666, "à±": 36667, "Ġstumbled": 36668, "Ġaider": 36669, "Ġsaben": 36670, "xter": 36671, "ĠCities": 36672, "ĠTürk": 36673, "ëĭ¥": 36674, "chine": 36675, "Ġtopping": 36676, "Ġpoisoned": 36677, "ĠRomania": 36678, "×ĵ×Ļ": 36679, "Ģë¡ľ": 36680, "ĠпоÑĢÑıд": 36681, "Ġchirping": 36682, "ĠìĻĦë": 36683, "×ij×¢": 36684, "Ġcuanto": 36685, "Ġdonating": 36686, "ĠRegent": 36687, "ĠBeruf": 36688, "Ġdistracting": 36689, "Ġstamina": 36690, "ĠDarren": 36691, "Ġì¶ķ": 36692, "lists": 36693, "dal": 36694, "chuss": 36695, "Ġeconomist": 36696, "ãģĪãĥ¼": 36697, "orgt": 36698, "Ġistiyorum": 36699, "è¿Ľ": 36700, "ĠSurprise": 36701, "ĠHao": 36702, "Ġìµľê³ł": 36703, "ĠGW": 36704, "ĠInner": 36705, "Ġquieren": 36706, "Ġminded": 36707, "Ġsupercomputer": 36708, "Ġdiagrams": 36709, "íĬľë": 36710, "ê²łìĸ´": 36711, "ĠобÑĬÑıÑģ": 36712, "Ġestaban": 36713, "Ġdestroys": 36714, "ĠBreaking": 36715, "ĠkarÄ±ÅŁ": 36716, "Ġrebuilding": 36717, "ľëĮĢ": 36718, "ливо": 36719, "ĠSauce": 36720, "ĠFusion": 36721, "×ķ×ŀ×": 36722, "ĠQuinn": 36723, "Ġgauche": 36724, "ĠÙĪØ£": 36725, "ĠÈ": 36726, "çĵľ": 36727, "Ġtechno": 36728, "Ġdispatch": 36729, "ĠaÅŁk": 36730, "Ġeinzel": 36731, "ĠGmail": 36732, "çŀ": 36733, "Ġê°ľìĿ¸": 36734, "ĠÑģемÑĮ": 36735, "Ġjourneys": 36736, "Ġiht": 36737, "Ġfibre": 36738, "Ġdramas": 36739, "ouched": 36740, "Ġrename": 36741, "ĠопеÑĢ": 36742, "Ġpoo": 36743, "ĠDru": 36744, "ĠиÑĤог": 36745, "Ġzast": 36746, "Ġcoz": 36747, "Ġzucch": 36748, "Ġobtaining": 36749, "Ġcommute": 36750, "Ġsubmer": 36751, "ĠVish": 36752, "ĠRabb": 36753, "ogg": 36754, "Ġhut": 36755, "íĸĪìĸ´": 36756, "æ¯Ķå¦Ĥ": 36757, "eremi": 36758, "Ġμα": 36759, "Ġdiskut": 36760, "ĠбÑĥк": 36761, "Ġimpaired": 36762, "depend": 36763, "ĠÙĪا": 36764, "ĠÑĢÑĥк": 36765, "ĠбаÑĢ": 36766, "Ġoxidation": 36767, "Ġsituação": 36768, "ÉĻn": 36769, "ução": 36770, "Ġsagte": 36771, "ĠSER": 36772, "ĠCake": 36773, "Ġturmeric": 36774, "ĠKak": 36775, "bung": 36776, "ĠKá¹Ľá¹£á¹ĩa": 36777, "Ġpoisoning": 36778, "Ġslipping": 36779, "ĠSays": 36780, "å°±åı¯ä»¥": 36781, "òng": 36782, "çŁ³": 36783, "«": 36784, "ĠClaudia": 36785, "ĠCharacter": 36786, "ниÑĨ": 36787, "coat": 36788, "Ġprogressed": 36789, "ĠFergus": 36790, "Ġìĺ¤ëĬ": 36791, "Ġoat": 36792, "ordable": 36793, "ĠLey": 36794, "ĠHeraus": 36795, "Ġresultados": 36796, "ĠKayla": 36797, "Ġriff": 36798, "Ġchegou": 36799, "Ġxi": 36800, "Ġspacious": 36801, "Ġrecognised": 36802, "Ġech": 36803, "ĠTie": 36804, "Ġlauncher": 36805, "Jim": 36806, "Ġsuppression": 36807, "ĠImpossible": 36808, "Ġguitars": 36809, "ĠFourier": 36810, "иÑĩеÑģкий": 36811, "ĠTherap": 36812, "ĠKaf": 36813, "centered": 36814, "ĠÑģооÑĤвеÑĤ": 36815, "Ġklim": 36816, "Ġcarbohydrates": 36817, "ignant": 36818, "ĠAstron": 36819, "Ġemple": 36820, "Ġdrastic": 36821, "ĠмиÑĢе": 36822, "вин": 36823, "uw": 36824, "Ġprettier": 36825, "Ġdonuts": 36826, "ĠAthena": 36827, "Ġdissert": 36828, "Ġplante": 36829, "Ġuranium": 36830, "ìĿĮë": 36831, "aré": 36832, "Ġrzecz": 36833, "Ġdisplaying": 36834, "æĪ²": 36835, "Ġsarc": 36836, "rão": 36837, "Ġtampoco": 36838, "Ġphilosophers": 36839, "ĠRecht": 36840, "æĵļ": 36841, "Ġcomentarios": 36842, "yse": 36843, "Ġìľ¤": 36844, "Ġmise": 36845, "ĠGin": 36846, "Ġном": 36847, "ĠFROM": 36848, "liner": 36849, "atif": 36850, "ĠspoÅĤec": 36851, "xa": 36852, "ĠÑĤÑĢÑĥд": 36853, "Ġwag": 36854, "기ìĹIJ": 36855, "ĠMG": 36856, "Ġoffspring": 36857, "ĠUnderstanding": 36858, "åıªæĺ¯": 36859, "ORA": 36860, "Ġwhirring": 36861, "Ġsurrend": 36862, "Ġpoker": 36863, "Ġmonuments": 36864, "ĠâĻ©": 36865, "Ġorganised": 36866, "ĠSozial": 36867, "ĠFactory": 36868, "Ñħа": 36869, "Ġresemble": 36870, "зд": 36871, "Ġexplosions": 36872, "Ġpayroll": 36873, "Ġomn": 36874, "ĠJorge": 36875, "ιÏĥ": 36876, "Ġfracture": 36877, "Ġpersecution": 36878, "Ġdemais": 36879, "ECH": 36880, ",)": 36881, "Ġcriar": 36882, "ĠJOSH": 36883, "Ġdemographics": 36884, "Ġ1600": 36885, "Ġcurrencies": 36886, "ĠTips": 36887, "ĠéĢĻåĢĭ": 36888, "ĠRefer": 36889, "ĠDancing": 36890, "Ġinconsistent": 36891, "Ġdeh": 36892, "Ġimmens": 36893, "Ġmeist": 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"Ġmej": 37758, "Ġstiffness": 37759, "ADAS": 37760, "Ġdiffers": 37761, "Ġalot": 37762, "ĠSig": 37763, "ÑıÑĤелÑĮ": 37764, "Ġabstraction": 37765, "åľĺ": 37766, "Ġkeiner": 37767, "grupp": 37768, "ĠSherlock": 37769, "íĺĶ": 37770, "Ġcite": 37771, "Ġoverflow": 37772, "Ġtại": 37773, "úcar": 37774, "bula": 37775, "Ġconjunto": 37776, "ĠCI": 37777, "Ġmoderator": 37778, "Ġindirectly": 37779, "Ġalleine": 37780, "âĤ": 37781, "ÑĪиб": 37782, "Ġбаб": 37783, "Ġdanach": 37784, "Ġ1939": 37785, "Ġpromet": 37786, "Ġdestinations": 37787, "ĠIllust": 37788, "ικÏĮ": 37789, "Ġsabes": 37790, "Ġheh": 37791, "ĠGesetzent": 37792, "ĠMiz": 37793, "енко": 37794, "ĠMys": 37795, "Ь": 37796, "ĠJudaism": 37797, "Ġmustache": 37798, "Ġstimmt": 37799, "ĠGaza": 37800, "Ġvolte": 37801, "Ġnuo": 37802, "Ġmón": 37803, "ĠComput": 37804, "ูà¹Ī": 37805, "ĠRadi": 37806, "Ġexceptionally": 37807, "Ġassumes": 37808, "éĸĭå¿ĥ": 37809, "ãģĪãģ°": 37810, "inform": 37811, "Ġshrine": 37812, "æĵĬ": 37813, "Ġimplication": 37814, "ĠFitz": 37815, "æ²ĴéĹľä¿Ĥ": 37816, "!.": 37817, "Ġlt": 37818, "Ġalloy": 37819, "Ġethic": 37820, "Ġmonastery": 37821, "ìĭľì£ł": 37822, "icação": 37823, "Ġcoordinating": 37824, "ĠMoto": 37825, "Ġoverlook": 37826, "Ġchois": 37827, "Ġantibiotic": 37828, "ĠMinne": 37829, "ĠBJ": 37830, "ĠApa": 37831, "orian": 37832, "Ġspilled": 37833, "Jam": 37834, "Ġhusbands": 37835, "Ġcreations": 37836, "Ġañ": 37837, "üssel": 37838, "ĠìĿ´ìļ©": 37839, "Ġanalyse": 37840, "rose": 37841, "Ġpunched": 37842, "Ġpresque": 37843, "Ġastronomy": 37844, "Ġschwierig": 37845, "ĠEbola": 37846, "Ġcis": 37847, "Ġacet": 37848, "ĠFX": 37849, "endre": 37850, "ĠìĿĮìķħ": 37851, "Ġwebpage": 37852, "Ġfreaked": 37853, "Ġlatte": 37854, "Ġì¿ł": 37855, "Ġ머ë": 37856, "Never": 37857, "Gra": 37858, "íĻĶ를": 37859, "eyed": 37860, "Ġë°ľëĿ¼": 37861, "Ġespera": 37862, "Ġaparece": 37863, "ração": 37864, "Ġdisruptive": 37865, "ĠJoint": 37866, "urous": 37867, "reas": 37868, "ĠquerÃŃa": 37869, "Ġdistributions": 37870, "Ġexponent": 37871, "ì¹ĺ를": 37872, "Ġdl": 37873, "zhou": 37874, "ĠHearing": 37875, "å·®ä¸įå¤ļ": 37876, "ĠCraw": 37877, "Ġfloats": 37878, "ounced": 37879, "Lab": 37880, "World": 37881, "Ġburdens": 37882, "Ġauthoritarian": 37883, "ĠBolt": 37884, "ĠоднÑĥ": 37885, "Ġpigeon": 37886, "Ġdistractions": 37887, "ĠHerausforder": 37888, "Ġzest": 37889, "esc": 37890, "Ġshakes": 37891, "atas": 37892, "ĠÙħØ´": 37893, "holes": 37894, "Ġthinkers": 37895, "alta": 37896, "Ġarche": 37897, "ĠSuk": 37898, "anha": 37899, "Ġtempting": 37900, "Ġyoutuber": 37901, "Ġvì": 37902, "ĠdziaÅĤa": 37903, "ĠVatican": 37904, "Park": 37905, "Ġsupers": 37906, "ĠNikki": 37907, "ëĬIJë": 37908, "orang": 37909, "ramient": 37910, "鬼": 37911, "Ġê°ĸê³ł": 37912, "Ġdesserts": 37913, "Ġavere": 37914, "ĠGregory": 37915, "Ġëĵ¤ìĸ´ìĺ": 37916, "Ġcosting": 37917, "ĠClinic": 37918, "Ġrebels": 37919, "ĠMob": 37920, "Ġbunlar": 37921, "ĠYours": 37922, "ertime": 37923, "Ġretali": 37924, "mara": 37925, "atus": 37926, "alles": 37927, "ĠдÑĢ": 37928, "ĠдиÑģ": 37929, "Ġdiscounts": 37930, "ĠGUY": 37931, "Ġкакое": 37932, "ĠExperiment": 37933, "rement": 37934, "ĠXiang": 37935, "Ġbate": 37936, "WE": 37937, "Ġspecialize": 37938, "Ġdeity": 37939, "ĠLoki": 37940, "mag": 37941, "ĠNit": 37942, "West": 37943, "Ġmaternal": 37944, "Ġquis": 37945, "åŁºæľ¬": 37946, "broken": 37947, "Ġlasers": 37948, "Ġhakk": 37949, "ĠAngels": 37950, "Ġmastery": 37951, "antis": 37952, "Tiffany": 37953, "eee": 37954, "çij": 37955, "orem": 37956, "Ġinacc": 37957, "Ġjurisdictions": 37958, "ĠKardash": 37959, "æľº": 37960, "Il": 37961, "ĠSinn": 37962, "åĭķçĶ»": 37963, "Ġathletics": 37964, "cÄĻ": 37965, "Ġloosely": 37966, "Ġdieta": 37967, "Ag": 37968, "Ġ??": 37969, "ĠëĮĢíijľ": 37970, "Ġsuperv": 37971, "Ġnutrit": 37972, "Ġdrifting": 37973, "ĠìĦłìĥĿëĭĺ": 37974, "ĠпонÑıл": 37975, "ĠVictory": 37976, "ÙĦØ©": 37977, "×ķ׳×Ķ": 37978, "ĠпиÑĪ": 37979, "Ġshaved": 37980, "Ġmesure": 37981, "onden": 37982, "Ùĥر": 37983, "Ġexile": 37984, "ĠDesde": 37985, "ĠPinterest": 37986, "Ġattachments": 37987, "Ġhombres": 37988, "Ġfines": 37989, "ĠìĦ¸ìĥģ": 37990, "Ġsleeps": 37991, "ĠTaco": 37992, "ĠIRA": 37993, "rios": 37994, "Ġoll": 37995, "etes": 37996, "Ġunut": 37997, "fashioned": 37998, "Ġtreball": 37999, "ĠNearly": 38000, "ĠÑĢеалÑĮно": 38001, "Ġchil": 38002, "éĢ±": 38003, "ÄŁa": 38004, "ĠMEL": 38005, "roscop": 38006, "ĠCG": 38007, "Ġvenge": 38008, "Ġdishwasher": 38009, "algic": 38010, "Ġmodifier": 38011, "Ġembassy": 38012, "timer": 38013, "emics": 38014, "Ġintricate": 38015, "Ġevet": 38016, "ĠëĮĢë°ķ": 38017, "Ġisot": 38018, "ĠнаÑĥÑĩ": 38019, "ĠQuiz": 38020, "reso": 38021, "δÏİ": 38022, "Ġyelled": 38023, "Ġfeder": 38024, "ELLER": 38025, "Ġexceeded": 38026, "onas": 38027, "icano": 38028, "ĠживоÑĤ": 38029, "ĠMao": 38030, "ĠKazuto": 38031, "Ġãħĭãħĭãħĭãħĭ": 38032, "Ġfrontline": 38033, "ĠHungarian": 38034, "Ġüberall": 38035, "awat": 38036, "Ġgrips": 38037, "ições": 38038, "arnya": 38039, "ĠÍ¡": 38040, "Ġseid": 38041, "Ġanak": 38042, "Ġacabou": 38043, "íķij": 38044, "Ġnotorious": 38045, "ĠGodzilla": 38046, "Ġovercoming": 38047, "ĠPend": 38048, "Ġolabilir": 38049, "ülme": 38050, "Ġerhalten": 38051, "ãĤīãģĦ": 38052, "ê·¹": 38053, "ĠMeter": 38054, "Ġstaan": 38055, "Ol": 38056, "Ġchats": 38057, "ĠBuenos": 38058, "ÃŃve": 38059, "aluable": 38060, "Ġstrategically": 38061, "Ġcomprised": 38062, "ĠпеÑĢÑģонаж": 38063, "Ġwann": 38064, "ĠCen": 38065, "ниÑĤе": 38066, "Łģ": 38067, "ĠÑĤобой": 38068, "iad": 38069, "ĠkardeÅŁim": 38070, "ĠCongressman": 38071, "reaming": 38072, "homme": 38073, "Ġcommunaut": 38074, "Ġalcoholic": 38075, "Ġpickled": 38076, "Ġacord": 38077, "position": 38078, "egól": 38079, "Ġtroubling": 38080, "ĠMarcheg": 38081, "Ġzumindest": 38082, "Ġseamlessly": 38083, "Ġolun": 38084, "ĠTVs": 38085, "ĠпÑĢакÑĤиÑĩеÑģки": 38086, "Ġbackend": 38087, "ãģĵãĤĵãģ«ãģ¡ãģ¯": 38088, "idable": 38089, "Ġgadget": 38090, "Ġfaço": 38091, "ĠMarchegiani": 38092, "Ġë°¤": 38093, "Ġaccidental": 38094, "ĠLP": 38095, "Ġeldest": 38096, "ĠAdmiral": 38097, "ĠnÄĥm": 38098, "lever": 38099, "Ġpastel": 38100, "Ġfondo": 38101, "Connie": 38102, "Ġtercer": 38103, "Ġpact": 38104, "ĠMonte": 38105, "Ġmeats": 38106, "ĠSMS": 38107, "ĠAustralians": 38108, "ç¼": 38109, "Rhett": 38110, "Ġexactement": 38111, "Ġë¹¼": 38112, "ĠMOD": 38113, "ç¡": 38114, "ĠRapt": 38115, "ĠNoch": 38116, "Ġabort": 38117, "ĠNaval": 38118, "ĠFuji": 38119, "INTER": 38120, "ĠновÑĭй": 38121, "Ġmiejsce": 38122, "ĠICU": 38123, "ĠGraduate": 38124, "ĠGlen": 38125, "ardi": 38126, "ĠÈĺ": 38127, "Ġsolder": 38128, "Ġprofessions": 38129, "Ġorthog": 38130, "omn": 38131, "introdu": 38132, "ĠDenise": 38133, "ìŀIJ를": 38134, "Ġcorrespondence": 38135, "AMA": 38136, "Ġinflict": 38137, "Ġfand": 38138, "ĠGü": 38139, "ĠÑĩеÑĤ": 38140, "Ġtraced": 38141, "Ġpatents": 38142, "Ġambush": 38143, "Ġlotta": 38144, "ffer": 38145, "ĠWagner": 38146, "Ġimperson": 38147, "Ġextrêmement": 38148, "ÙĤت": 38149, "conduct": 38150, "Att": 38151, "ĠMueller": 38152, "ĠAlicia": 38153, "Ġcyc": 38154, "Ġhacker": 38155, "Ġtys": 38156, "Ġhail": 38157, "ĠзаÑıв": 38158, "Ġpasso": 38159, "Ġì¶Ķê°Ģ": 38160, "ĠÎĪ": 38161, "Ġpackaged": 38162, "ĠCynthia": 38163, "heet": 38164, "ä¸ŃåĽ½": 38165, "ĠNissan": 38166, "ĠQuesto": 38167, "é¨": 38168, "did": 38169, "Ġμια": 38170, "ĠEllis": 38171, "ĠAnalysis": 38172, "cemos": 38173, "Ġaseg": 38174, "ĠMyster": 38175, "ĠCao": 38176, "Ġtuv": 38177, "ĠIndustry": 38178, "ì£¼ê³ł": 38179, "otal": 38180, "Ġpequeño": 38181, "bras": 38182, "Ġcomprehend": 38183, "ĠSimpson": 38184, "ÑģÑĤвие": 38185, "ocracy": 38186, "иÑĩеÑģки": 38187, "ĠMush": 38188, "ĠLaurie": 38189, "Ġtriangular": 38190, "ĠPresents": 38191, "ĠKunden": 38192, "ç´¹": 38193, "æѦ": 38194, "ĠIss": 38195, "ĠDeck": 38196, "á»ĥn": 38197, "ĠDarkness": 38198, "Ġinflammatory": 38199, "eremiah": 38200, "Ġwarmed": 38201, "veyard": 38202, "ĠMemory": 38203, "etty": 38204, "Ġtaxpayers": 38205, "à¸ĵ": 38206, "Ø¡": 38207, "Ġpractise": 38208, "ëĭ¬ë": 38209, "Ġdrilled": 38210, "mÃ¼ÅŁ": 38211, "logo": 38212, "ĠFach": 38213, "¤ë¡ľ": 38214, "Ġübrigens": 38215, "Ġkonnten": 38216, "Ġnormalmente": 38217, "Ġargues": 38218, "ilingual": 38219, "°ë¥¼": 38220, "egal": 38221, "Ġtravaill": 38222, "ovy": 38223, "аÑĤо": 38224, "Ġruth": 38225, "ĠLights": 38226, "Ġconsisted": 38227, "×ijר×Ļ×Ŀ": 38228, "Ġstereotype": 38229, "Ġpayer": 38230, "ĠRee": 38231, "ĠAirbnb": 38232, "Ġdrowned": 38233, "ĠZoe": 38234, "Ġcanopy": 38235, "Ġbarr": 38236, "ĠноÑĩ": 38237, "Ġpagan": 38238, "Ġjars": 38239, "Ġrê": 38240, "erver": 38241, "æĪ¿": 38242, "ieben": 38243, "Ġespect": 38244, "ĠFi": 38245, "Ġunwilling": 38246, "Ġtechnician": 38247, "ặt": 38248, "member": 38249, "ĠCanal": 38250, "سÙħ": 38251, "Ġlieber": 38252, "Ġinference": 38253, "Ġhonoring": 38254, "åijµ": 38255, "ĠCampaign": 38256, "Ġlineage": 38257, "ĠStress": 38258, "Ġvictories": 38259, "Ġdeja": 38260, "×£": 38261, "êtes": 38262, "blick": 38263, "Ġменее": 38264, "oths": 38265, "ĠCouple": 38266, "Jason": 38267, "ĠNicolas": 38268, "екÑģ": 38269, "lib": 38270, "Ġherramient": 38271, "Ġ×IJ×ķ×ŀר": 38272, "Ġвидим": 38273, "millimeter": 38274, "Ġsilhouette": 38275, "Ġdriveway": 38276, "Ġcherish": 38277, "ãħłãħł": 38278, "Ġransom": 38279, "Ġinterdisciplinary": 38280, "ĠPortal": 38281, "Ġtrag": 38282, "thood": 38283, "Ġtedious": 38284, "Ġglossy": 38285, "Ġprépar": 38286, "ĠCay": 38287, "ĠTook": 38288, "ĠBottom": 38289, "Ġzig": 38290, "å«": 38291, "åį±": 38292, "represented": 38293, "à¹Ģลย": 38294, "Ġdesarrollo": 38295, "ìĦľë": 38296, "Ġviscos": 38297, "Ġmilligram": 38298, "ĠGund": 38299, "Ġferment": 38300, "drum": 38301, "Ġdrawers": 38302, "Laugh": 38303, "Ġpelos": 38304, "Ġpavement": 38305, "Ġmemoir": 38306, "avait": 38307, "Ġ2050": 38308, "¤ë¥¼": 38309, "Ġrazón": 38310, "Ġflourish": 38311, "Ġstern": 38312, "ä¸Ī": 38313, "ĠChung": 38314, "Ġserpent": 38315, "ĠGentlemen": 38316, "羣çļĦå¾Ī": 38317, "kook": 38318, "Ġlut": 38319, "importe": 38320, "parent": 38321, "Ġwsz": 38322, "Ġscree": 38323, "ĠMitarbeiter": 38324, "å·´": 38325, "mut": 38326, "Ġìĸĺ기를": 38327, "Ġsemble": 38328, "ĠOW": 38329, "Ġinvestigator": 38330, "ĠCheryl": 38331, "ĠGerald": 38332, "Ġprere": 38333, "Ġcompares": 38334, "nyt": 38335, "Ġdiferença": 38336, "?-": 38337, "Ġquá": 38338, "ר×Ļ": 38339, "Sen": 38340, "Ġheps": 38341, "Ġgratuit": 38342, "Ġconsort": 38343, "ĠSTOP": 38344, "ĠProtestant": 38345, "Ġelectrode": 38346, "âĹ": 38347, "Ġsecurely": 38348, "иÑĩеÑģкой": 38349, "Ġtää": 38350, "Ġregisters": 38351, "ĠHeavenly": 38352, "ogly": 38353, "issä": 38354, "ĠPhysics": 38355, "ĠMerkel": 38356, "Ġrév": 38357, "éĻ¢": 38358, "Ġerased": 38359, "ĠSacramento": 38360, "Ġcoffin": 38361, "Ġexacer": 38362, "Ġlanz": 38363, "Ġpoets": 38364, "ulif": 38365, "Ġì¹ĺë": 38366, "ĠNerd": 38367, "ĠNCT": 38368, "ĠHour": 38369, "nehmer": 38370, "ŀĺëıĦ": 38371, "ĠPrinci": 38372, "Sw": 38373, "mies": 38374, "armed": 38375, "ĠBeatles": 38376, "Ġpropagation": 38377, "Ġexchanged": 38378, "Ġcumulative": 38379, "Ġì§ijìĹIJ": 38380, "Ġdefeating": 38381, "æĬ±": 38382, "bels": 38383, "Ġwes": 38384, "ĠOdyssey": 38385, "ä½łæĥ³": 38386, "avior": 38387, "ĠìľĦìĹIJ": 38388, "Ġbrit": 38389, "Ġhijo": 38390, "DAY": 38391, "ĠاÙĦتÙĬ": 38392, "ĠСеÑĢг": 38393, "Ñĥка": 38394, "edsiÄĻ": 38395, "Ġimpos": 38396, "Ġellas": 38397, "Ġfirearms": 38398, "ĠNR": 38399, "Ġ×ij×IJ": 38400, "ĠÐŁÐ¾ÐºÐ°": 38401, "awi": 38402, "ĠìĦ±ê³µ": 38403, "Ġpupils": 38404, "ĠTack": 38405, "Ġfrase": 38406, "ĠShip": 38407, "Ġstad": 38408, "举": 38409, "ĠGreater": 38410, "unun": 38411, "immung": 38412, "grown": 38413, "ĠNXT": 38414, "ĠAmericas": 38415, "fox": 38416, "Ġmanten": 38417, "éłIJåĤĻ": 38418, "ĠÑģок": 38419, "Ġrikt": 38420, "lectric": 38421, "deep": 38422, "ĠзнаеÑĪÑĮ": 38423, "Ġbenut": 38424, "ĠInfrast": 38425, "ĠEmir": 38426, "ĠоÑĤпÑĢав": 38427, "ĠKimchi": 38428, "ĠFinnish": 38429, "´ìłģ": 38430, "inaire": 38431, "Ġoike": 38432, "æ¸ħæ¥ļ": 38433, "Ġhostage": 38434, "ĠButton": 38435, "ÙĤÙĬ": 38436, "eking": 38437, "ĠKazakh": 38438, "Ġcomforting": 38439, "Ġsog": 38440, "Ġgreeted": 38441, "guitar": 38442, "payer": 38443, "Ġrelational": 38444, "Ġconstruir": 38445, "çī¹åĪ¥": 38446, "opian": 38447, "ĠVolume": 38448, "ieth": 38449, "ÑģÑĤвом": 38450, "urrection": 38451, "liÅĽmy": 38452, "Ġhemisphere": 38453, "ĠBean": 38454, "IGN": 38455, "Ġkötü": 38456, "ĠFallout": 38457, "Ġbrace": 38458, "ç¹¼çºĮ": 38459, "ÏĢά": 38460, "ĠHAS": 38461, "Ġgé": 38462, "Ġcharacterize": 38463, "ặc": 38464, "ĠMilky": 38465, "Ġtumors": 38466, "Ġnuit": 38467, "ĠGaz": 38468, "ĠìŀĪëĭ¤ëĬĶ": 38469, "ĠгаÑĢ": 38470, "essment": 38471, "ĠAbe": 38472, "Ġë½ij": 38473, "ĠEinsatz": 38474, "JIN": 38475, "jä": 38476, "Cry": 38477, "ĠPromised": 38478, "ĠÑģеÑĢд": 38479, "okus": 38480, "Ġscalable": 38481, "ĠпоÑģмоÑĤÑĢеÑĤÑĮ": 38482, "ücklich": 38483, "Ġrealism": 38484, "Ġmayo": 38485, "Ġjuvenile": 38486, "Ġheadlights": 38487, "ĠgörÃ¼ÅŁ": 38488, "ĠReform": 38489, "Ġhalves": 38490, "czne": 38491, "Ġbreakup": 38492, "żej": 38493, "Ġrätt": 38494, "Day": 38495, "ĠìĿ¼ë³¸": 38496, "Ġmuerte": 38497, "Ġtunes": 38498, "ĠSmile": 38499, "record": 38500, "Ġrecherche": 38501, "atisfied": 38502, "Ġpozi": 38503, "Ġcelebrations": 38504, "isexual": 38505, "ĠROB": 38506, "thirds": 38507, "ĠFortune": 38508, "ĠÑĤой": 38509, "Ġbranded": 38510, "loo": 38511, "Ġdud": 38512, "Ġrandomized": 38513, "Ġcombin": 38514, "ä¸ĢäºĽ": 38515, "ieran": 38516, "czenia": 38517, "įãĥ«": 38518, "Ġcurator": 38519, "Ġartery": 38520, "ĠÑĥÑĪ": 38521, "ĠÑĩиÑĤ": 38522, "Ġsubsidies": 38523, "Ġblossom": 38524, "ĠTwilight": 38525, "Ġhyvä": 38526, "ĠPompe": 38527, "ĠCisco": 38528, "ĠÐŁÑĢо": 38529, "Ġbiri": 38530, "Ġgern": 38531, "Ġrebuilt": 38532, "Ġwcze": 38533, "Ġbenefici": 38534, "Ġdrummer": 38535, "Ġsolids": 38536, "Ġdiyorsun": 38537, "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸãģĦãģ¾ãģĹãģŁ": 38538, "lated": 38539, "Ġmuddy": 38540, "Ġholog": 38541, "Ġclaps": 38542, "ĠRings": 38543, "ĠOkey": 38544, "ĠBrave": 38545, "Ġvaluation": 38546, "Ġmigrant": 38547, "Ġintermitt": 38548, "Ġeigene": 38549, "iliary": 38550, "ãĥ¼ãĥĪ": 38551, "markt": 38552, "kr": 38553, "ĠRib": 38554, "á»Ļi": 38555, "Ġaccusations": 38556, "Ġarab": 38557, "wash": 38558, "ĠBardzo": 38559, "Ġugh": 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"Ġappreh": 38675, "ìłķìĿ´": 38676, "Pop": 38677, "Ġjong": 38678, "ĠGiul": 38679, "ĠADHD": 38680, "Ġhören": 38681, "Ġelo": 38682, "ivent": 38683, "Ġrus": 38684, "Ġoutrageous": 38685, "Ġmastered": 38686, "Ġ커": 38687, "ÙĪÙģ": 38688, "ipes": 38689, "ĠRudy": 38690, "Jacob": 38691, "Ġbullish": 38692, "Ġtapped": 38693, "Ġfaud": 38694, "izophren": 38695, "ĠÑģоÑħ": 38696, "ĠDarling": 38697, "Ġ1963": 38698, "ĠPrevention": 38699, "²Ķ": 38700, "Ġabdominal": 38701, "stones": 38702, "Ġavaient": 38703, "á»ķi": 38704, "make": 38705, "Ġsare": 38706, "ĠInstant": 38707, "кам": 38708, "Ġkeeper": 38709, "Ġblankets": 38710, "ãģ§ãģĹãĤĩãģĨ": 38711, "Ġsweats": 38712, "ĠMinneapolis": 38713, "åħ¨éĥ¨": 38714, "Ġgenommen": 38715, "Ġfasten": 38716, "ĠBrussels": 38717, "åij¼": 38718, "Ġcafeter": 38719, "Ġabsorbing": 38720, "Ġhago": 38721, "ĠElmo": 38722, "Ġgusto": 38723, "ĠYap": 38724, "Música": 38725, "Ġtert": 38726, "Ġbanda": 38727, "Ġmily": 38728, "Ġthereafter": 38729, "ĠStockholm": 38730, "ĠCarson": 38731, 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38848, "ÛĮا": 38849, "Ġdesem": 38850, "Ġважно": 38851, "ĠJU": 38852, "ĠìŀĪìŀĸìķĦìļĶ": 38853, "ĠÎĿ": 38854, "Ġinformations": 38855, "ĠHEL": 38856, "hst": 38857, "ĠпоговоÑĢ": 38858, "Ġvoiture": 38859, "Ġreus": 38860, "ändig": 38861, "ĠпоÑħож": 38862, "jing": 38863, "Ġdru": 38864, "altra": 38865, "Ġproduits": 38866, "Ġkite": 38867, "Ġeyeball": 38868, "ĠBelt": 38869, "ĠRestaurant": 38870, "Ġgamb": 38871, "Ġporridge": 38872, "itters": 38873, "Ġconverts": 38874, "Ġyardım": 38875, "Ġmáximo": 38876, "wirtschaft": 38877, "ĠíķĺëĤĺë": 38878, "Ġì¤Ģ": 38879, "Ġiceberg": 38880, "Ġvorbei": 38881, "Ġ256": 38882, "ocratic": 38883, "Ġreckless": 38884, "onner": 38885, "Ġmús": 38886, "Ġlogically": 38887, "ĠPrison": 38888, "ĠNetz": 38889, "Ġvacant": 38890, "Ġnimmt": 38891, "ĠHARR": 38892, "Ġзов": 38893, "ĠDee": 38894, "ringe": 38895, "niest": 38896, "ĠRules": 38897, "ìĬ¤ëŁ½": 38898, "cussions": 38899, "Ġfloral": 38900, "Ġconstrained": 38901, "Ġdifferentiation": 38902, "ĠQuebec": 38903, "ĠÛģÛĮÚº": 38904, 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"Ġliquids": 38960, "Ġcapacit": 38961, "arken": 38962, "Ġvagina": 38963, "Ġmashed": 38964, "Ġemerges": 38965, "yscy": 38966, "Ġunrelated": 38967, "ĠGuild": 38968, "Ġinverted": 38969, "itives": 38970, "Tra": 38971, "Ġbegr": 38972, "Ġalte": 38973, "ì§ķ": 38974, "ãĤģãģ¦": 38975, "ĠÑĢазÑĢабоÑĤ": 38976, "finder": 38977, "Ġдалее": 38978, "ĠблагодаÑĢ": 38979, "walker": 38980, "Ġcrater": 38981, "assadors": 38982, "rences": 38983, "inski": 38984, "ĠKIM": 38985, "ĠElliot": 38986, "2017": 38987, "ĠSr": 38988, "inka": 38989, "anov": 38990, "Ġìŀĺ못": 38991, "Ġproprietary": 38992, "displaystyle": 38993, "ĠÑģим": 38994, "Ġизб": 38995, "ĠPanel": 38996, "Ġinstincts": 38997, "ĠCommunications": 38998, "麻": 38999, "midt": 39000, "Ġë§Įëĵ¤ìĸ´": 39001, "ĠÑģлова": 39002, "ĠGilbert": 39003, "缮åīį": 39004, "Так": 39005, "voorbeeld": 39006, "еÑİÑģÑĮ": 39007, "aryn": 39008, "quez": 39009, "Ġdart": 39010, "ÑĸÑĪ": 39011, "ĠHut": 39012, "Sal": 39013, "Ġsoutheast": 39014, "Ġpesticides": 39015, "Ġhelicopters": 39016, 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"Ġattaching": 39074, "Hay": 39075, "Ġsuka": 39076, "ìį¹": 39077, "Ġpivotal": 39078, "ĠRespect": 39079, "ÃŃda": 39080, "IB": 39081, "ĠVerantwort": 39082, "wiet": 39083, "Ġforensic": 39084, "ÑĢиÑģÑĤ": 39085, "ĠпÑĢинÑĨипе": 39086, "Ġmarkings": 39087, "Ġkettle": 39088, "ĠOpera": 39089, "ĠDoctors": 39090, "Ġshredded": 39091, "Ġrecuer": 39092, "Ġvigil": 39093, "ĠFail": 39094, "Ġentrev": 39095, "ĠдÑĥÑĪ": 39096, "Ġoutbreaks": 39097, "èµ°åIJ§": 39098, "ĠÏĢο": 39099, "Ġrogue": 39100, "angled": 39101, "Ġyearly": 39102, "ĠCreed": 39103, "Ġwam": 39104, "Ġlotus": 39105, "ê³¼ë": 39106, "ãĢģãĢģ": 39107, "ĠSpit": 39108, "ĠItu": 39109, "Ġstrains": 39110, "Ġstamped": 39111, "Ġplaint": 39112, "Ġpotion": 39113, "Ġconsolidation": 39114, "è©ķ": 39115, "оÑĩкÑĥ": 39116, "Ġvlogging": 39117, "Ġslate": 39118, "ĠAuft": 39119, "ĠIncor": 39120, "ừng": 39121, "§IJ": 39122, "enh": 39123, "ĠheiÃŁ": 39124, "Ġdomest": 39125, "ĠStrom": 39126, "åį³": 39127, "akis": 39128, "Ġfragen": 39129, "Ġfiner": 39130, "ĠSug": 39131, 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39481, "ĠCritical": 39482, "ĠSinging": 39483, "ĠÑĦÑĥнк": 39484, "éŃĶ": 39485, "nova": 39486, "rebbe": 39487, "dt": 39488, "Unis": 39489, "Ġwebcam": 39490, "Ġcamoufl": 39491, "Ken": 39492, "Ġlawsuits": 39493, "ĠConsumer": 39494, "Ġrecoll": 39495, "Ġkleiner": 39496, "ĠFIFA": 39497, "Ġ1962": 39498, "èѦ": 39499, "Ġmalad": 39500, "Ġì°½": 39501, "ĠÃ¥t": 39502, "Ġinfluencer": 39503, "ĠArtist": 39504, "sti": 39505, "ãģªãĤĭãģ»ãģ©": 39506, "วย": 39507, "ysÅĤ": 39508, "ĠBian": 39509, "ĪëĦ¤": 39510, "Ġfireplace": 39511, "ĠApplication": 39512, "Ġmniej": 39513, "Ġacidic": 39514, "ĠMormon": 39515, "ssa": 39516, "åĭĻ": 39517, "Ġsneaky": 39518, "Ġojos": 39519, "Ġvoud": 39520, "ĠDai": 39521, "Ġgrassroots": 39522, "ĠUnbelievable": 39523, "ĠGabe": 39524, "ĠExtreme": 39525, "Ġhassle": 39526, "Ġcob": 39527, "mumbling": 39528, "Pass": 39529, "Į룬": 39530, "Ġsystematically": 39531, "Ġseventeen": 39532, "ÏĢει": 39533, "âĻ¡": 39534, "ĠкоÑĤ": 39535, "Ġsendiri": 39536, "Ġbathrooms": 39537, "ĠStern": 39538, 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"Ġê·¸ëĮĢë¡ľ": 41711, "ìĭ¤í": 41712, "Ġsimulated": 41713, "ĠëĦ¤ê°Ģ": 41714, "Ġbö": 41715, "ĠOtto": 41716, "LING": 41717, "Ġillustrates": 41718, "ĠDestroy": 41719, "Ġ1961": 41720, "ĠTagen": 41721, "Ġmelon": 41722, "ĠPascal": 41723, "QUE": 41724, "ĠполÑĥÑĩиÑĤÑĮ": 41725, "Ġincidence": 41726, "ĠStevens": 41727, "ĠGins": 41728, "rue": 41729, "Ġunreasonable": 41730, "ĠJie": 41731, "ysics": 41732, "Ġ몰ëĿ¼": 41733, "Ġfishes": 41734, "©´ì": 41735, "Ġprecurs": 41736, "ĠmogÄĻ": 41737, "tight": 41738, "eté": 41739, "Ġmundial": 41740, "ìĹĪëĭ¤": 41741, "âĢ¦!": 41742, "BU": 41743, "Ġsociology": 41744, "Ġbrutality": 41745, "Ġpersonaje": 41746, "ĠnÃŃvel": 41747, "Ġfazem": 41748, "Ġessen": 41749, "Ġdwelling": 41750, "Ġcommercially": 41751, "Ġedits": 41752, "Ġdues": 41753, "ĠGSA": 41754, "ìĿ¸ê°Ģ": 41755, "ĠíĹĪíĮĿ": 41756, "ĠYahoo": 41757, "енеÑĢ": 41758, "ìľ¨": 41759, "ÑĥÑĪки": 41760, "left": 41761, "Ġcaptive": 41762, "cipher": 41763, "Ġ×ŀ×ŀ×": 41764, "ĠгÑĢом": 41765, "Ġinnate": 41766, "Ġimpul": 41767, "ĠìŬìŀIJ": 41768, "Ġswallowed": 41769, "ĠTabii": 41770, "ìĿ´ìĭ": 41771, "ĠÑģоÑģÑĤав": 41772, "Ġoyun": 41773, "Ġobrigado": 41774, "ĠAph": 41775, "Katie": 41776, "Ġcena": 41777, "ĠAllÄģh": 41778, "ÙĪس": 41779, "Ġprzyp": 41780, "Ġpept": 41781, "Ġvoluntarily": 41782, "ĠOÄŁlum": 41783, "ĠElo": 41784, "oue": 41785, "Bir": 41786, "burger": 41787, "ĠSBS": 41788, "Ġ6000": 41789, "Ġpromotional": 41790, "ĠHerrn": 41791, "Ġstamping": 41792, "Ġqualifying": 41793, "Ġcosmos": 41794, "Ġafar": 41795, "æ±Ł": 41796, "abus": 41797, "Ġdads": 41798, "ãģŃãģĩ": 41799, "ĠÑįконом": 41800, "incarn": 41801, "Ġìĸ´ëĶ": 41802, "Ġлеж": 41803, "ĠBET": 41804, "Ġнайд": 41805, "onter": 41806, "Ġreusable": 41807, "Ġkomma": 41808, "ĠBij": 41809, "ĠTeraz": 41810, "ĠOlá": 41811, "ĠìķĦ침": 41812, "ĠÑĢазмеÑĢ": 41813, "awan": 41814, "Ġcarta": 41815, "æIJŀ": 41816, "iceless": 41817, "Ġsme": 41818, "ĠTutaj": 41819, "ĠÈĺi": 41820, "Ġprobation": 41821, "Ġadequately": 41822, "ĠPresidential": 41823, "indruck": 41824, "blade": 41825, 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"нÑıÑı": 41884, "Ġfleeing": 41885, "ĠMartinez": 41886, "ĠDreams": 41887, "kek": 41888, "Ġsociale": 41889, "ĠPlaza": 41890, "دة": 41891, "Ġkell": 41892, "ĠStellen": 41893, "felt": 41894, "ĠÑģпаÑģ": 41895, "ĠPv": 41896, "Ġcanción": 41897, "ĠHert": 41898, "ĠBalance": 41899, "Ġselves": 41900, "Ġvandaag": 41901, "Ġpry": 41902, "Ġnajle": 41903, "ĠвидиÑĤе": 41904, "Ġvelvet": 41905, "Ġgroot": 41906, "Ġfout": 41907, "模": 41908, "ĠSchulen": 41909, "ĠMohammed": 41910, "ĠCenters": 41911, "Ġhaver": 41912, "Ġfreuen": 41913, "¤íĬ¸": 41914, "лан": 41915, "POS": 41916, "inki": 41917, "Ġëĭµ": 41918, "Ġparalyzed": 41919, "GLISH": 41920, "Ġcasts": 41921, "ĠVC": 41922, "ìĿ´ìħĺ": 41923, "Ġتھ": 41924, "票": 41925, "Ġì¤ĺ": 41926, "Ġר×ķצ": 41927, "Ġsuced": 41928, "Ġprogresses": 41929, "ĠEÄŁer": 41930, "°ëıĦ": 41931, "Ġinstallations": 41932, "pedo": 41933, "еÑĢб": 41934, "interpret": 41935, "Ġê³łë¯¼": 41936, "ĠAzerbai": 41937, "ividades": 41938, "Ġì£ĦìĨ¡": 41939, "Ġentfer": 41940, "Ġchwil": 41941, "ĠHerbert": 41942, "ĠAlexandria": 41943, "yty": 41944, "Ġsechs": 41945, "Ġcaliber": 41946, "ĠWeise": 41947, "ĠHeck": 41948, "ĠYug": 41949, "ĠاÙĦØ·": 41950, "Ġpesar": 41951, "Ġcigar": 41952, "Ġmél": 41953, "Ġhaird": 41954, "Ġprzypadku": 41955, "Ġconfidently": 41956, "Ġanarch": 41957, "ĠGian": 41958, "Ġdobre": 41959, "cjÄĻ": 41960, "awy": 41961, "ĠRece": 41962, "ĠGobierno": 41963, "Ġcarga": 41964, "umsy": 41965, "Ġnorte": 41966, "Ġhandler": 41967, "Ġrespecting": 41968, "Ġallied": 41969, "ĠPiet": 41970, "ichtlich": 41971, "Ġolds": 41972, "Ġdusty": 41973, "Ġgry": 41974, "Ġ-...": 41975, "GHT": 41976, "Ġneo": 41977, "Ñĩики": 41978, "ежд": 41979, "aide": 41980, "ĠбÑĥло": 41981, "íį¼": 41982, "Ġtemporada": 41983, "Ġdoute": 41984, "âĺĨ": 41985, "ĠìĪł": 41986, "ĠJUSTIN": 41987, "auto": 41988, "Ġrationale": 41989, "prob": 41990, "Ġfishy": 41991, "Ġdoorway": 41992, "Ġemptiness": 41993, "еннаÑı": 41994, "Ġbrag": 41995, "ĠÐĵде": 41996, "çĪ¾": 41997, "Ġtransient": 41998, "Ġmittlerweile": 41999, "ĠBret": 42000, "Ġfij": 42001, "Ġdeposited": 42002, "NS": 42003, "ĠìķŀìĹIJ": 42004, "Ġkimse": 42005, "Ġcharities": 42006, "ĠMillenn": 42007, "dogs": 42008, "Ġmoyen": 42009, "Ġnuevos": 42010, "ĠCookie": 42011, "parable": 42012, "doing": 42013, "ĠSail": 42014, "Ġicy": 42015, "haba": 42016, "Ġqueens": 42017, "Ġchocolates": 42018, "ĠNay": 42019, "ĠÑĦин": 42020, "Ġvec": 42021, "Ġhelmets": 42022, "TM": 42023, "ĠArmed": 42024, "Ġimpairment": 42025, "ĠTus": 42026, "ĠMême": 42027, "omez": 42028, "ĠRequ": 42029, "ĠInvestig": 42030, "íİĺ": 42031, "Ġgolpe": 42032, "ĠRac": 42033, "igraph": 42034, "Ġkwest": 42035, "Ġsailors": 42036, "Ġstatutory": 42037, "Ġmilestones": 42038, "ĠMash": 42039, "ĠGesetzentwurf": 42040, "éĬ": 42041, "Ġcoloured": 42042, "huma": 42043, "Ġyere": 42044, "Ġsubtitles": 42045, "Ġembodied": 42046, "Ġmisschien": 42047, "ĠiPh": 42048, "ützen": 42049, "Ġdetached": 42050, "Ġdescrição": 42051, "ciamo": 42052, "Ġrecoil": 42053, "ĠÐŃÑĤоÑĤ": 42054, "Ġexported": 42055, "ĠAlone": 42056, "antry": 42057, 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42804, "ĠJJonak": 42805, "Ġtheatrical": 42806, "Ġinvinci": 42807, "Ġcommunion": 42808, "vocal": 42809, "Eh": 42810, "ĠDetails": 42811, "Ġstroll": 42812, "ĠRaymond": 42813, "ĠAmelia": 42814, "ij¥": 42815, "Ġprodukt": 42816, "Ġnuevas": 42817, "Ġmustn": 42818, "mayı": 42819, "colored": 42820, "dec": 42821, "Ġhjäl": 42822, "Ġsentimental": 42823, "Ġrealms": 42824, "Ġkrit": 42825, "Ġsext": 42826, "ĠPsychology": 42827, "èĪī": 42828, "hil": 42829, "ĠкоÑĢаб": 42830, "ĠëĤ´ìĿ¼": 42831, "ĠUnderstood": 42832, "ĠGuten": 42833, "Ġgangs": 42834, "Ġevenings": 42835, "æĢİ樣": 42836, "Ent": 42837, "ĠLegacy": 42838, "ĠCongo": 42839, "Ġdurchaus": 42840, "Ġbuoy": 42841, "erella": 42842, "WAN": 42843, "Pre": 42844, "ĠÑĢед": 42845, "ĠCrisis": 42846, "ãģªãģŁ": 42847, "ĠìĿ¼ìĿ´": 42848, "Ġmanuscripts": 42849, "еÑĤÑĢ": 42850, "Ġnonprofits": 42851, "Ġdictator": 42852, "Ġbaskets": 42853, "ĠIsh": 42854, "Ġperto": 42855, "Ġdatasets": 42856, "Ġample": 42857, "gebaut": 42858, "Ġcontributor": 42859, "Ġciao": 42860, 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"Ġplugging": 42975, "ĠKAR": 42976, "vl": 42977, "´ìĺ": 42978, "ĠRegierung": 42979, "ĠOczywiÅĽcie": 42980, "ĠEdgar": 42981, "Ġconductivity": 42982, "yelling": 42983, "vais": 42984, "adian": 42985, "Ġbulky": 42986, "ĠÑģÑĢав": 42987, "ĠпÑĢом": 42988, "Ġpaved": 42989, "Ġbends": 42990, "ĠSkillshare": 42991, "ĠMmmm": 42992, "ĠHorror": 42993, "Ġtumb": 42994, "Ġgoofy": 42995, "ĠMeow": 42996, "×Ļ׾×ķ": 42997, "ĠWass": 42998, "ĠScale": 42999, "ĠRak": 43000, "Ġprojecting": 43001, "Ġlinguistic": 43002, "ĠWorlds": 43003, "ensemble": 43004, "Ġpega": 43005, "stoppable": 43006, "Ġimbalance": 43007, "Ġø": 43008, "Ġthriller": 43009, "колÑĮкÑĥ": 43010, "Ġleftovers": 43011, "Ġcaveat": 43012, "ĠSTR": 43013, "undai": 43014, "Ġwatery": 43015, "ĠMarin": 43016, "ãĥ³ãĤ°": 43017, "Ġeggplant": 43018, "ĠJB": 43019, "ÙħÙĥÙĨ": 43020, "vidia": 43021, "ĠFIN": 43022, "icable": 43023, "Ġpodob": 43024, "Ġcohesive": 43025, "ĠVerfügung": 43026, "ĠPlato": 43027, "аÑĢиÑī": 43028, "Ġkot": 43029, "ĠÐŁÐ¾Ð¼": 43030, "ĠдокÑĥм": 43031, "Ġimplants": 43032, "issez": 43033, "Bre": 43034, "Ġgasps": 43035, "ĠTED": 43036, "rato": 43037, "JI": 43038, "Ġavenues": 43039, "ĠChong": 43040, "ladı": 43041, "رض": 43042, "Ġinici": 43043, "ĠSubaru": 43044, "æķħ": 43045, "éģĬæĪ²": 43046, "à¸ĭ": 43047, "Ġacht": 43048, "ĠArchitecture": 43049, "ĠвеÑīи": 43050, "ĠDevOps": 43051, "Ġtoppings": 43052, "Ġobsol": 43053, "aina": 43054, "ĠBangkok": 43055, "estruct": 43056, "Ġkob": 43057, "Ġëĵ¯": 43058, "ĠÑĢазнÑĭе": 43059, "Ġree": 43060, "Ġbijvoorbeeld": 43061, "ĠDemocracy": 43062, "à¹Ģรา": 43063, "ĠконÑĤ": 43064, "Ġseç": 43065, "Ġrahat": 43066, "Ġparliamentary": 43067, "ĠBash": 43068, "æĬĵ": 43069, "ziaÅĤ": 43070, "ITCH": 43071, "ĠBubble": 43072, "któ": 43073, "Whoa": 43074, "Ġflats": 43075, "æķĪ": 43076, "zne": 43077, "Ġservicio": 43078, "ĠDew": 43079, "Õ¸ÖĤ": 43080, "Ġunterstützen": 43081, "ĠWinds": 43082, "éĤ£ä¸ª": 43083, "ĠìĸĺëĬĶ": 43084, "Ġevaluations": 43085, "Ġreca": 43086, "Ġelves": 43087, "cheer": 43088, "Ġjal": 43089, "Ġrested": 43090, "Ġquienes": 43091, "ĠBrooke": 43092, "Ġë§ĪìĿĮìĹIJ": 43093, "Ġinten": 43094, "Ġoats": 43095, "Ġreferee": 43096, "Ġpneumonia": 43097, "Ġdelve": 43098, "peace": 43099, "eny": 43100, "Ġmostra": 43101, "ĠCannon": 43102, "ÏģοÏį": 43103, "ĠÐIJл": 43104, "Ġmonumental": 43105, "οÏįμε": 43106, "immers": 43107, "avian": 43108, "ĠделаеÑĤ": 43109, "Ġpitches": 43110, "ĠGrove": 43111, "Ġseminars": 43112, "Ġrécup": 43113, "ĠVoor": 43114, "Ġdeven": 43115, "ĠdB": 43116, "Ġboosting": 43117, "egan": 43118, "Ġwelt": 43119, "ĠGuatemala": 43120, "Ġmileage": 43121, "Ġbehand": 43122, "ĠWaar": 43123, "ĠSurf": 43124, "Ġcauliflower": 43125, "ĠTyr": 43126, "Ġmiteinander": 43127, "Ġdaring": 43128, "ĠSitting": 43129, "dled": 43130, "Ġresentment": 43131, "mÃ¤ÃŁig": 43132, "Ġfilmmaking": 43133, "warts": 43134, "thought": 43135, "ologique": 43136, "ĠCOR": 43137, "Ġaccounted": 43138, "Ġaper": 43139, "ĠINT": 43140, "olare": 43141, "Ġacompañ": 43142, "èŃĺ": 43143, "ĠÆ¡i": 43144, "ä¹Ŀ": 43145, "Ġmermaid": 43146, "ĠBentley": 43147, "atore": 43148, "Ġpren": 43149, "Ġethanol": 43150, "Ġastronomers": 43151, "seat": 43152, "keepers": 43153, "Ġexemption": 43154, "Ġamo": 43155, "ĠëĤĺìĦľ": 43156, "Ġinhal": 43157, "Ġbows": 43158, "ÑģкÑĥÑİ": 43159, "3000": 43160, "Ġfermentation": 43161, "Ġsinks": 43162, "Ġcomercial": 43163, "Ġstump": 43164, "Ġcele": 43165, "ĠSisters": 43166, "ĠRegister": 43167, "Ġsoort": 43168, "Ġnatomiast": 43169, "Ġ그림": 43170, "ĠÅŀey": 43171, "Ġhyped": 43172, "ĠRafael": 43173, "ĠEis": 43174, "ĠBasil": 43175, "ĠAssassin": 43176, "ĠAde": 43177, "rÃ¥n": 43178, "Ġonlar": 43179, "Ġmovimiento": 43180, "Ġadditionally": 43181, "Ġslit": 43182, "ĠChry": 43183, "ĠInterviewer": 43184, "׾ק": 43185, "Ġdisl": 43186, "Ġligger": 43187, "Ñĥки": 43188, "berish": 43189, "ĠÑĢÑıдом": 43190, "ARON": 43191, "],,": 43192, "Ġlumière": 43193, "Ġolvid": 43194, "Ġfreue": 43195, "ĠTing": 43196, "ĠKö": 43197, "Ġgeo": 43198, "Ġdyed": 43199, "ãģ§ãģį": 43200, "ÑĪей": 43201, "Ġżycie": 43202, "Ġie": 43203, "Ġtaxpayer": 43204, "ĠpeÅĤ": 43205, "Ġdécidé": 43206, "ĠcÅĵur": 43207, "Ġentwickelt": 43208, "ĠHQ": 43209, "KK": 43210, "odar": 43211, "Ġhone": 43212, "Ġconfiance": 43213, "Ġissuing": 43214, "Ġdiagnost": 43215, "ĠìŀĦ": 43216, "ĠкÑĢÑĥÑĤ": 43217, "ĠкаÑģ": 43218, "Ġþ": 43219, "Ġrestrictive": 43220, "ĠCastro": 43221, "ĠuÄŁ": 43222, "Ġempre": 43223, "ĠMoo": 43224, "ĠFigure": 43225, "phonetic": 43226, "Prof": 43227, "ĠпÑĢе": 43228, "Ġtilted": 43229, "ĠNegative": 43230, "ĠLimited": 43231, "meno": 43232, "lamation": 43233, "Ġtrustees": 43234, "Ġintensely": 43235, "Ġaçıl": 43236, "ĠUsed": 43237, "Ġzul": 43238, "Ġappreciative": 43239, "Ġtinc": 43240, "Ġconquest": 43241, "ĠعÙĨد": 43242, "Ġsuicidal": 43243, "Ġmulheres": 43244, "Ġdetach": 43245, "Ġkamera": 43246, "ĠAirPods": 43247, "INDISTINCT": 43248, "глий": 43249, "ĠëĥĦ": 43250, "Ġwrestle": 43251, "æ´Ĺ": 43252, "Ġfirearm": 43253, "Ġlire": 43254, "pra": 43255, "Ġjewels": 43256, "ĠCornell": 43257, "Ġíķłê²ĮìļĶ": 43258, "Ġsucker": 43259, "Ġnombreux": 43260, "ĠFerm": 43261, "ìĽIJìĿ´": 43262, "ĠPis": 43263, "ĠизÑĥÑĩ": 43264, "Ġmiten": 43265, "Ġcev": 43266, "ĠURLs": 43267, "ĠCAS": 43268, "Ġåı¯ä»¥": 43269, "finden": 43270, "Ġbravery": 43271, "ĠÑģлово": 43272, "Ġnenhuma": 43273, "Ġencuentra": 43274, "ĠShirley": 43275, "Ġpercept": 43276, "frames": 43277, "ĠRover": 43278, "ĠAlberta": 43279, "occ": 43280, "ĠëĿ¼ê³ł": 43281, "Ġsúper": 43282, "Ġpresume": 43283, "Ġgland": 43284, "Ġpacing": 43285, "Ġneurot": 43286, "Ġsno": 43287, "Ġplotted": 43288, "ĠpaÅĦstwa": 43289, "ĠOwner": 43290, "ĠDefence": 43291, "ridges": 43292, "Ġwallpaper": 43293, "onian": 43294, "Bro": 43295, "ĠAriana": 43296, "缴æİ¥": 43297, "kry": 43298, "Ġnarration": 43299, "Ġcriança": 43300, "ĠAlrighty": 43301, "ĠìĿ½": 43302, "Ġìĵ°ê³ł": 43303, "Ġliberated": 43304, "Ġexceeds": 43305, "Ġdominating": 43306, "Ġbakın": 43307, "lk": 43308, "Ġslapped": 43309, "ÐĹд": 43310, "umental": 43311, "gettable": 43312, "ĠRoz": 43313, "ĠGul": 43314, "ouvert": 43315, "Ġsmashing": 43316, "azuje": 43317, "Sir": 43318, "Ġgrated": 43319, "ä½łæľī": 43320, "ATT": 43321, "Ġarticulated": 43322, "Ġstora": 43323, "Ġextrater": 43324, "á»ī": 43325, "ÏĥÏī": 43326, "wir": 43327, "ĠMete": 43328, "Imp": 43329, "Ġhoor": 43330, "phase": 43331, "ĠÑĩÑĥд": 43332, "ĠбÑĢаÑĤ": 43333, "Ġidag": 43334, "Ġcinq": 43335, "Ġaparecer": 43336, "ĠICE": 43337, "åĪĹ": 43338, "Ġquieter": 43339, "Ġfalsch": 43340, "adic": 43341, "ĠплÑİÑģ": 43342, "ĠMenu": 43343, "uxe": 43344, "ĠTôi": 43345, "ĠMIL": 43346, "ĠHaj": 43347, "verbs": 43348, "Ġtubing": 43349, "Ġmachst": 43350, "Ġdall": 43351, "Ter": 43352, "Ġgelen": 43353, "Ġcucumbers": 43354, "Ġwidgets": 43355, "Ġdevrait": 43356, "Ġmike": 43357, "Ġintra": 43358, "íķŃ": 43359, "ĠÃħ": 43360, "ĠHund": 43361, "æ§ĭ": 43362, "quarter": 43363, "Ġew": 43364, "Ġkeluar": 43365, "Ġmats": 43366, "ĠTrick": 43367, "ĠInfinite": 43368, "ŀ¨": 43369, "Ġpeac": 43370, "ĠProte": 43371, "à¥Ī": 43372, "Ġ1700": 43373, "ĠRais": 43374, "à¹Ĭ": 43375, "ählt": 43376, "ifica": 43377, "aimer": 43378, "aÄĩ": 43379, "Ġakl": 43380, "ĠVolvo": 43381, "ĠTyson": 43382, "ĠRong": 43383, "irsin": 43384, "ĠâĻ¥": 43385, "Ġparody": 43386, "national": 43387, "pod": 43388, "ayd": 43389, "ambled": 43390, "Ġgovernmental": 43391, "Ġconfort": 43392, "icides": 43393, "Ġnasze": 43394, "ĠShepherd": 43395, "ĠKontakt": 43396, "Ġdisproportionately": 43397, "ĠклÑİÑĩ": 43398, "ĠtÃŃtulo": 43399, "Ġsina": 43400, "Ġcompositions": 43401, "ĠPF": 43402, "Ġverkl": 43403, "Ġsuivre": 43404, "Ġasta": 43405, "Ġstakeholder": 43406, "Ġsamma": 43407, "ĠBLACK": 43408, "Ġnodig": 43409, "Ġleva": 43410, "Ġjuegos": 43411, "Ġernst": 43412, "Ġbottoms": 43413, "ĠSignal": 43414, "Ġpollut": 43415, "Ġdura": 43416, "Musik": 43417, "Ġкомна": 43418, "ĠвÑģей": 43419, "alter": 43420, "ĠStef": 43421, "ĠBigQuery": 43422, "ĠVerantwortung": 43423, "Ġëĭ¹ìĹ°": 43424, "Ġquizz": 43425, "ĠLetter": 43426, "ĠInvestment": 43427, "ÑĪÑĤ": 43428, "IJëį°": 43429, "Ġencoding": 43430, "Ġtänker": 43431, "ĠKw": 43432, "annie": 43433, "åĭĿ": 43434, "110": 43435, "Ġzwy": 43436, "Ġ짧": 43437, "Ġdaw": 43438, "estä": 43439, "Ġdeceive": 43440, "ĠLänder": 43441, "isko": 43442, "Ġpodstaw": 43443, "ĠPharaoh": 43444, "쳤": 43445, "éĻIJ": 43446, "últ": 43447, "Ġtyö": 43448, "Ġmusimy": 43449, "質": 43450, "Ġpc": 43451, "ĠNT": 43452, "ĠCostco": 43453, "Ġå°ı": 43454, "ĠÏĥοÏħ": 43455, "Ġunin": 43456, "rounds": 43457, "Ġreminders": 43458, "Ġpuisqu": 43459, "Ġkrijgen": 43460, "Ġworkflows": 43461, "neten": 43462, "ĠëIJĺì§Ģ": 43463, "Ġsleek": 43464, "Ġcoworkers": 43465, "amientos": 43466, "Ġwitches": 43467, "baar": 43468, "eties": 43469, "Ġunnatural": 43470, "ĠSick": 43471, "ĠEfendi": 43472, "ãĥ³ãĥĢãĥĽ": 43473, "jcie": 43474, "Ġchamado": 43475, "ìĺĢìĬµëĭĪëĭ¤": 43476, "ĠprzedsiÄĻbior": 43477, "Ġbookstore": 43478, "Ġìŀłê¹IJ": 43479, "ĠSepar": 43480, "angi": 43481, "Evet": 43482, "Ġemergencies": 43483, "ĠXML": 43484, "нд": 43485, "¥´ë©´": 43486, "Ġê¿Ī": 43487, "Ġëĵ¤ê³ł": 43488, "Ġsut": 43489, "ĠWiz": 43490, "å±ķ": 43491, "Ġdynamically": 43492, "operation": 43493, "dot": 43494, "Ġinefficient": 43495, "clears": 43496, "Ġmundane": 43497, "ĠVeronica": 43498, "èĮ¶": 43499, "رت": 43500, "pose": 43501, "pai": 43502, "Ġnylon": 43503, "Ġaumentar": 43504, "ĠalltsÃ¥": 43505, "vak": 43506, "Ġcapacidad": 43507, "ĠWrestling": 43508, "Ġfertile": 43509, "Ġmég": 43510, "ĠNano": 43511, "аÑĤели": 43512, "Ġìĸ´ì©": 43513, "Ġtoca": 43514, "ĠEg": 43515, "âģ": 43516, "Ġì³": 43517, "luent": 43518, "Ġsolem": 43519, "Ġcinemat": 43520, "ĠQuel": 43521, "Ġorbits": 43522, "ĠHarm": 43523, "ricanes": 43524, "Ġblurred": 43525, "å¦Ĥä½ķ": 43526, "ĠاÙĦØ°ÙĬ": 43527, "Ġjin": 43528, "Ġgrenades": 43529, "Ġatroc": 43530, "Ġwherein": 43531, "Ġreplen": 43532, "ĠComics": 43533, "edaan": 43534, "Ġdenim": 43535, "Ġembarrassment": 43536, "ĠGomez": 43537, "ĠBusan": 43538, "ivities": 43539, "Ġsaliva": 43540, "Ġmerk": 43541, "Ġilgili": 43542, "ĠкÑĢÑĥг": 43543, "Ġoccupational": 43544, "ĠSahib": 43545, "Sta": 43546, "Ġadviser": 43547, "ĠTruly": 43548, "ĠYEAH": 43549, "ĠìŀĪëĬĶëį°ìļĶ": 43550, "zew": 43551, "baren": 43552, "Ġstol": 43553, "Ġbelongings": 43554, "ĠResearchers": 43555, "Ġefendim": 43556, "ÏħÏĩ": 43557, "ÅĤÄħcz": 43558, "ĠUng": 43559, "ĠJub": 43560, "Ġcerebral": 43561, "á»ĩu": 43562, "Ġצר": 43563, "ĠподаÑĢ": 43564, "Ġmarched": 43565, "Ġawaken": 43566, "Ġako": 43567, "Ġacept": 43568, "Ġinitiation": 43569, "è¯ī": 43570, "lot": 43571, "ĠwÅĤas": 43572, "ĠMongol": 43573, "utral": 43574, "Ġtentang": 43575, "Ġinversion": 43576, "ĠìĿ´íĽĦ": 43577, "Ġlok": 43578, "ÅĤbym": 43579, "RS": 43580, "Ġstos": 43581, "Ġinteracts": 43582, "ĠCalendar": 43583, "Ġvanish": 43584, "Ġphysiology": 43585, "Ġlinearly": 43586, "ĠJY": 43587, "ÄŁan": 43588, "funded": 43589, "iziert": 43590, "Ġzmian": 43591, "ĠGrill": 43592, "Ġunbelievably": 43593, "otechnology": 43594, "ĠCars": 43595, "ĠÙĨÛģ": 43596, "ĠFolge": 43597, "ĠBeverly": 43598, "äischen": 43599, "Ġaumento": 43600, "ìĽĮìĦľ": 43601, "Ġmailbox": 43602, "Ġsteeds": 43603, "ĠPeak": 43604, "å·§": 43605, "Ġwykor": 43606, "Ġprawda": 43607, "иÑĤÑĭ": 43608, "Ġdiscours": 43609, "Ġaccuse": 43610, "cesso": 43611, "uire": 43612, "Ġпопад": 43613, "Ġtha": 43614, "Ġmeasurable": 43615, "beeping": 43616, "ĠInnen": 43617, "ĠпÑıÑĤÑĮ": 43618, "Ġcompeted": 43619, "ĠItalians": 43620, "Ġencontra": 43621, "Ġniew": 43622, "Ġfiltration": 43623, "ĠпÑĢоÑĦеÑģÑģ": 43624, "Ġpajamas": 43625, "Ġcilantro": 43626, "ĠSoc": 43627, "Luc": 43628, "Ġê¹Ģë": 43629, "ĠOdd": 43630, "Ġhydration": 43631, "мов": 43632, "Ġplywood": 43633, "ĠCompetition": 43634, "изнеÑģ": 43635, "flight": 43636, "ĠBeit": 43637, "bourg": 43638, "Ġcoils": 43639, "Ġcâmera": 43640, "Ġamended": 43641, "Äģm": 43642, "Angel": 43643, "ĠStacy": 43644, "flo": 43645, "Ġnormale": 43646, "Ġconsonant": 43647, "Ġaccompanying": 43648, "кÑĸ": 43649, "Ġirritated": 43650, "ĠfÃ¥tt": 43651, "Ġcrocodile": 43652, "IJĺëĬĶ": 43653, "Ġalbeit": 43654, "ĠPhilosophy": 43655, "ç´¯": 43656, "ÅĨ": 43657, "ytic": 43658, "Ġrèg": 43659, "Ġfrança": 43660, "Ġattentive": 43661, "Ham": 43662, "Ġalrededor": 43663, "æĿ¿": 43664, "sei": 43665, "ĠÑģвид": 43666, "Ġgimbal": 43667, "Ġchina": 43668, "ĠðŁİ¶": 43669, "ĠÐĴам": 43670, "Ġstimulating": 43671, "ĠOra": 43672, "ytes": 43673, "Ġheft": 43674, "Ġhaters": 43675, "Ġcomplexes": 43676, "Ġ03": 43677, "ród": 43678, "clear": 43679, "Ġbesteht": 43680, "çķĻè¨Ģ": 43681, "wny": 43682, "moil": 43683, "Ġsloppy": 43684, "Ġinsignificant": 43685, "Ġdubbed": 43686, "Ġëĸł": 43687, "Ġconsigo": 43688, "лÑĥÑĪай": 43689, "Sn": 43690, "Ġ×Ķצ": 43691, "ĠÎĮ": 43692, "Ġnadzie": 43693, "Ġfreshmen": 43694, "taa": 43695, "ĠuwagÄĻ": 43696, "ĠFavorite": 43697, "ĠCriminal": 43698, "Ġeviden": 43699, "Ġsymb": 43700, "Les": 43701, "ĠBeau": 43702, "uned": 43703, "plement": 43704, "Ac": 43705, "Ġdermat": 43706, "ĠNolan": 43707, "Ñĭп": 43708, "Ġsitt": 43709, "Ġeverlasting": 43710, "Ġestavam": 43711, "Ġмик": 43712, "Ġkhác": 43713, "Ġinvit": 43714, "Ġtreble": 43715, "Ġjig": 43716, "mani": 43717, "Ġtuvo": 43718, "ĠRUS": 43719, "ĠErde": 43720, "ĠDziÄĻkujÄĻ": 43721, "Ġblueberries": 43722, "kell": 43723, "acions": 43724, "çĪ·": 43725, "ви": 43726, "LET": 43727, "Ġsprout": 43728, "Ġspor": 43729, "Ġbên": 43730, "ĠMona": 43731, "ĠContain": 43732, "ĠKeys": 43733, "озÑı": 43734, "Ġfunción": 43735, "Ġrappelle": 43736, "Ġevolves": 43737, "Ġscraping": 43738, "Ġcomentários": 43739, "Ġpratique": 43740, "Ġauxiliary": 43741, "ĠSponge": 43742, "Ñģким": 43743, "uvo": 43744, "ĠÑģамо": 43745, "Ġsank": 43746, "Ġhighways": 43747, "Ġinventions": 43748, "Ġиногда": 43749, "Ġcreatively": 43750, "Ġbenchmarks": 43751, "oncé": 43752, "alal": 43753, "Ġsotto": 43754, "Ġcalves": 43755, "ĠMov": 43756, "Ġlavender": 43757, "Ġeyeballs": 43758, "Ġawaiting": 43759, "ĠPaty": 43760, "ÙĦÙĩ": 43761, "Ġembroidery": 43762, "Ġduh": 43763, "Ġcamar": 43764, "ĠBOB": 43765, "Ġspaced": 43766, "ĠgÅĤos": 43767, "аемÑģÑı": 43768, "Ġescapes": 43769, "ĠRogue": 43770, "zcz": 43771, "èŀ": 43772, "¬ë¥¼": 43773, "ĠMoże": 43774, "ĠеÑģÑĤе": 43775, "ĠBurada": 43776, "éĮ²": 43777, "wd": 43778, "uuuu": 43779, "Ġsash": 43780, "ĠLub": 43781, "Ġnotebooks": 43782, "Ġmae": 43783, "Ġconflicting": 43784, "Ġsummertime": 43785, "acas": 43786, "Ġbauen": 43787, "blowing": 43788, "ạo": 43789, "Ġìĸ¸ìłľ": 43790, "ä»ĬæĹ¥ãģ¯": 43791, "ĠSenhor": 43792, "ĠiPhones": 43793, "ĠQuarter": 43794, "ĠìłľëĮĢë¡ľ": 43795, "uÃŁ": 43796, "Ġë§Ī무ë": 43797, "Ġsettlers": 43798, "Ġcrest": 43799, "Ġtransc": 43800, "æĽ¾": 43801, "Ġriots": 43802, "Ġclones": 43803, "ĠOprah": 43804, "ίζ": 43805, "Ġpals": 43806, ".......": 43807, "ãģĶãģĸãģĦãģ¾ãģĻ": 43808, "ĠÑĢоÑģÑģ": 43809, "ĠLaser": 43810, "Ġzaczy": 43811, "Ġsevi": 43812, "Ġregeneration": 43813, "ìĹ¼": 43814, "would": 43815, "Ġüzerine": 43816, "ĠStraÃŁe": 43817, "Ġvengeance": 43818, "Ġrer": 43819, "ĠSafari": 43820, "ĠHEY": 43821, "çķ«": 43822, "Ġsacar": 43823, "Ġimagem": 43824, "ĠBundest": 43825, "mesan": 43826, "ĠPaste": 43827, "Ġsizz": 43828, "ĠпоÑģÑĤÑĥп": 43829, "×Ķ×ķ": 43830, "trad": 43831, "Ġfrançaise": 43832, "ĠBou": 43833, "Ġbarre": 43834, "ĠZhi": 43835, "ĠGeez": 43836, "ihad": 43837, "Ġreconoc": 43838, "Ġpelig": 43839, "Ġindices": 43840, "Ġë°ĶëĢ": 43841, "Ġconduction": 43842, "Ġìķħ": 43843, "Ġzeker": 43844, "Ġfum": 43845, "ĠWür": 43846, "breaker": 43847, "Ġsprite": 43848, "Crowd": 43849, "Ġopener": 43850, "Ġolv": 43851, "Ġbuenas": 43852, "ĠSilk": 43853, "ĠHIM": 43854, "kop": 43855, "compl": 43856, "Ġpossono": 43857, "³Ģ": 43858, "Ġoscillator": 43859, "ĠSith": 43860, "èĥ¡": 43861, "ажи": 43862, "Ġraft": 43863, "hall": 43864, "Ġschneller": 43865, "Ġimporting": 43866, "Ġassembling": 43867, "Ġubiqu": 43868, "Ġactivates": 43869, "acci": 43870, "ĵľë¥¼": 43871, "Ġcomposers": 43872, "ĠACL": 43873, "Conf": 43874, "Ġì½ĺ": 43875, "ĠнекоÑĤоÑĢÑĭе": 43876, "Ġcandies": 43877, "åĬłåħ¥": 43878, "ĠMuss": 43879, "à¹ĥà¸Ĭ": 43880, "Ġduda": 43881, "ником": 43882, "meden": 43883, "Ġìĸ´ëķĮ": 43884, "ĠYeshua": 43885, "zag": 43886, "hodou": 43887, "Ġaloud": 43888, "ĠPalmer": 43889, "imize": 43890, "ãĤ·ãĥ§": 43891, "Ġmaritime": 43892, "Ġcommunal": 43893, "Ġbadges": 43894, "Ġrugby": 43895, "Ġmarshmallow": 43896, "Ġfiery": 43897, "Ġaccountant": 43898, "Ġabla": 43899, "ĠMonroe": 43900, "ĠFont": 43901, "ĠBoost": 43902, "ĠBarnes": 43903, "answer": 43904, "ĠBurning": 43905, "Ġä¸įæĺ¯": 43906, "Ġangef": 43907, "ĠWesley": 43908, "lls": 43909, "ìµ": 43910, "ש׾": 43911, "iliÅĽmy": 43912, "×IJף": 43913, "amura": 43914, "ĠFuj": 43915, "Ġpani": 43916, "ĠTrop": 43917, "arbeiten": 43918, "Ġrue": 43919, "ĠRare": 43920, "ängen": 43921, "ĠÑģмоÑĤÑĢеÑĤÑĮ": 43922, "ĠÐļаÑĢ": 43923, "ĠMTV": 43924, "boarding": 43925, "][": 43926, "ĠëłĪë": 43927, "stanbul": 43928, "pielt": 43929, "ĠHardy": 43930, "ĠEngagement": 43931, "ĠDienst": 43932, "Ġwären": 43933, "Ġfuego": 43934, "Ġestruct": 43935, "Ġcalam": 43936, "ĠResponse": 43937, "ĠãĤĦ": 43938, "ĠMohammad": 43939, "Ġresisting": 43940, "Ġdurant": 43941, "èģ¯": 43942, "åĨµ": 43943, "ĠOLED": 43944, "Ġverz": 43945, "män": 43946, "ĠÙĨÛĴ": 43947, "Ġparanoid": 43948, "ĠAware": 43949, "ĠEngineers": 43950, "Ġprocedural": 43951, "Ġpersonnage": 43952, "Ġfarklı": 43953, "é¡Ĩ": 43954, "flowing": 43955, "ĠмеÑģÑĤа": 43956, "ĠBare": 43957, "istem": 43958, "ĠpoczÄħtku": 43959, "Ġpersonajes": 43960, "Ġìĸ´ëłµ": 43961, "Ńī": 43962, "ĠХоÑĤÑı": 43963, "Ġunsett": 43964, "ĠAbsol": 43965, "Ġấy": 43966, "ĠMAYOR": 43967, "полне": 43968, "Ġinforming": 43969, "Ġamps": 43970, "ÐŁÑĢ": 43971, "ĠëŃĶ": 43972, "aeda": 43973, "Ġ×Ķ×ij×": 43974, "ấn": 43975, "kelijk": 43976, "Ġatheist": 43977, "Ġtrout": 43978, "Ġneues": 43979, "ĠNokia": 43980, "machen": 43981, "Ġwholesale": 43982, "ırd": 43983, "Ins": 43984, "ĠÑįп": 43985, "Ġprick": 43986, "ĠKindern": 43987, "à¸Ĺำ": 43988, "Ġclassy": 43989, "Ġînt": 43990, "ĠShopify": 43991, "ĠÑģоÑĢ": 43992, "ĠзакÑĢÑĭ": 43993, "zuk": 43994, "Ġuniversally": 43995, "Ġteaspoons": 43996, "Ġrecount": 43997, "ĠnÃ¥gonting": 43998, "ĠXue": 43999, "isième": 44000, "Ġweakest": 44001, "ĠteÅŁekkür": 44002, "Ġmathematically": 44003, "ĠHos": 44004, "Ġíķľëĭ¤": 44005, "Ġpartager": 44006, "ĠDarr": 44007, "êº": 44008, "Ġεκ": 44009, "Ġgerms": 44010, "Ġgelir": 44011, "Ġdul": 44012, ",-": 44013, "Ġìĸ¸ë": 44014, "Ġ×ŀצ": 44015, "ĠÑıÑĢ": 44016, "Ġquotid": 44017, "Ġprzysz": 44018, "Ġhardness": 44019, "Ġaquatic": 44020, "ĠJungle": 44021, "ĠPCR": 44022, "ĠEliot": 44023, "Ġostr": 44024, "Ġmapa": 44025, "essä": 44026, "ĠGIR": 44027, "ĠDriving": 44028, "ĠSami": 44029, "ĠMedien": 44030, "ĠCompanies": 44031, "ĠPharm": 44032, "seits": 44033, "ĠRim": 44034, "ĠοÏĢο": 44035, "Ġweiteren": 44036, "Ġpizzas": 44037, "ĠLydia": 44038, "ĠHeights": 44039, "Ġsincerity": 44040, "Ġnossas": 44041, "ĠdÅĤ": 44042, "Ġalarming": 44043, "ĠCauc": 44044, "ĠÑģмÑĭÑģ": 44045, "facing": 44046, "bags": 44047, "WW": 44048, "ĠØ´ÙĬ": 44049, "Ġcourtroom": 44050, "ĠPhillip": 44051, "Ġê²ĥì²ĺëŁ¼": 44052, "ĠSpieler": 44053, "ãĤıãģĭ": 44054, "Ġkant": 44055, "Ġadmitting": 44056, "ãĥģãĥ£ãĥ³ãĥįãĥ«": 44057, "Ġcontainment": 44058, "å¼ł": 44059, "Ġremovable": 44060, "Ġjumper": 44061, "focused": 44062, "ĠиÑĤоге": 44063, "ĠТем": 44064, "Ġvase": 44065, "ĠUSC": 44066, "ĠMonate": 44067, "ĠJacobs": 44068, "ĠHOL": 44069, "iked": 44070, "erweise": 44071, "Ġgoodies": 44072, "Ġhomage": 44073, "׼ש×Ļ×ķ": 44074, "Ġquais": 44075, "Ġinicial": 44076, "Ġguarding": 44077, "Ġdazz": 44078, "Ġcombos": 44079, "ĠÑĥпÑĢав": 44080, "ĠTalent": 44081, "å¥ĩæĢª": 44082, "Ġór": 44083, "Ġintermittent": 44084, "ĠMcCarthy": 44085, "Ġspans": 44086, "Ġtyre": 44087, "Ġquy": 44088, "èĪĪ": 44089, "jut": 44090, "ĠZent": 44091, "Ġgat": 44092, "大åĵ¥": 44093, "Ġscaffold": 44094, "Ġnecesario": 44095, "ĠZahlen": 44096, "ĠSAND": 44097, "ĠPU": 44098, "Everything": 44099, "----------------": 44100, "ĠвзÑıÑĤÑĮ": 44101, "Ġsparks": 44102, "Ġpendulum": 44103, "×ŀף": 44104, "Ġìĥīê¹": 44105, "Ġmultiplier": 44106, "Ġладно": 44107, "urat": 44108, "Ġupsetting": 44109, "è¡Ģ": 44110, "bak": 44111, "ĠìµľëĮĢ": 44112, "Ġanál": 44113, "ĠJOE": 44114, "Ġkosten": 44115, "ĠPatty": 44116, "ĠGuin": 44117, "cked": 44118, "ĠEgyptians": 44119, "ĠCitizens": 44120, "ר׼": 44121, "ĠÐķÑīе": 44122, "Ġйого": 44123, "Ġsnowfl": 44124, "Ġlekker": 44125, "Ġacost": 44126, "ĠBabe": 44127, "Ġgamble": 44128, "Ġadjective": 44129, "кими": 44130, "oys": 44131, "Ġmontre": 44132, "ĠHyundai": 44133, "Ġmoisturizing": 44134, "Ġmozzarella": 44135, "OOO": 44136, "Ġfacult": 44137, "Ġdoet": 44138, "Ġfearless": 44139, "Ġespresso": 44140, "Ġallora": 44141, "ĠCinc": 44142, "ãĥ¼ãĤ¸": 44143, "Ġconteúdo": 44144, "ĠPelosi": 44145, "Ġminder": 44146, "root": 44147, "Ġíķłë": 44148, "Ġпад": 44149, "ĠCalling": 44150, "ĠConfig": 44151, "ĠConsole": 44152, "insky": 44153, "énergie": 44154, "Ġsolitary": 44155, "оде": 44156, "Ġguarded": 44157, "160": 44158, "ĠпÑģиÑħ": 44159, "ĠShap": 44160, "Ġtitre": 44161, "ologne": 44162, "ĠпаÑĢÑĥ": 44163, "ĠPRE": 44164, "ãĥ¼ãĥī": 44165, "Ġln": 44166, "ĠMitgl": 44167, "ĠCarry": 44168, "Ġspind": 44169, "ĠCanton": 44170, "Ġkingdoms": 44171, "remo": 44172, "Ġraging": 44173, "Ġincapable": 44174, "ĠWR": 44175, "åĨįè§ģ": 44176, "ĠÑģобÑģÑĤвен": 44177, "ĠкакиÑħ": 44178, "ĠSHE": 44179, "ëĭ¹íŀĪ": 44180, "Ġscarcity": 44181, "Ġperde": 44182, "Ġexits": 44183, "ĠSinger": 44184, "Ġsupper": 44185, "Ġmunicipality": 44186, "ĠDiversity": 44187, "Ġtiro": 44188, "iels": 44189, "ĠlÃŃder": 44190, "Ġbluff": 44191, "Ġatra": 44192, "lys": 44193, "Ġmahd": 44194, "Ġcódigo": 44195, "ĠHarlem": 44196, "rule": 44197, "icity": 44198, "Ġsimplistic": 44199, "ĠKonst": 44200, "åģ¥": 44201, "ELLI": 44202, "Ġförsta": 44203, "Ġconstitutes": 44204, "ĠÑģÑĤоÑĢонÑĥ": 44205, "Ġurged": 44206, "ĠPanda": 44207, "ì°¨ë": 44208, "rece": 44209, "Ġpatriot": 44210, "ĠCrush": 44211, "Ġwink": 44212, "ойÑĤи": 44213, "urança": 44214, "Ġseizures": 44215, "Ġelectrod": 44216, "ĠDonkey": 44217, "ĠIU": 44218, "ĠMOS": 44219, "Ġalkal": 44220, "ì´ī": 44221, "besondere": 44222, "Ġparallels": 44223, "Ġbitterness": 44224, "ättre": 44225, "essional": 44226, "Ġsoybean": 44227, "Ġcollab": 44228, "ĠReporting": 44229, "å§Ķ": 44230, "Ġкомпании": 44231, "Ġwszyscy": 44232, "ĠCrunch": 44233, "iseen": 44234, "Ġambassadors": 44235, "ĠChev": 44236, "åįĪ": 44237, "овÑĭе": 44238, "sca": 44239, "ĠÑĢеÑĪил": 44240, "оÑĤо": 44241, "Ġgleichzeitig": 44242, "mern": 44243, "üst": 44244, "ĠHae": 44245, "³´ê²łìĬµëĭĪëĭ¤": 44246, "Ġshores": 44247, "Ġdepress": 44248, "Ġahor": 44249, "ĠSteuer": 44250, "ahh": 44251, "Ġrevise": 44252, "ĠÑģамÑĭе": 44253, "jat": 44254, "Ġherbal": 44255, "Ġcuánt": 44256, "Ġbuna": 44257, "niejsze": 44258, "Finally": 44259, "×ķ×ĸ": 44260, "cje": 44261, "ĠìŀĪê±°ëĵłìļĶ": 44262, "ĠëĤĺëĪ": 44263, "Ġprzest": 44264, "ãĥ¼ãĥł": 44265, "lica": 44266, "ĠDuch": 44267, "å°įå°į": 44268, "ÑĸйÑģÑĮ": 44269, "passen": 44270, "Ġsatisfies": 44271, "ĠAdditional": 44272, "Ġcámara": 44273, "еÑĩение": 44274, "Ġpomp": 44275, "Ġë§IJìĿ´": 44276, "ĠMills": 44277, "евид": 44278, "Ġrespectable": 44279, "Ġfilament": 44280, "Ġvender": 44281, "Ġmattered": 44282, "oure": 44283, "층": 44284, "Korean": 44285, "Ġestudio": 44286, "Ġcactus": 44287, "ĠVive": 44288, "ĠRag": 44289, "Ġcompliqué": 44290, "ĠÙĪÛģ": 44291, "Ġtao": 44292, "¦¿": 44293, "Since": 44294, "Ġjeopard": 44295, "ĠSell": 44296, "åºĶ": 44297, "ĠìĺĽ": 44298, "Ġketo": 44299, "Ġintelig": 44300, "ĠAngeb": 44301, "Ġtiden": 44302, "Ġsocio": 44303, "Ġreminiscent": 44304, "Ġcaregiver": 44305, "Space": 44306, "ĠExercise": 44307, "ĠBecome": 44308, "êts": 44309, "akk": 44310, "!..": 44311, "ĠÑģпÑĢоÑģ": 44312, "ĠαÏĢο": 44313, "Ġshootings": 44314, "Ġape": 44315, "ĠSammy": 44316, "ĠKung": 44317, "Ġcuál": 44318, "ĠLup": 44319, "æĿŁ": 44320, "ä¾ĨåĪ°": 44321, "ĠÑģÑĤÑĥд": 44322, "Ġsweeter": 44323, "Ġcomum": 44324, "ĠAds": 44325, "hyung": 44326, "ĠбÑĥдÑĥÑī": 44327, "Ġwaffle": 44328, "ĠOrb": 44329, "Ġlaut": 44330, "Ġforecasting": 44331, "åª": 44332, "Ġrapping": 44333, "Ġprefers": 44334, "Ġbenz": 44335, "Ġnik": 44336, "ĠBahn": 44337, "Ġsanding": 44338, "Ġimminent": 44339, "ĠпÑĢоблемÑĭ": 44340, "Ġdoivent": 44341, "ола": 44342, "Ġżycia": 44343, "ihu": 44344, "Ġexistem": 44345, "ĠInterior": 44346, "ĠTakes": 44347, "Ġtoddler": 44348, "Ġdictatorship": 44349, "ĠSmithson": 44350, "ĠAllahu": 44351, "ÏİÏģα": 44352, "ìķĺìĬµëĭĪëĭ¤": 44353, "ĠVote": 44354, "ĠSmells": 44355, "одно": 44356, "Ġhindsight": 44357, "VR": 44358, "ĠPatch": 44359, "ĠJahres": 44360, "Ġsouvenir": 44361, "Ġneutron": 44362, "Ġlongtime": 44363, "Ġsayin": 44364, "ä¹IJ": 44365, "asaki": 44366, "ĠоÑģÑĤанов": 44367, "Ġexpelled": 44368, "Ġcryptocurrencies": 44369, "ĠMurder": 44370, "ĠCitizen": 44371, "WAY": 44372, "Ġplu": 44373, "Ġlemonade": 44374, "Ġconveniently": 44375, "ĠHI": 44376, "Ġ2023": 44377, "ש×ķת": 44378, "аÑĨион": 44379, "Ġ뼰": 44380, "ĠÙĦÙĥÙĨ": 44381, "Ġнемножко": 44382, "Ġunused": 44383, "Ġmaioria": 44384, "Ġastrology": 44385, "ĠDownt": 44386, "Nick": 44387, "Ġpreoccup": 44388, "Ġdemain": 44389, "×ŀ×¢": 44390, "ĠводÑĭ": 44391, "ĠSanskrit": 44392, "Ġprêt": 44393, "Ġstranded": 44394, "Ġrefin": 44395, "ĠпÑĢиним": 44396, "ĠповеÑĢÑħ": 44397, "à¯į?": 44398, "Ġzrob": 44399, "Ġintertw": 44400, "ĠDavidson": 44401, "лена": 44402, "ĠпонÑıÑĤÑĮ": 44403, "ĠReno": 44404, "ĠполÑĥÑĩилоÑģÑĮ": 44405, "Ġcorrespondent": 44406, "ĠUran": 44407, "else": 44408, "··": 44409, "Ġtutoring": 44410, "Ġgranddaughter": 44411, "luded": 44412, "Ġstesso": 44413, "Ġhết": 44414, "Ġgegangen": 44415, "ĠÐĿÐIJ": 44416, "Ġantig": 44417, "background": 44418, "Ġgedaan": 44419, "Ġfavored": 44420, "ĠEmmanuel": 44421, "Ġiod": 44422, "Ġclamps": 44423, "Ġcomple": 44424, "ĠAdvance": 44425, "ĠìŀĪê³łìļĶ": 44426, "ĠRox": 44427, "ĠìĹIJë": 44428, "Ġintestines": 44429, "Ġpercussion": 44430, "Ġlegitimately": 44431, "ĠEternal": 44432, "family": 44433, "alog": 44434, "Brad": 44435, "ениÑĤÑĮ": 44436, "ĠÑģнаÑĩала": 44437, "Ġcerta": 44438, "Ġakkor": 44439, "ĠεδÏİ": 44440, "Ġoctave": 44441, "ĠVac": 44442, "моÑĤÑĢи": 44443, "ĠÃītats": 44444, "Ġlongue": 44445, "Ġdissoci": 44446, "ÑĢÑıд": 44447, "hein": 44448, "Ġpantalla": 44449, "Ġindications": 44450, "ĠLt": 44451, "ĠGrade": 44452, "è£Ŀ": 44453, "oine": 44454, "bug": 44455, "ĠVerizon": 44456, "ĠAlém": 44457, "Ġviennent": 44458, "ĠÑĩиÑģÑĤ": 44459, "ĠBeni": 44460, "ĠTsch": 44461, 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44633, "Ġpartis": 44634, "ĠAnimation": 44635, "§Īë": 44636, "Cre": 44637, "ötzlich": 44638, "Ġmagg": 44639, "Ġclumsy": 44640, "Ġbottlene": 44641, "Ġbirlikte": 44642, "ĠGamb": 44643, "Ġ׼ף": 44644, "Ġmetropolitan": 44645, "该": 44646, "æİĴ": 44647, "Ooh": 44648, "Ġobjections": 44649, "ĠÙħت": 44650, "Ġмел": 44651, "Ġremnants": 44652, "ĠXavier": 44653, "Rich": 44654, "Ġolsa": 44655, "ĠPill": 44656, "Ġgroans": 44657, "ĠNaruhodou": 44658, "ĠContract": 44659, "ада": 44660, "nai": 44661, "ĠÑĦиз": 44662, "Ġops": 44663, "ạt": 44664, "Ġparachute": 44665, "Ġnell": 44666, "ĠEntscheidung": 44667, "׾×Ļ×Ŀ": 44668, "Ġtruthful": 44669, "Ġsharper": 44670, "Ġbureaucracy": 44671, "cart": 44672, "ĠинÑĤ": 44673, "wiek": 44674, "Ġwillingly": 44675, "ĠHerman": 44676, "Ġmehrere": 44677, "Ġelites": 44678, "ĠArmor": 44679, "ãĥĪãĥŁãĥ¼": 44680, "Ġembora": 44681, "ĠRecogn": 44682, "ĠлÑİблÑİ": 44683, "ĠExcellence": 44684, "ibel": 44685, "Ġexporting": 44686, "ì²´ìłģ": 44687, "Kelly": 44688, "Cameraman": 44689, 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44803, "ĠÐijÑĭ": 44804, "Ġnovelty": 44805, "pound": 44806, "Ġweirdest": 44807, "Ġbois": 44808, "émie": 44809, "upl": 44810, "ATA": 44811, "Ġtehd": 44812, "ĠNir": 44813, "sınız": 44814, "!\",": 44815, "åijĬè¯ī": 44816, "Ġimmort": 44817, "Ġelk": 44818, "аниÑĩ": 44819, "Ġfabrication": 44820, "ĠNoise": 44821, "ĠAvant": 44822, "رÛĮ": 44823, "wat": 44824, "Ġwhooshing": 44825, "Ġ׼×Ļ": 44826, "ĠÐĹнаÑĩиÑĤ": 44827, "Ġcentrif": 44828, "ansing": 44829, "Sound": 44830, "ĠëĿ¼ë": 44831, "Ġcaptions": 44832, "à³į": 44833, "Ġorgas": 44834, "Ġdolphins": 44835, "ĠBlend": 44836, "ĠTaj": 44837, "ĠCCTV": 44838, "Ġinom": 44839, "Ġeditions": 44840, "Ġburnout": 44841, "Ġbättre": 44842, "ĠCasa": 44843, "ovich": 44844, "Ġmolten": 44845, "Ġblindfold": 44846, "ĠGue": 44847, "æŶéĹ´": 44848, "Ġspinner": 44849, "Ġmöglichst": 44850, "ĠVÃł": 44851, "eneca": 44852, "Ġmédico": 44853, "å¹¹åĺĽ": 44854, "ástico": 44855, "Ġard": 44856, "ĠSundays": 44857, "ĠRemote": 44858, "Ġìĸ¼ë§Ī": 44859, "ĠtrÆ°á»Ľc": 44860, "ìħ¨ë": 44861, 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"Ġhoof": 44974, "ĠBullet": 44975, "imagin": 44976, "׳×Ļ×Ŀ": 44977, "Ġengagements": 44978, "ĠBlues": 44979, "Ġañad": 44980, "Ġfps": 44981, "Ġcaterp": 44982, "Ġsá»ij": 44983, "ĠTribe": 44984, "ç¶ļ": 44985, "пон": 44986, "iferation": 44987, "Ġrumah": 44988, "ĠPunj": 44989, "lab": 44990, "Ġcomprehension": 44991, "bringing": 44992, "Wo": 44993, "Ġtik": 44994, "Ġanyhow": 44995, "以åīį": 44996, "áticas": 44997, "Ġsitzen": 44998, "Ġkolay": 44999, "ĠConfederate": 45000, "ĠCalled": 45001, "Ġnaszych": 45002, "ĠdziÄĻki": 45003, "Ġcloak": 45004, "ĠGoog": 45005, "ĠAshe": 45006, "象": 45007, "enan": 45008, "ĠмÑĭÑĪ": 45009, "ĠвеÑĤ": 45010, "ĠSpo": 45011, "ĠSket": 45012, "ĠHenderson": 45013, "ilah": 45014, "ĠбезопаÑģ": 45015, "Ġsekali": 45016, "ìĸ´ê°Ģ": 45017, "Ġsnare": 45018, "Ġrằng": 45019, "Ġförsö": 45020, "szych": 45021, "Ġübers": 45022, "Ġstratég": 45023, "ĠìºIJë": 45024, "Ġrappers": 45025, "Ġcep": 45026, "ĠHasta": 45027, "Ġhorribly": 45028, "Ġfrüh": 45029, "Ġبع": 45030, "Ġmantle": 45031, "ãĢħ": 45032, "funding": 45033, "Ġzust": 45034, "ĠPens": 45035, "sed": 45036, "ĠíŤ": 45037, "Ġgereki": 45038, "Ġalarms": 45039, "ĠWha": 45040, "ĠMarkus": 45041, "aksi": 45042, "ĠÐIJле": 45043, "klore": 45044, "Ġéner": 45045, "Ġtilde": 45046, "boxing": 45047, "ĠìĦŀ": 45048, "Ġencontramos": 45049, "ĠPhar": 45050, "наком": 45051, "óst": 45052, "ĠÄ°s": 45053, "Ġëĭĺ": 45054, "Ġsquats": 45055, "Ġpretended": 45056, "Ġdez": 45057, "Ġê´ľì°®ìķĦ": 45058, "jach": 45059, "ëĿ¼ê³ł": 45060, "ĠíĻķì§Ħ": 45061, "ĠAnsch": 45062, "imerk": 45063, "Ġconjugate": 45064, "Ġpeninsula": 45065, "Ġgorilla": 45066, "Ġphotographed": 45067, "ĠAunque": 45068, "Ġentren": 45069, "ĠDeutschen": 45070, "ĠAladdin": 45071, "Ġ무ìĦľ": 45072, "ĠStella": 45073, "ĠElection": 45074, "outine": 45075, "Grand": 45076, "ĠWak": 45077, "ĠSergio": 45078, "horse": 45079, "ahon": 45080, "ĠFamilies": 45081, "Ġhating": 45082, "ĠBett": 45083, "à¸Ļะà¸Ħะ": 45084, "Ġcurling": 45085, "ĠIsraelis": 45086, "Ġ׾×IJ×": 45087, "ĠMyers": 45088, "Ġscanned": 45089, 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"ĠBlockchain": 48916, "ĠÑħоÑĢоÑĪий": 48917, "Ġvirtuous": 48918, "Ġinvestigación": 48919, "Ġdevoir": 48920, "Ġmasturb": 48921, "ĠSale": 48922, "ÙĬرة": 48923, "ĠΧ": 48924, "ĠStraÃŁen": 48925, "Ġdikk": 48926, "Ġafore": 48927, "ĠJungkook": 48928, "Ġchociaż": 48929, "ĠDebatte": 48930, "Ġweirdly": 48931, "Ġviaje": 48932, "regist": 48933, "Help": 48934, "Ġkinderen": 48935, "Ġformulated": 48936, "Ġenfim": 48937, "ĠTowards": 48938, "коÑĹ": 48939, "ivering": 48940, "ĠдеÑĤи": 48941, "charger": 48942, "Ġpurl": 48943, "Ġacademically": 48944, "ĠNurse": 48945, "Ġdeleting": 48946, "ayo": 48947, "Ġrefusal": 48948, "Ġdepicts": 48949, "ĠDracula": 48950, "Ġtoasted": 48951, "ĠZombie": 48952, "ĠSuperior": 48953, "ĠBold": 48954, "Ġquizzes": 48955, "Ġgle": 48956, "450": 48957, "Ġcomeço": 48958, "ynn": 48959, "Ġverst": 48960, "ĠOlaf": 48961, "Ġpomoc": 48962, "ĠSask": 48963, "ëĺ": 48964, "ĠTCP": 48965, "ĠProperty": 48966, "íķĺì£ł": 48967, "à¸ľà¸¡": 48968, "boom": 48969, "aros": 48970, "ĠÑĢоÑģÑģий": 48971, "ĠбÑĭваеÑĤ": 48972, "åĩºåİ»": 48973, "ĠìĿ´ìķ¼ê¸°ë¥¼": 48974, "Ġcombien": 48975, "vacc": 48976, "Ġebenfalls": 48977, "para": 48978, "Ġзм": 48979, "Ġdesperation": 48980, "ordre": 48981, "Ġש׾×Ļ": 48982, "Ġgenerously": 48983, "ĠÐŀк": 48984, "Ġorbiting": 48985, "></": 48986, "ĠespÃŃ": 48987, "ĠCOP": 48988, "åŃ©åŃIJ": 48989, "visible": 48990, "ĠпÑĢеÑģÑĤÑĥп": 48991, "Ġstitched": 48992, "à¯Ī.": 48993, "Ġlatent": 48994, "ĠPrab": 48995, "ĠMcN": 48996, "ĠHealing": 48997, "ĠCuriosity": 48998, "cert": 48999, "Ġ민주": 49000, "Ġpatiently": 49001, "ĠYT": 49002, "foreign": 49003, "Ġvẫn": 49004, "Ġindustri": 49005, "Ġcocktails": 49006, "Ġbrighten": 49007, "Ġconsolidated": 49008, "аÑĢд": 49009, "ltry": 49010, "Ġgrille": 49011, "Ġbona": 49012, "Ġdiligently": 49013, "ĠWrestleMania": 49014, "erkt": 49015, "energy": 49016, "999": 49017, "à®ķவ": 49018, "Ġtote": 49019, "iono": 49020, "DIO": 49021, "Ġschizophrenia": 49022, "Ġpostponed": 49023, "ĠQiu": 49024, "ĠÏĥÏħν": 49025, "ĠzdjÄĻ": 49026, "Ġspannend": 49027, "ĠDIS": 49028, "Rel": 49029, "Ġrhin": 49030, "immune": 49031, "Old": 49032, "Ġplötzlich": 49033, "Ġmound": 49034, "Ġastronomical": 49035, "ĠGuid": 49036, "ĠCul": 49037, "HI": 49038, "ĠÅł": 49039, "Ġrepo": 49040, "ĠMaurice": 49041, "ä¸ĢçĤ¹": 49042, "Ġbandits": 49043, "ĠDesktop": 49044, "äss": 49045, "fta": 49046, "Ġlicence": 49047, "Ġimaginar": 49048, "ĠEntreprene": 49049, "xo": 49050, "Ġ맼ìŀĪëĬĶ": 49051, "Ġ×Ķ×ij": 49052, "Ġpumpkins": 49053, "Ġkanssa": 49054, "ĠjÄĻzy": 49055, "Ġcommunauté": 49056, "bür": 49057, "Ġerhö": 49058, "ĠWolver": 49059, "ĠSharing": 49060, "令": 49061, "Ġpakai": 49062, "Ġinsulted": 49063, "ÐľÑĭ": 49064, "оÑĹ": 49065, "Ġconsiste": 49066, "æĮij": 49067, "Ġyoungsters": 49068, "Ġgleichen": 49069, "weder": 49070, "Ġmote": 49071, "Ġclauses": 49072, "état": 49073, "prus": 49074, "Ġwast": 49075, "ç»ĻæĪij": 49076, "ĠCrisp": 49077, "ĠçĦ¶å¾Į": 49078, "Ġoffenders": 49079, "Ġconvection": 49080, "Ġconfian": 49081, "ollow": 49082, "amet": 49083, "ĠÑĹÑħ": 49084, "第äºĮåĢĭ": 49085, "fficiency": 49086, "Ġunglaub": 49087, "igans": 49088, "Ġmarketed": 49089, "ĠVAN": 49090, "Ġproclaimed": 49091, "Ġcélulas": 49092, "Ġcollide": 49093, "ĠOculus": 49094, "adore": 49095, "Ji": 49096, "Ġsustaining": 49097, "ĠFasc": 49098, "Ġsetzt": 49099, "Ġnosaltres": 49100, "Most": 49101, "ĠвÑĩ": 49102, "Ġnauc": 49103, "ĠBhar": 49104, "çĪ¸çĪ¸": 49105, "æĪijè·Łä½łè¬Ľ": 49106, "Ġyêu": 49107, "Ġtimest": 49108, "Ġpertama": 49109, "irmi": 49110, "Ġzwr": 49111, "Ġverbess": 49112, "Ġvortex": 49113, "ĠSTACK": 49114, "ثر": 49115, "¹Ħë": 49116, "ĶĶìĺ¤": 49117, "Ġlinkage": 49118, "ĠFraser": 49119, "enario": 49120, "ĠëĿ¼ëĬĶ": 49121, "ĠìĦłë°°": 49122, "hthal": 49123, "Ġê¹Į": 49124, "ĠKhông": 49125, "Ãĥ": 49126, "Ġscrambled": 49127, "ĠEink": 49128, "Ġmicroorgan": 49129, "Ġnarcissist": 49130, "ĠKombat": 49131, "Ġ맡": 49132, "ĠAGA": 49133, "Ġperfekt": 49134, "ĠSerie": 49135, "determ": 49136, "-'": 49137, "Ġponytail": 49138, "Ġkoska": 49139, "ìĵ": 49140, "Ġobec": 49141, "Ġchests": 49142, "veer": 49143, "Ġuprising": 49144, "Ġstoked": 49145, "associ": 49146, "Ġprodução": 49147, "ĠShape": 49148, "ìłľê°Ģ": 49149, "ĠëĶ°": 49150, "Ġjon": 49151, "Ġinadvert": 49152, "antas": 49153, "ĠнаконеÑĨ": 49154, "Ġå°įåķĬ": 49155, "ĠArsenal": 49156, "Ġproteg": 49157, "Ġliberté": 49158, "Ġglare": 49159, "åĪļ": 49160, "å·²ç»ı": 49161, "Ġverein": 49162, "Ġinserts": 49163, "ĠJana": 49164, "Ġwydaje": 49165, "ÅĤum": 49166, "Ġ%.": 49167, "origine": 49168, "Ġsynagogue": 49169, "Ġfallait": 49170, "Ġdisobed": 49171, "Ġantic": 49172, "ĠCycl": 49173, "Ġasynchronous": 49174, "Ġë²Įìį¨": 49175, "Ġgesund": 49176, "Ġgagn": 49177, "Ġpea": 49178, "Ġgrin": 49179, "ést": 49180, "Ġsauc": 49181, "ĠMäd": 49182, "íķ´ëıĦ": 49183, "pps": 49184, "ĠεÏĢι": 49185, "Ġpeuple": 49186, "Ġdeben": 49187, "ĠBree": 49188, "ĠÑĢолÑĮ": 49189, "Ġкаким": 49190, "Ġútil": 49191, "Ġdistributor": 49192, "алÑĭ": 49193, "ĠswojÄħ": 49194, "Ġfolklore": 49195, "Ġreceivers": 49196, "ĠMOO": 49197, "bins": 49198, "astre": 49199, "ìķĪë": 49200, "ĠëĦ£ê³ł": 49201, "Ġmultimedia": 49202, "Ġgebaut": 49203, "овÑĭÑħ": 49204, "ãy": 49205, "Ġdane": 49206, "okol": 49207, "emitism": 49208, "ONEY": 49209, "ĠyaÄŁ": 49210, "Ġchauff": 49211, "容æĺĵ": 49212, "Ġesfuer": 49213, "Äĥn": 49214, "ertas": 49215, "Ġfonctionne": 49216, "omina": 49217, "Ġivory": 49218, "ĠYoutuber": 49219, "ĠSkywalker": 49220, "иÑĩеÑģкаÑı": 49221, "toi": 49222, "Ġveya": 49223, "Ġgelernt": 49224, "Ġchancellor": 49225, "ĠStatistics": 49226, "Ġwelded": 49227, "Ġondan": 49228, "ĠSei": 49229, "Ġmedically": 49230, "Ġenergized": 49231, "ĠVia": 49232, "Ġвик": 49233, "Ġuninter": 49234, "Ġhighness": 49235, "ĠíĮĶë": 49236, "Ġamplified": 49237, "ĠSergey": 49238, "ĠMins": 49239, "warm": 49240, "pell": 49241, "ophile": 49242, "Ġhè": 49243, "ĠBelo": 49244, "ĠSketch": 49245, "Ġcharacterization": 49246, "ansen": 49247, "ĠÑĤÑĥÑĢ": 49248, "Ġãħĭãħĭãħĭ": 49249, "Note": 49250, "ĠkoÅŁ": 49251, "Ġciert": 49252, "flu": 49253, "Ġbaht": 49254, "ĠDowntown": 49255, "ĠCRIS": 49256, "odie": 49257, "140": 49258, "Ġlitres": 49259, "Ġgriev": 49260, "æ§ĺ": 49261, "ĠìĶ¨ê°Ģ": 49262, "Ġsucceeds": 49263, "Ġ__": 49264, "enting": 49265, "Ġvimos": 49266, "Ġsì": 49267, "defense": 49268, "ĠMcD": 49269, "ĠMarion": 49270, "ĠDont": 49271, "ĠDDR": 49272, "ĠLazar": 49273, "ĠDAR": 49274, "Ġkuv": 49275, "Kn": 49276, "Ġsembla": 49277, "Ġairborne": 49278, "ĠViolence": 49279, "ëIJIJ": 49280, "Ġrestraint": 49281, "Ġwhistles": 49282, "Ġscolded": 49283, "Ġacceso": 49284, "Ġabsolutamente": 49285, "ĠTyl": 49286, "ĠSap": 49287, "¶Ģë¶Ħ": 49288, "itäten": 49289, "adem": 49290, "Ġý": 49291, "Ġprescribe": 49292, "ĠMage": 49293, "ĠHelena": 49294, "å¾Īæľī": 49295, "亲": 49296, "vt": 49297, "Ġvienen": 49298, "Ġsneez": 49299, "Ġmolé": 49300, "Æ°á»Łng": 49301, "Ġtransporting": 49302, "ĠLean": 49303, "Ġkung": 49304, "ÑĥÑĢа": 49305, "ÏĦÎŃ": 49306, "utches": 49307, "onders": 49308, "liyor": 49309, "Nat": 49310, "Ġzij": 49311, "Ġmammal": 49312, "Ġkäyt": 49313, "ĠJoanna": 49314, "sent": 49315, "Ġस": 49316, "Ġvested": 49317, "ĠErfahrung": 49318, "okee": 49319, "Ġclipping": 49320, "ĠListening": 49321, "Ġ(#": 49322, "fö": 49323, "Ġvidare": 49324, "Ġbrittle": 49325, "ĠSTART": 49326, "ĠDamas": 49327, "ĠYog": 49328, "ãĤĵãģ¨": 49329, "gart": 49330, "Ġverlier": 49331, "Ġheartfelt": 49332, "ĠdoÅĽÄĩ": 49333, "ì¹ĺê°Ģ": 49334, ".»": 49335, "Ġmaximal": 49336, "Ġdistintos": 49337, "ĠìĻľëĥIJíķĺë©´": 49338, "Ġsailed": 49339, "Ġconveyed": 49340, "ĠTinder": 49341, "ĠSUPER": 49342, "ниÑĨÑĥ": 49343, "controlled": 49344, "Ġfunz": 49345, "Ġbastards": 49346, "ĠGinsburg": 49347, "Ġnuovo": 49348, "ĠPere": 49349, "ĠJES": 49350, "ĠDingen": 49351, "ĠBets": 49352, "umba": 49353, "acción": 49354, "ĠìŀĪì§Ģë§Į": 49355, "Ġretra": 49356, "ĠLaurent": 49357, "Ġpozy": 49358, "Ġgrooves": 49359, "Ġmáquina": 49360, "Ġminion": 49361, "Ġdeinen": 49362, "ĠShaun": 49363, "×Ļ×Ļ": 49364, "Ġhonorary": 49365, "osaurus": 49366, "Ġzeit": 49367, "Ġespecie": 49368, "ĠBCE": 49369, "аÑĤе": 49370, "Justin": 49371, "ĠWheels": 49372, "ĠìĿ´íķ´": 49373, "ĠبÙĬÙĨ": 49374, "Ġpropulsion": 49375, "Ġperceber": 49376, "ĠNewman": 49377, "å´": 49378, "culosis": 49379, "Mi": 49380, "ĠаккÑĥ": 49381, "Ġmastering": 49382, "Ġläh": 49383, "Ġfists": 49384, "ä»Ķ": 49385, "Ġmarinade": 49386, "Lilly": 49387, "Ġëħ¸ëł¥": 49388, "ĠYH": 49389, "Ġurgently": 49390, "Ġinformational": 49391, "Ġacordo": 49392, "izzy": 49393, "ãģĦãģı": 49394, "ìĿ´ìĸ´": 49395, "imar": 49396, "ĠëĤĺìĺ¤ë": 49397, "Ġtwenties": 49398, "Ġrasp": 49399, "Ġbumpy": 49400, "بة": 49401, "worker": 49402, "Ġquickest": 49403, "Ġattaches": 49404, "виг": 49405, "ĠëĤĺíĥĢë": 49406, "Ġpuree": 49407, "Ġoversized": 49408, "Ġstirred": 49409, "Ġjakim": 49410, "Ġhomicide": 49411, "ãĤĤãģĹ": 49412, "iscilla": 49413, "Ġì±Ļ": 49414, "Ġspeculative": 49415, "Ġassists": 49416, "main": 49417, "jähr": 49418, "indet": 49419, "ĠÅŁur": 49420, "Ġforecasts": 49421, "Ġdiversion": 49422, "Ġtare": 49423, "Ġogl": 49424, "ĠOrganisation": 49425, "ĠChevy": 49426, "Ġbaja": 49427, "andır": 49428, "ĠÙĪÙĦا": 49429, "Ġradiant": 49430, "Ġliaison": 49431, "Ġdemokrat": 49432, "ĠMARC": 49433, "ÏĢοÏħ": 49434, "Ġrunt": 49435, "Ġprécis": 49436, "Ġgeven": 49437, "Ġvéhic": 49438, "ĠJESS": 49439, "STR": 49440, "Ġìĸĺë": 49441, "Ġvisionary": 49442, "Ġburadan": 49443, "ĠãģĤãĤĬ": 49444, "Ġrebirth": 49445, "Ġexhibited": 49446, "ĠMetall": 49447, "olie": 49448, "elyn": 49449, "Ġflavours": 49450, "Ġescrito": 49451, "ĠDelete": 49452, "ĠìķĮìķĺìĸ´": 49453, "ĠÑĥкÑĢаÑĹн": 49454, "Ġinterrupting": 49455, "Ġidentific": 49456, "ĠSuzuki": 49457, "ĠLanding": 49458, "件äºĭæĥħ": 49459, "andi": 49460, "Ġestran": 49461, "Ġcouleur": 49462, "Ġagrad": 49463, "ĠSny": 49464, "Ġà®ĩல": 49465, "Ġander": 49466, "Ġrua": 49467, "Ġprise": 49468, "Ġlaure": 49469, "ĠíĬĢ": 49470, "Ġmoderation": 49471, "Ġerfahren": 49472, "Ġdeconst": 49473, "ĠReese": 49474, "ĠPK": 49475, "etos": 49476, "ãģĵãĤĮãģ§": 49477, "ĠGravity": 49478, "ĠEren": 49479, "Ġoverboard": 49480, "Ġmüsst": 49481, "ĠEmail": 49482, "еÑĢм": 49483, "ydi": 49484, "iÄĻdzy": 49485, "ĠLOU": 49486, "ĠFuÃŁball": 49487, "ĠRD": 49488, "alts": 49489, "ĠìĬ¤íĬ¸ë": 49490, "ĠÐļÑĢаÑģ": 49491, "Ġtelev": 49492, "ĠÑĢо": 49493, "Ġresignation": 49494, "Ġjingle": 49495, "ĠStudien": 49496, "ĠIX": 49497, "ĠSentinel": 49498, "ĠPang": 49499, "éĦ": 49500, "Jake": 49501, "Ġpersonagem": 49502, "Ġmédia": 49503, "ĠChern": 49504, "antically": 49505, "Ġthá»Ŀi": 49506, "Ġparalysis": 49507, "Ġjapanese": 49508, "Ġconex": 49509, "Ġefic": 49510, "Ġunderside": 49511, "Ġneol": 49512, "Ġfian": 49513, "имоÑģÑĤÑĮ": 49514, "Ġquirky": 49515, "Ġpista": 49516, "ĠClement": 49517, "nothing": 49518, "ĠпоеÑħ": 49519, "Ġhorrend": 49520, "Ġconsolidate": 49521, "ploys": 49522, "emaker": 49523, "Jennifer": 49524, "Ġnuméro": 49525, "Ġfamoso": 49526, "ĠNeptune": 49527, "ĠíĸĪìĸ´": 49528, "ĠпÑĢезид": 49529, "Ġsitcom": 49530, "Ġserio": 49531, "Ġmue": 49532, "Ġglands": 49533, "Ġbörjar": 49534, "ĠYJ": 49535, "ĠRiot": 49536, "paragus": 49537, "Ġsegurança": 49538, "Ġimmature": 49539, "ĠMadonna": 49540, "à¸į": 49541, "Ġlingering": 49542, "Ġacesso": 49543, "ĠOrient": 49544, "ĠRecomm": 49545, "Ġcomplac": 49546, "founded": 49547, "attend": 49548, "Ġcielo": 49549, "ĠZhan": 49550, "naires": 49551, "cco": 49552, "Ġ×IJ׳": 49553, "Ġstata": 49554, "Ġcontradictory": 49555, "ĠSé": 49556, "ĠSAN": 49557, "ĠConnie": 49558, "Ġëĭ¹ìĭľ": 49559, "ĠÑģамой": 49560, "Ġmajestic": 49561, "ĠPenguin": 49562, "ĠCOME": 49563, "ÃŃcios": 49564, "pero": 49565, "Ġmg": 49566, "Ġfauc": 49567, "Ġcorrer": 49568, "ĠGottes": 49569, "ĠAnglo": 49570, "Har": 49571, "á»Ĺi": 49572, "Ġvitesse": 49573, "Ġannouncer": 49574, "ĠOmaha": 49575, "kum": 49576, "Ġspared": 49577, "ĠÑĢаза": 49578, "ĠполÑĥÑĩиÑĤÑģÑı": 49579, "Ġtähän": 49580, "Ġпонад": 49581, "Ġpertaining": 49582, "ĠRate": 49583, "iern": 49584, "Gold": 49585, "Ġteste": 49586, "ĠdeÄŁild": 49587, "Ġdamping": 49588, "ĠPartnership": 49589, "zysta": 49590, "geld": 49591, "Ġsmokes": 49592, "ĠMarriage": 49593, "쪽ìĹIJ": 49594, "èħ³": 49595, "isce": 49596, "Ġtryna": 49597, "ĠDirectory": 49598, "ĠëĤĺìĺ¬": 49599, "Ġshameful": 49600, "Ġmentre": 49601, "Ġassigning": 49602, "æĺ¯éĢĻ樣": 49603, "Ġrepertoire": 49604, "Ġobjetos": 49605, "稱": 49606, "Ġunderworld": 49607, "Ġendeavors": 49608, "Ġignite": 49609, "ĠÙĪج": 49610, "Ġexperient": 49611, "ĠÐĹап": 49612, "ĠзаклÑİÑĩ": 49613, "Ġvoltages": 49614, "Ġniego": 49615, "Ġdeficits": 49616, "Ġbuenos": 49617, "ĠSleeping": 49618, "ĠSalem": 49619, "Ġunlocking": 49620, "Ġinteracted": 49621, "Ġentendeu": 49622, "ĠSuperintendent": 49623, "Ġszczegól": 49624, "Ġquas": 49625, "Ġpaling": 49626, "Ġkho": 49627, "بØŃ": 49628, "Ġcolabor": 49629, "ĠпÑĢигоÑĤов": 49630, "Ġmauv": 49631, "ĠJudas": 49632, "ĠAssist": 49633, "ĠÑĤеÑĢÑĢи": 49634, "ĠнаÑģколÑĮко": 49635, "Ġsubsidy": 49636, "ĠEmbassy": 49637, "Ġdagen": 49638, "ĠSanto": 49639, "èĪ¬": 49640, "ש×ķ×ij": 49641, "Ġabruptly": 49642, "ĠAdapt": 49643, "Ġvaak": 49644, "Ġpostal": 49645, "Ġinvestir": 49646, "Ġfiquei": 49647, "Ġdowntime": 49648, "ĠWebb": 49649, "ĠNCAA": 49650, "ĠEstoy": 49651, "олоÑĤ": 49652, "ĠìĤ¬ê±´": 49653, 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\ No newline at end of file
diff --git a/utils/digital_human/musetalk/whisper/whisper/audio.py b/utils/digital_human/musetalk/whisper/whisper/audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a1986d630105eeacbcb007338bd8ce54d2a69d6
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/audio.py
@@ -0,0 +1,125 @@
+import os
+from functools import lru_cache
+from typing import Union
+
+import ffmpeg
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from .utils import exact_div
+
+# hard-coded audio hyperparameters
+SAMPLE_RATE = 16000
+N_FFT = 400
+N_MELS = 80
+HOP_LENGTH = 160
+CHUNK_LENGTH = 30
+N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE  # 480000: number of samples in a chunk
+N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH)  # 3000: number of frames in a mel spectrogram input
+
+
+def load_audio(file: str, sr: int = SAMPLE_RATE):
+    """
+    Open an audio file and read as mono waveform, resampling as necessary
+
+    Parameters
+    ----------
+    file: str
+        The audio file to open
+
+    sr: int
+        The sample rate to resample the audio if necessary
+
+    Returns
+    -------
+    A NumPy array containing the audio waveform, in float32 dtype.
+    """
+    try:
+        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
+        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
+        out, _ = (
+            ffmpeg.input(file, threads=0)
+            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
+            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
+        )
+    except ffmpeg.Error as e:
+        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+
+    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
+
+
+def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
+    """
+    Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
+    """
+    if torch.is_tensor(array):
+        if array.shape[axis] > length:
+            array = array.index_select(dim=axis, index=torch.arange(length))
+
+        if array.shape[axis] < length:
+            pad_widths = [(0, 0)] * array.ndim
+            pad_widths[axis] = (0, length - array.shape[axis])
+            array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
+    else:
+        if array.shape[axis] > length:
+            array = array.take(indices=range(length), axis=axis)
+
+        if array.shape[axis] < length:
+            pad_widths = [(0, 0)] * array.ndim
+            pad_widths[axis] = (0, length - array.shape[axis])
+            array = np.pad(array, pad_widths)
+
+    return array
+
+
+@lru_cache(maxsize=None)
+def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
+    """
+    load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
+    Allows decoupling librosa dependency; saved using:
+
+        np.savez_compressed(
+            "mel_filters.npz",
+            mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
+        )
+    """
+    assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
+    with np.load(os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")) as f:
+        return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
+
+
+def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
+    """
+    Compute the log-Mel spectrogram of
+
+    Parameters
+    ----------
+    audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
+        The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
+
+    n_mels: int
+        The number of Mel-frequency filters, only 80 is supported
+
+    Returns
+    -------
+    torch.Tensor, shape = (80, n_frames)
+        A Tensor that contains the Mel spectrogram
+    """
+    if not torch.is_tensor(audio):
+        if isinstance(audio, str):
+            audio = load_audio(audio)
+        audio = torch.from_numpy(audio)
+        
+    window = torch.hann_window(N_FFT).to(audio.device)
+    stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
+    
+    magnitudes = stft[:, :-1].abs() ** 2
+
+    filters = mel_filters(audio.device, n_mels)
+    mel_spec = filters @ magnitudes
+
+    log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+    log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+    log_spec = (log_spec + 4.0) / 4.0
+    return log_spec
diff --git a/utils/digital_human/musetalk/whisper/whisper/decoding.py b/utils/digital_human/musetalk/whisper/whisper/decoding.py
new file mode 100644
index 0000000000000000000000000000000000000000..183505481e40a4f3779933e5fde8086d79e4cc39
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/decoding.py
@@ -0,0 +1,729 @@
+from dataclasses import dataclass, field
+from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+from torch.distributions import Categorical
+
+from .audio import CHUNK_LENGTH
+from .tokenizer import Tokenizer, get_tokenizer
+from .utils import compression_ratio
+
+if TYPE_CHECKING:
+    from .model import Whisper
+
+
+@torch.no_grad()
+def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[Tensor, List[dict]]:
+    """
+    Detect the spoken language in the audio, and return them as list of strings, along with the ids
+    of the most probable language tokens and the probability distribution over all language tokens.
+    This is performed outside the main decode loop in order to not interfere with kv-caching.
+
+    Returns
+    -------
+    language_tokens : Tensor, shape = (n_audio,)
+        ids of the most probable language tokens, which appears after the startoftranscript token.
+    language_probs : List[Dict[str, float]], length = n_audio
+        list of dictionaries containing the probability distribution over all languages.
+    """
+    if tokenizer is None:
+        tokenizer = get_tokenizer(model.is_multilingual)
+    if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence:
+        raise ValueError(f"This model doesn't have language tokens so it can't perform lang id")
+
+    single = mel.ndim == 2
+    if single:
+        mel = mel.unsqueeze(0)
+
+    # skip encoder forward pass if already-encoded audio features were given
+    if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state):
+        mel = model.encoder(mel)
+
+    # forward pass using a single token, startoftranscript
+    n_audio = mel.shape[0]
+    x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device)  # [n_audio, 1]
+    logits = model.logits(x, mel)[:, 0]
+
+    # collect detected languages; suppress all non-language tokens
+    mask = torch.ones(logits.shape[-1], dtype=torch.bool)
+    mask[list(tokenizer.all_language_tokens)] = False
+    logits[:, mask] = -np.inf
+    language_tokens = logits.argmax(dim=-1)
+    language_token_probs = logits.softmax(dim=-1).cpu()
+    language_probs = [
+        {
+            c: language_token_probs[i, j].item()
+            for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes)
+        }
+        for i in range(n_audio)
+    ]
+
+    if single:
+        language_tokens = language_tokens[0]
+        language_probs = language_probs[0]
+
+    return language_tokens, language_probs
+
+
+@dataclass(frozen=True)
+class DecodingOptions:
+    task: str = "transcribe"  # whether to perform X->X "transcribe" or X->English "translate"
+    language: Optional[str] = None  # language that the audio is in; uses detected language if None
+
+    # sampling-related options
+    temperature: float = 0.0
+    sample_len: Optional[int] = None  # maximum number of tokens to sample
+    best_of: Optional[int] = None     # number of independent samples to collect, when t > 0
+    beam_size: Optional[int] = None   # number of beams in beam search, when t == 0
+    patience: Optional[float] = None  # patience in beam search (https://arxiv.org/abs/2204.05424)
+
+    # options for ranking generations (either beams or best-of-N samples)
+    length_penalty: Optional[float] = None   # "alpha" in Google NMT, None defaults to length norm
+
+    # prompt, prefix, and token suppression
+    prompt: Optional[Union[str, List[int]]] = None   # text or tokens for the previous context
+    prefix: Optional[Union[str, List[int]]] = None   # text or tokens to prefix the current context
+    suppress_blank: bool = True                      # this will suppress blank outputs
+
+    # list of tokens ids (or comma-separated token ids) to suppress
+    # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()`
+    suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1"
+
+    # timestamp sampling options
+    without_timestamps: bool = False              # use <|notimestamps|> to sample text tokens only
+    max_initial_timestamp: Optional[float] = 1.0  # the initial timestamp cannot be later than this
+
+    # implementation details
+    fp16: bool = True  # use fp16 for most of the calculation
+
+
+@dataclass(frozen=True)
+class DecodingResult:
+    audio_features: Tensor
+    language: str
+    encoder_embeddings: np.ndarray
+    decoder_embeddings: np.ndarray
+    language_probs: Optional[Dict[str, float]] = None
+    tokens: List[int] = field(default_factory=list)
+    text: str = ""
+    avg_logprob: float = np.nan
+    no_speech_prob: float = np.nan
+    temperature: float = np.nan
+    compression_ratio: float = np.nan
+
+
+class Inference:
+    def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor:
+        """Perform a forward pass on the decoder and return per-token logits"""
+        raise NotImplementedError
+
+    def rearrange_kv_cache(self, source_indices) -> None:
+        """Update the key-value cache according to the updated beams"""
+        raise NotImplementedError
+
+    def cleanup_caching(self) -> None:
+        """Clean up any resources or hooks after decoding is finished"""
+        pass
+
+
+class PyTorchInference(Inference):
+    def __init__(self, model: "Whisper", initial_token_length: int):
+        self.model: "Whisper" = model
+        self.initial_token_length = initial_token_length
+        self.kv_cache = {}
+        self.hooks = []
+
+    def logits(self, tokens: Tensor, audio_features: Tensor, include_embeddings=False) -> Tensor:
+        if not self.kv_cache:
+            self.kv_cache, self.hooks = self.model.install_kv_cache_hooks()
+
+        if tokens.shape[-1] > self.initial_token_length:
+            # only need to use the last token except in the first forward pass
+            tokens = tokens[:, -1:]
+
+        return_val = self.model.decoder(tokens, audio_features,
+                                        kv_cache=self.kv_cache, include_embeddings=include_embeddings)
+        return return_val
+
+    def cleanup_caching(self):
+        for hook in self.hooks:
+            hook.remove()
+
+        self.kv_cache = {}
+        self.hooks = []
+
+    def rearrange_kv_cache(self, source_indices):
+        for module, tensor in self.kv_cache.items():
+            # update the key/value cache to contain the selected sequences
+            self.kv_cache[module] = tensor[source_indices].detach()
+
+
+class SequenceRanker:
+    def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]) -> List[int]:
+        """
+        Given a list of groups of samples and their cumulative log probabilities,
+        return the indices of the samples in each group to select as the final result
+        """
+        raise NotImplementedError
+
+
+class MaximumLikelihoodRanker(SequenceRanker):
+    """
+    Select the sample with the highest log probabilities, penalized using either
+    a simple length normalization or Google NMT paper's length penalty
+    """
+
+    def __init__(self, length_penalty: Optional[float]):
+        self.length_penalty = length_penalty
+
+    def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]):
+        def scores(logprobs, lengths):
+            result = []
+            for logprob, length in zip(logprobs, lengths):
+                if self.length_penalty is None:
+                    penalty = length
+                else:
+                    # from the Google NMT paper
+                    penalty = ((5 + length) / 6) ** self.length_penalty
+                result.append(logprob / penalty)
+            return result
+
+        # get the sequence with the highest score
+        lengths = [[len(t) for t in s] for s in tokens]
+        return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)]
+
+
+class TokenDecoder:
+    def reset(self):
+        """Initialize any stateful variables for decoding a new sequence"""
+
+    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
+        """Specify how to select the next token, based on the current trace and logits
+
+        Parameters
+        ----------
+        tokens : Tensor, shape = (n_batch, current_sequence_length)
+            all tokens in the context so far, including the prefix and sot_sequence tokens
+
+        logits : Tensor, shape = (n_batch, vocab_size)
+            per-token logits of the probability distribution at the current step
+
+        sum_logprobs : Tensor, shape = (n_batch)
+            cumulative log probabilities for each sequence
+
+        Returns
+        -------
+        tokens : Tensor, shape = (n_batch, current_sequence_length + 1)
+            the tokens, appended with the selected next token
+
+        completed : bool
+            True if all sequences has reached the end of text
+
+        """
+        raise NotImplementedError
+
+    def finalize(
+        self, tokens: Tensor, sum_logprobs: Tensor
+    ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]:
+        """Finalize search and return the final candidate sequences
+
+        Parameters
+        ----------
+        tokens : Tensor, shape = (n_audio, n_group, current_sequence_length)
+            all tokens in the context so far, including the prefix and sot_sequence
+
+        sum_logprobs : Tensor, shape = (n_audio, n_group)
+            cumulative log probabilities for each sequence
+
+        Returns
+        -------
+        tokens : Sequence[Sequence[Tensor]], length = n_audio
+            sequence of Tensors containing candidate token sequences, for each audio input
+
+        sum_logprobs : List[List[float]], length = n_audio
+            sequence of cumulative log probabilities corresponding to the above
+
+        """
+        raise NotImplementedError
+
+
+class GreedyDecoder(TokenDecoder):
+    def __init__(self, temperature: float, eot: int):
+        self.temperature = temperature
+        self.eot = eot
+
+    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
+        temperature = self.temperature
+        if temperature == 0:
+            next_tokens = logits.argmax(dim=-1)
+        else:
+            next_tokens = Categorical(logits=logits / temperature).sample()
+
+        logprobs = F.log_softmax(logits.float(), dim=-1)
+        current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens]
+        sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot)
+
+        next_tokens[tokens[:, -1] == self.eot] = self.eot
+        tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1)
+
+        completed = (tokens[:, -1] == self.eot).all()
+        return tokens, completed
+
+    def finalize(self, tokens: Tensor, sum_logprobs: Tensor):
+        # make sure each sequence has at least one EOT token at the end
+        tokens = F.pad(tokens, (0, 1), value=self.eot)
+        return tokens, sum_logprobs.tolist()
+
+
+class BeamSearchDecoder(TokenDecoder):
+    def __init__(self, beam_size: int, eot: int, inference: Inference, patience: Optional[float] = None):
+        self.beam_size = beam_size
+        self.eot = eot
+        self.inference = inference
+        self.patience = patience or 1.0
+        self.max_candidates: int = round(beam_size * self.patience)
+        self.finished_sequences = None
+
+        assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})"
+
+    def reset(self):
+        self.finished_sequences = None
+
+    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
+        if tokens.shape[0] % self.beam_size != 0:
+            raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0")
+
+        n_audio = tokens.shape[0] // self.beam_size
+        if self.finished_sequences is None:  # for the first update
+            self.finished_sequences = [{} for _ in range(n_audio)]
+
+        logprobs = F.log_softmax(logits.float(), dim=-1)
+        next_tokens, source_indices, finished_sequences = [], [], []
+        for i in range(n_audio):
+            scores, sources, finished = {}, {}, {}
+
+            # STEP 1: calculate the cumulative log probabilities for possible candidates
+            for j in range(self.beam_size):
+                idx = i * self.beam_size + j
+                prefix = tokens[idx].tolist()
+                for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)):
+                    new_logprob = (sum_logprobs[idx] + logprob).item()
+                    sequence = tuple(prefix + [token.item()])
+                    scores[sequence] = new_logprob
+                    sources[sequence] = idx
+
+            # STEP 2: rank the candidates and keep the top beam_size sequences for each audio
+            saved = 0
+            for sequence in sorted(scores, key=scores.get, reverse=True):
+                if sequence[-1] == self.eot:
+                    finished[sequence] = scores[sequence]
+                else:
+                    sum_logprobs[len(next_tokens)] = scores[sequence]
+                    next_tokens.append(sequence)
+                    source_indices.append(sources[sequence])
+
+                    saved += 1
+                    if saved == self.beam_size:
+                        break
+
+            finished_sequences.append(finished)
+
+        tokens = torch.tensor(next_tokens, device=tokens.device)
+        self.inference.rearrange_kv_cache(source_indices)
+
+        # add newly finished sequences to self.finished_sequences
+        assert len(self.finished_sequences) == len(finished_sequences)
+        for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences):
+            for seq in sorted(newly_finished, key=newly_finished.get, reverse=True):
+                if len(previously_finished) >= self.max_candidates:
+                    break  # the candidate list is full
+                previously_finished[seq] = newly_finished[seq]
+
+        # mark as completed if all audio has enough number of samples
+        completed = all(
+            len(sequences) >= self.max_candidates for sequences in self.finished_sequences
+        )
+        return tokens, completed
+
+    def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor):
+        # collect all finished sequences, including patience, and add unfinished ones if not enough
+        sum_logprobs = sum_logprobs.cpu()
+        for i, sequences in enumerate(self.finished_sequences):
+            if len(sequences) < self.beam_size:  # when not enough sequences are finished
+                for j in list(np.argsort(sum_logprobs[i]))[::-1]:
+                    sequence = preceding_tokens[i, j].tolist() + [self.eot]
+                    sequences[tuple(sequence)] = sum_logprobs[i][j].item()
+                    if len(sequences) >= self.beam_size:
+                        break
+
+        tokens: List[List[Tensor]] = [
+            [torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences
+        ]
+        sum_logprobs: List[List[float]] = [
+            list(sequences.values()) for sequences in self.finished_sequences
+        ]
+        return tokens, sum_logprobs
+
+
+class LogitFilter:
+    def apply(self, logits: Tensor, tokens: Tensor) -> None:
+        """Apply any filtering or masking to logits in-place
+
+        Parameters
+        ----------
+        logits : Tensor, shape = (n_batch, vocab_size)
+            per-token logits of the probability distribution at the current step
+
+        tokens : Tensor, shape = (n_batch, current_sequence_length)
+            all tokens in the context so far, including the prefix and sot_sequence tokens
+
+        """
+        raise NotImplementedError
+
+
+class SuppressBlank(LogitFilter):
+    def __init__(self, tokenizer: Tokenizer, sample_begin: int):
+        self.tokenizer = tokenizer
+        self.sample_begin = sample_begin
+
+    def apply(self, logits: Tensor, tokens: Tensor):
+        if tokens.shape[1] == self.sample_begin:
+            logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf
+
+
+class SuppressTokens(LogitFilter):
+    def __init__(self, suppress_tokens: Sequence[int]):
+        self.suppress_tokens = list(suppress_tokens)
+
+    def apply(self, logits: Tensor, tokens: Tensor):
+        logits[:, self.suppress_tokens] = -np.inf
+
+
+class ApplyTimestampRules(LogitFilter):
+    def __init__(
+        self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int]
+    ):
+        self.tokenizer = tokenizer
+        self.sample_begin = sample_begin
+        self.max_initial_timestamp_index = max_initial_timestamp_index
+
+    def apply(self, logits: Tensor, tokens: Tensor):
+        # suppress <|notimestamps|> which is handled by without_timestamps
+        if self.tokenizer.no_timestamps is not None:
+            logits[:, self.tokenizer.no_timestamps] = -np.inf
+
+        # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
+        for k in range(tokens.shape[0]):
+            seq = [t for t in tokens[k, self.sample_begin :].tolist()]
+            last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin
+            penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin
+
+            if last_was_timestamp:
+                if penultimate_was_timestamp:  # has to be non-timestamp
+                    logits[k, self.tokenizer.timestamp_begin :] = -np.inf
+                else:  # cannot be normal text tokens
+                    logits[k, : self.tokenizer.eot] = -np.inf
+
+        # apply the `max_initial_timestamp` option
+        if tokens.shape[1] == self.sample_begin and self.max_initial_timestamp_index is not None:
+            last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index
+            logits[:, last_allowed + 1 :] = -np.inf
+
+        # if sum of probability over timestamps is above any other token, sample timestamp
+        logprobs = F.log_softmax(logits.float(), dim=-1)
+        for k in range(tokens.shape[0]):
+            timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1)
+            max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max()
+            if timestamp_logprob > max_text_token_logprob:
+                logits[k, : self.tokenizer.timestamp_begin] = -np.inf
+
+
+class DecodingTask:
+    inference: Inference
+    sequence_ranker: SequenceRanker
+    decoder: TokenDecoder
+    logit_filters: List[LogitFilter]
+
+    def __init__(self, model: "Whisper", options: DecodingOptions):
+        self.model = model
+
+        language = options.language or "en"
+        tokenizer = get_tokenizer(model.is_multilingual, language=language, task=options.task)
+        self.tokenizer: Tokenizer = tokenizer
+        self.options: DecodingOptions = self._verify_options(options)
+
+        self.n_group: int = options.beam_size or options.best_of or 1
+        self.n_ctx: int = model.dims.n_text_ctx
+        self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2
+
+        self.sot_sequence: Tuple[int] = tokenizer.sot_sequence
+        if self.options.without_timestamps:
+            self.sot_sequence = tokenizer.sot_sequence_including_notimestamps
+
+        self.initial_tokens: Tuple[int] = self._get_initial_tokens()
+        self.sample_begin: int = len(self.initial_tokens)
+        self.sot_index: int = self.initial_tokens.index(tokenizer.sot)
+
+        # inference: implements the forward pass through the decoder, including kv caching
+        self.inference = PyTorchInference(model, len(self.initial_tokens))
+
+        # sequence ranker: implements how to rank a group of sampled sequences
+        self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty)
+
+        # decoder: implements how to select the next tokens, given the autoregressive distribution
+        if options.beam_size is not None:
+            self.decoder = BeamSearchDecoder(
+                options.beam_size, tokenizer.eot, self.inference, options.patience
+            )
+        else:
+            self.decoder = GreedyDecoder(options.temperature, tokenizer.eot)
+
+        # logit filters: applies various rules to suppress or penalize certain tokens
+        self.logit_filters = []
+        if self.options.suppress_blank:
+            self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin))
+        if self.options.suppress_tokens:
+            self.logit_filters.append(SuppressTokens(self._get_suppress_tokens()))
+        if not options.without_timestamps:
+            precision = CHUNK_LENGTH / model.dims.n_audio_ctx  # usually 0.02 seconds
+            max_initial_timestamp_index = None
+            if options.max_initial_timestamp:
+                max_initial_timestamp_index = round(self.options.max_initial_timestamp / precision)
+            self.logit_filters.append(
+                ApplyTimestampRules(tokenizer, self.sample_begin, max_initial_timestamp_index)
+            )
+
+    def _verify_options(self, options: DecodingOptions) -> DecodingOptions:
+        if options.beam_size is not None and options.best_of is not None:
+            raise ValueError("beam_size and best_of can't be given together")
+        if options.temperature == 0:
+            if options.best_of is not None:
+                raise ValueError("best_of with greedy sampling (T=0) is not compatible")
+        if options.patience is not None and options.beam_size is None:
+            raise ValueError("patience requires beam_size to be given")
+        if options.length_penalty is not None and not (0 <= options.length_penalty <= 1):
+            raise ValueError("length_penalty (alpha) should be a value between 0 and 1")
+
+        return options
+
+    def _get_initial_tokens(self) -> Tuple[int]:
+        tokens = list(self.sot_sequence)
+        prefix = self.options.prefix
+        prompt = self.options.prompt
+
+        if prefix:
+            prefix_tokens = (
+                self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix
+            )
+            if self.sample_len is not None:
+                max_prefix_len = self.n_ctx // 2 - self.sample_len
+                prefix_tokens = prefix_tokens[-max_prefix_len:]
+            tokens = tokens + prefix_tokens
+
+        if prompt:
+            prompt_tokens = (
+                self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt
+            )
+            tokens = [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2 - 1) :] + tokens
+
+        return tuple(tokens)
+
+    def _get_suppress_tokens(self) -> Tuple[int]:
+        suppress_tokens = self.options.suppress_tokens
+
+        if isinstance(suppress_tokens, str):
+            suppress_tokens = [int(t) for t in suppress_tokens.split(",")]
+
+        if -1 in suppress_tokens:
+            suppress_tokens = [t for t in suppress_tokens if t >= 0]
+            suppress_tokens.extend(self.tokenizer.non_speech_tokens)
+        elif suppress_tokens is None or len(suppress_tokens) == 0:
+            suppress_tokens = []  # interpret empty string as an empty list
+        else:
+            assert isinstance(suppress_tokens, list), "suppress_tokens must be a list"
+
+        suppress_tokens.extend(
+            [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm]
+        )
+        if self.tokenizer.no_speech is not None:
+            # no-speech probability is collected separately
+            suppress_tokens.append(self.tokenizer.no_speech)
+
+        return tuple(sorted(set(suppress_tokens)))
+
+    def _get_audio_features(self, mel: Tensor, include_embeddings: bool = False):
+        if self.options.fp16:
+            mel = mel.half()
+
+        if mel.shape[-2:] == (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state):
+            # encoded audio features are given; skip audio encoding
+            audio_features = mel
+        else:
+            result = self.model.encoder(mel, include_embeddings)
+            if include_embeddings:
+                audio_features, embeddings = result
+            else:
+                audio_features = result
+
+        if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32):
+            return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}")
+
+        if include_embeddings:
+            return audio_features, embeddings
+        else:
+            return audio_features
+
+    def _detect_language(self, audio_features: Tensor, tokens: Tensor):
+        languages = [self.options.language] * audio_features.shape[0]
+        lang_probs = None
+
+        if self.options.language is None or self.options.task == "lang_id":
+            lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer)
+            languages = [max(probs, key=probs.get) for probs in lang_probs]
+            if self.options.language is None:
+                tokens[:, self.sot_index + 1] = lang_tokens  # write language tokens
+
+        return languages, lang_probs
+
+    def _main_loop(self, audio_features: Tensor, tokens: Tensor):
+        assert audio_features.shape[0] == tokens.shape[0]
+        n_batch = tokens.shape[0]
+        sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device)
+        no_speech_probs = [np.nan] * n_batch
+
+        try:
+            embeddings = []
+            for i in range(self.sample_len):
+                logits, token_embeddings = self.inference.logits(tokens, audio_features, include_embeddings=True)
+
+                if i == 0 and self.tokenizer.no_speech is not None:  # save no_speech_probs
+                    probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1)
+                    no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist()
+
+                # now we need to consider the logits at the last token only
+                logits = logits[:, -1]
+                token_embeddings = token_embeddings[:, :, -1]
+
+                # Append embeddings together
+                embeddings.append(token_embeddings)
+
+                # apply the logit filters, e.g. for suppressing or applying penalty to
+                for logit_filter in self.logit_filters:
+                    logit_filter.apply(logits, tokens)
+
+                # expand the tokens tensor with the selected next tokens
+                tokens, completed = self.decoder.update(tokens, logits, sum_logprobs)
+
+                if completed or tokens.shape[-1] > self.n_ctx:
+                    break
+        finally:
+            if completed:
+                embeddings = embeddings[:-1]
+            embeddings = np.stack(embeddings, 2)
+            self.inference.cleanup_caching()
+
+        return tokens, sum_logprobs, no_speech_probs, embeddings
+
+    @torch.no_grad()
+    def run(self, mel: Tensor) -> List[DecodingResult]:
+        self.decoder.reset()
+        tokenizer: Tokenizer = self.tokenizer
+        n_audio: int = mel.shape[0]
+
+        # encoder forward pass
+        forward_pass: Tuple[Tensor, np.ndarray] = self._get_audio_features(mel, include_embeddings=True)
+        audio_features, encoder_embeddings = forward_pass
+        tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1)
+
+        # detect language if requested, overwriting the language token
+        languages, language_probs = self._detect_language(audio_features, tokens)
+        if self.options.task == "lang_id":
+            return [
+                DecodingResult(audio_features=features, language=language, language_probs=probs)
+                for features, language, probs in zip(audio_features, languages, language_probs)
+            ]
+
+        # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling
+        audio_features = audio_features.repeat_interleave(self.n_group, dim=0)
+        tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device)
+
+        # call the main sampling loop
+        tokens, sum_logprobs, no_speech_probs, decoder_embeddings = self._main_loop(audio_features, tokens)
+
+        # reshape the tensors to have (n_audio, n_group) as the first two dimensions
+        audio_features = audio_features[:: self.n_group]
+        no_speech_probs = no_speech_probs[:: self.n_group]
+        assert audio_features.shape[0] == len(no_speech_probs) == n_audio
+
+        tokens = tokens.reshape(n_audio, self.n_group, -1)
+        sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group)
+
+        # get the final candidates for each group, and slice between the first sampled token and EOT
+        tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs)
+        tokens: List[List[Tensor]] = [
+            [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens
+        ]
+
+        # select the top-ranked sample in each group
+        selected = self.sequence_ranker.rank(tokens, sum_logprobs)
+        tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)]
+        texts: List[str] = [tokenizer.decode(t).strip() for t in tokens]
+
+        sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)]
+        avg_logprobs: List[float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)]
+
+        fields = (texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs)
+        if len(set(map(len, fields))) != 1:
+            raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}")
+
+        return [
+            DecodingResult(
+                audio_features=features,
+                language=language,
+                tokens=tokens,
+                text=text,
+                avg_logprob=avg_logprob,
+                no_speech_prob=no_speech_prob,
+                temperature=self.options.temperature,
+                compression_ratio=compression_ratio(text),
+                encoder_embeddings=encoder_embeddings,
+                decoder_embeddings=decoder_embeddings
+            )
+            for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields)
+        ]
+
+
+@torch.no_grad()
+def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]:
+    """
+    Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s).
+
+    Parameters
+    ----------
+    model: Whisper
+        the Whisper model instance
+
+    mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000)
+        A tensor containing the Mel spectrogram(s)
+
+    options: DecodingOptions
+        A dataclass that contains all necessary options for decoding 30-second segments
+
+    Returns
+    -------
+    result: Union[DecodingResult, List[DecodingResult]]
+        The result(s) of decoding contained in `DecodingResult` dataclass instance(s)
+    """
+    single = mel.ndim == 2
+    if single:
+        mel = mel.unsqueeze(0)
+
+    result = DecodingTask(model, options).run(mel)
+    
+    if single:
+        result = result[0]
+
+    return result
diff --git a/utils/digital_human/musetalk/whisper/whisper/model.py b/utils/digital_human/musetalk/whisper/whisper/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..acb6a0be88109a884b2c4d0e2d50daaf00870307
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/model.py
@@ -0,0 +1,290 @@
+from dataclasses import dataclass
+from typing import Dict
+from typing import Iterable, Optional
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+from torch import nn
+
+from .transcribe import transcribe as transcribe_function
+from .decoding import detect_language as detect_language_function, decode as decode_function
+
+
+@dataclass
+class ModelDimensions:
+    n_mels: int
+    n_audio_ctx: int
+    n_audio_state: int
+    n_audio_head: int
+    n_audio_layer: int
+    n_vocab: int
+    n_text_ctx: int
+    n_text_state: int
+    n_text_head: int
+    n_text_layer: int
+
+
+class LayerNorm(nn.LayerNorm):
+    def forward(self, x: Tensor) -> Tensor:
+        return super().forward(x.float()).type(x.dtype)
+
+
+class Linear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        return F.linear(
+            x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype)
+        )
+
+
+class Conv1d(nn.Conv1d):
+    def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
+        return super()._conv_forward(
+            x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
+        )
+
+
+def sinusoids(length, channels, max_timescale=10000):
+    """Returns sinusoids for positional embedding"""
+    assert channels % 2 == 0
+    log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+    inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
+    scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+    return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, n_state: int, n_head: int):
+        super().__init__()
+        self.n_head = n_head
+        self.query = Linear(n_state, n_state)
+        self.key = Linear(n_state, n_state, bias=False)
+        self.value = Linear(n_state, n_state)
+        self.out = Linear(n_state, n_state)
+
+    def forward(
+        self,
+        x: Tensor,
+        xa: Optional[Tensor] = None,
+        mask: Optional[Tensor] = None,
+        kv_cache: Optional[dict] = None,
+    ):
+        q = self.query(x)
+
+        if kv_cache is None or xa is None:
+            # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
+            # otherwise, perform key/value projections for self- or cross-attention as usual.
+            k = self.key(x if xa is None else xa)
+            v = self.value(x if xa is None else xa)
+        else:
+            # for cross-attention, calculate keys and values once and reuse in subsequent calls.
+            k = kv_cache.get(self.key, self.key(xa))
+            v = kv_cache.get(self.value, self.value(xa))
+
+        wv = self.qkv_attention(q, k, v, mask)
+        return self.out(wv)
+
+    def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None):
+        n_batch, n_ctx, n_state = q.shape
+        scale = (n_state // self.n_head) ** -0.25
+        q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
+        k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
+        v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
+
+        qk = q @ k
+        if mask is not None:
+            qk = qk + mask[:n_ctx, :n_ctx]
+
+        w = F.softmax(qk.float(), dim=-1).to(q.dtype)
+        return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2)
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
+        super().__init__()
+
+        self.attn = MultiHeadAttention(n_state, n_head)
+        self.attn_ln = LayerNorm(n_state)
+
+        self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None
+        self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
+
+        n_mlp = n_state * 4
+        self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
+        self.mlp_ln = LayerNorm(n_state)
+
+    def forward(
+        self,
+        x: Tensor,
+        xa: Optional[Tensor] = None,
+        mask: Optional[Tensor] = None,
+        kv_cache: Optional[dict] = None,
+    ):
+        x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)
+        if self.cross_attn:
+            x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)
+        x = x + self.mlp(self.mlp_ln(x))
+        return x
+
+
+class AudioEncoder(nn.Module):
+    def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
+        super().__init__()
+        self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
+        self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
+        self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
+
+        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
+            [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
+        )
+        self.ln_post = LayerNorm(n_state)
+
+    def forward(self, x: Tensor, include_embeddings: bool = False):
+        """
+        x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
+            the mel spectrogram of the audio
+        include_embeddings: bool
+            whether to include intermediate steps in the output
+        """
+        x = F.gelu(self.conv1(x))
+        x = F.gelu(self.conv2(x))
+        x = x.permute(0, 2, 1)
+
+        assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape"
+        x = (x + self.positional_embedding).to(x.dtype)
+
+        if include_embeddings:
+            embeddings = [x.cpu().detach().numpy()]
+
+        for block in self.blocks:
+            x = block(x)
+            if include_embeddings:
+                embeddings.append(x.cpu().detach().numpy())
+
+        x = self.ln_post(x)
+
+        if include_embeddings:
+            embeddings = np.stack(embeddings, axis=1)
+            return x, embeddings
+        else:
+            return x
+
+
+class TextDecoder(nn.Module):
+    def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
+        super().__init__()
+
+        self.token_embedding = nn.Embedding(n_vocab, n_state)
+        self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
+
+        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
+            [ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)]
+        )
+        self.ln = LayerNorm(n_state)
+
+        mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
+        self.register_buffer("mask", mask, persistent=False)
+
+    def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None, include_embeddings: bool = False):
+        """
+        x : torch.LongTensor, shape = (batch_size, <= n_ctx)
+            the text tokens
+        xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
+            the encoded audio features to be attended on
+        include_embeddings : bool
+            Whether to include intermediate values in the output to this function
+        """
+        offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
+        x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]]
+        x = x.to(xa.dtype)
+
+        if include_embeddings:
+            embeddings = [x.cpu().detach().numpy()]
+
+        for block in self.blocks:
+            x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
+            if include_embeddings:
+                embeddings.append(x.cpu().detach().numpy())
+
+        x = self.ln(x)
+        logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
+
+        if include_embeddings:
+            embeddings = np.stack(embeddings, axis=1)
+            return logits, embeddings
+        else:
+            return logits
+
+
+class Whisper(nn.Module):
+    def __init__(self, dims: ModelDimensions):
+        super().__init__()
+        self.dims = dims
+        self.encoder = AudioEncoder(
+            self.dims.n_mels,
+            self.dims.n_audio_ctx,
+            self.dims.n_audio_state,
+            self.dims.n_audio_head,
+            self.dims.n_audio_layer,
+        )
+        self.decoder = TextDecoder(
+            self.dims.n_vocab,
+            self.dims.n_text_ctx,
+            self.dims.n_text_state,
+            self.dims.n_text_head,
+            self.dims.n_text_layer,
+        )
+
+    def embed_audio(self, mel: torch.Tensor):
+        return self.encoder.forward(mel)
+
+    def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
+        return self.decoder.forward(tokens, audio_features)
+
+    def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]:
+        return self.decoder(tokens, self.encoder(mel))
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    @property
+    def is_multilingual(self):
+        return self.dims.n_vocab == 51865
+
+    def install_kv_cache_hooks(self, cache: Optional[dict] = None):
+        """
+        The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value
+        tensors calculated for the previous positions. This method returns a dictionary that stores
+        all caches, and the necessary hooks for the key and value projection modules that save the
+        intermediate tensors to be reused during later calculations.
+
+        Returns
+        -------
+        cache : Dict[nn.Module, torch.Tensor]
+            A dictionary object mapping the key/value projection modules to its cache
+        hooks : List[RemovableHandle]
+            List of PyTorch RemovableHandle objects to stop the hooks to be called
+        """
+        cache = {**cache} if cache is not None else {}
+        hooks = []
+
+        def save_to_cache(module, _, output):
+            if module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0]:
+                cache[module] = output  # save as-is, for the first token or cross attention
+            else:
+                cache[module] = torch.cat([cache[module], output], dim=1).detach()
+            return cache[module]
+
+        def install_hooks(layer: nn.Module):
+            if isinstance(layer, MultiHeadAttention):
+                hooks.append(layer.key.register_forward_hook(save_to_cache))
+                hooks.append(layer.value.register_forward_hook(save_to_cache))
+
+        self.decoder.apply(install_hooks)
+        return cache, hooks
+
+    detect_language = detect_language_function
+    transcribe = transcribe_function
+    decode = decode_function
diff --git a/utils/digital_human/musetalk/whisper/whisper/normalizers/__init__.py b/utils/digital_human/musetalk/whisper/whisper/normalizers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b10d5a9d6389368fe7f3bac38e3b3cd9cc1470f
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/normalizers/__init__.py
@@ -0,0 +1,2 @@
+from .basic import BasicTextNormalizer
+from .english import EnglishTextNormalizer
diff --git a/utils/digital_human/musetalk/whisper/whisper/normalizers/basic.py b/utils/digital_human/musetalk/whisper/whisper/normalizers/basic.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef8d249bfca2cd10bccdd56950504581e2598560
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/normalizers/basic.py
@@ -0,0 +1,71 @@
+import re
+import unicodedata
+
+import regex
+
+# non-ASCII letters that are not separated by "NFKD" normalization
+ADDITIONAL_DIACRITICS = {
+    "œ": "oe",
+    "Œ": "OE",
+    "ø": "o",
+    "Ø": "O",
+    "æ": "ae",
+    "Æ": "AE",
+    "ß": "ss",
+    "ẞ": "SS",
+    "đ": "d",
+    "Đ": "D",
+    "ð": "d",
+    "Ð": "D",
+    "þ": "th",
+    "Þ": "th",
+    "ł": "l",
+    "Ł": "L",
+}
+
+
+def remove_symbols_and_diacritics(s: str, keep=""):
+    """
+    Replace any other markers, symbols, and punctuations with a space,
+    and drop any diacritics (category 'Mn' and some manual mappings)
+    """
+    return "".join(
+        c
+        if c in keep
+        else ADDITIONAL_DIACRITICS[c]
+        if c in ADDITIONAL_DIACRITICS
+        else ""
+        if unicodedata.category(c) == "Mn"
+        else " "
+        if unicodedata.category(c)[0] in "MSP"
+        else c
+        for c in unicodedata.normalize("NFKD", s)
+    )
+
+
+def remove_symbols(s: str):
+    """
+    Replace any other markers, symbols, punctuations with a space, keeping diacritics
+    """
+    return "".join(
+        " " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKC", s)
+    )
+
+
+class BasicTextNormalizer:
+    def __init__(self, remove_diacritics: bool = False, split_letters: bool = False):
+        self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols
+        self.split_letters = split_letters
+
+    def __call__(self, s: str):
+        s = s.lower()
+        s = re.sub(r"[<\[][^>\]]*[>\]]", "", s)  # remove words between brackets
+        s = re.sub(r"\(([^)]+?)\)", "", s)  # remove words between parenthesis
+        s = self.clean(s).lower()
+
+        if self.split_letters:
+            s = " ".join(regex.findall(r"\X", s, regex.U))
+
+        s = re.sub(r"\s+", " ", s)  # replace any successive whitespace characters with a space
+
+        return s
diff --git a/utils/digital_human/musetalk/whisper/whisper/normalizers/english.json b/utils/digital_human/musetalk/whisper/whisper/normalizers/english.json
new file mode 100644
index 0000000000000000000000000000000000000000..bd84ae73faeb4dfa75214b99c4b392a51be90336
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/normalizers/english.json
@@ -0,0 +1,1742 @@
+{
+    "accessorise": "accessorize",
+    "accessorised": "accessorized",
+    "accessorises": "accessorizes",
+    "accessorising": "accessorizing",
+    "acclimatisation": "acclimatization",
+    "acclimatise": "acclimatize",
+    "acclimatised": "acclimatized",
+    "acclimatises": "acclimatizes",
+    "acclimatising": "acclimatizing",
+    "accoutrements": "accouterments",
+    "aeon": "eon",
+    "aeons": "eons",
+    "aerogramme": "aerogram",
+    "aerogrammes": "aerograms",
+    "aeroplane": "airplane",
+    "aeroplanes": "airplanes",
+    "aesthete": "esthete",
+    "aesthetes": "esthetes",
+    "aesthetic": "esthetic",
+    "aesthetically": "esthetically",
+    "aesthetics": "esthetics",
+    "aetiology": "etiology",
+    "ageing": "aging",
+    "aggrandisement": "aggrandizement",
+    "agonise": "agonize",
+    "agonised": "agonized",
+    "agonises": "agonizes",
+    "agonising": "agonizing",
+    "agonisingly": "agonizingly",
+    "almanack": "almanac",
+    "almanacks": "almanacs",
+    "aluminium": "aluminum",
+    "amortisable": "amortizable",
+    "amortisation": "amortization",
+    "amortisations": "amortizations",
+    "amortise": "amortize",
+    "amortised": "amortized",
+    "amortises": "amortizes",
+    "amortising": "amortizing",
+    "amphitheatre": "amphitheater",
+    "amphitheatres": "amphitheaters",
+    "anaemia": "anemia",
+    "anaemic": "anemic",
+    "anaesthesia": "anesthesia",
+    "anaesthetic": "anesthetic",
+    "anaesthetics": "anesthetics",
+    "anaesthetise": "anesthetize",
+    "anaesthetised": "anesthetized",
+    "anaesthetises": "anesthetizes",
+    "anaesthetising": "anesthetizing",
+    "anaesthetist": "anesthetist",
+    "anaesthetists": "anesthetists",
+    "anaesthetize": "anesthetize",
+    "anaesthetized": "anesthetized",
+    "anaesthetizes": "anesthetizes",
+    "anaesthetizing": "anesthetizing",
+    "analogue": "analog",
+    "analogues": "analogs",
+    "analyse": "analyze",
+    "analysed": "analyzed",
+    "analyses": "analyzes",
+    "analysing": "analyzing",
+    "anglicise": "anglicize",
+    "anglicised": "anglicized",
+    "anglicises": "anglicizes",
+    "anglicising": "anglicizing",
+    "annualised": "annualized",
+    "antagonise": "antagonize",
+    "antagonised": "antagonized",
+    "antagonises": "antagonizes",
+    "antagonising": "antagonizing",
+    "apologise": "apologize",
+    "apologised": "apologized",
+    "apologises": "apologizes",
+    "apologising": "apologizing",
+    "appal": "appall",
+    "appals": "appalls",
+    "appetiser": "appetizer",
+    "appetisers": "appetizers",
+    "appetising": "appetizing",
+    "appetisingly": "appetizingly",
+    "arbour": "arbor",
+    "arbours": "arbors",
+    "archeological": "archaeological",
+    "archaeologically": "archeologically",
+    "archaeologist": "archeologist",
+    "archaeologists": "archeologists",
+    "archaeology": "archeology</span>",
+    "ardour": "ardor",
+    "armour": "armor",
+    "armoured": "armored",
+    "armourer": "armorer",
+    "armourers": "armorers",
+    "armouries": "armories",
+    "armoury": "armory",
+    "artefact": "artifact",
+    "artefacts": "artifacts",
+    "authorise": "authorize",
+    "authorised": "authorized",
+    "authorises": "authorizes",
+    "authorising": "authorizing",
+    "axe": "ax",
+    "backpedalled": "backpedaled",
+    "backpedalling": "backpedaling",
+    "bannister": "banister",
+    "bannisters": "banisters",
+    "baptise": "baptize",
+    "baptised": "baptized",
+    "baptises": "baptizes",
+    "baptising": "baptizing",
+    "bastardise": "bastardize",
+    "bastardised": "bastardized",
+    "bastardises": "bastardizes",
+    "bastardising": "bastardizing",
+    "battleax": "battleaxe",
+    "baulk": "balk",
+    "baulked": "balked",
+    "baulking": "balking",
+    "baulks": "balks",
+    "bedevilled": "bedeviled",
+    "bedevilling": "bedeviling",
+    "behaviour": "behavior",
+    "behavioural": "behavioral",
+    "behaviourism": "behaviorism",
+    "behaviourist": "behaviorist",
+    "behaviourists": "behaviorists",
+    "behaviours": "behaviors",
+    "behove": "behoove",
+    "behoved": "behooved",
+    "behoves": "behooves",
+    "bejewelled": "bejeweled",
+    "belabour": "belabor",
+    "belaboured": "belabored",
+    "belabouring": "belaboring",
+    "belabours": "belabors",
+    "bevelled": "beveled",
+    "bevvies": "bevies",
+    "bevvy": "bevy",
+    "biassed": "biased",
+    "biassing": "biasing",
+    "bingeing": "binging",
+    "bougainvillaea": "bougainvillea",
+    "bougainvillaeas": "bougainvilleas",
+    "bowdlerise": "bowdlerize",
+    "bowdlerised": "bowdlerized",
+    "bowdlerises": "bowdlerizes",
+    "bowdlerising": "bowdlerizing",
+    "breathalyse": "breathalyze",
+    "breathalysed": "breathalyzed",
+    "breathalyser": "breathalyzer",
+    "breathalysers": "breathalyzers",
+    "breathalyses": "breathalyzes",
+    "breathalysing": "breathalyzing",
+    "brutalise": "brutalize",
+    "brutalised": "brutalized",
+    "brutalises": "brutalizes",
+    "brutalising": "brutalizing",
+    "busses": "buses",
+    "bussing": "busing",
+    "caesarean": "cesarean",
+    "caesareans": "cesareans",
+    "calibre": "caliber",
+    "calibres": "calibers",
+    "calliper": "caliper",
+    "callipers": "calipers",
+    "callisthenics": "calisthenics",
+    "canalise": "canalize",
+    "canalised": "canalized",
+    "canalises": "canalizes",
+    "canalising": "canalizing",
+    "cancelation": "cancellation",
+    "cancelations": "cancellations",
+    "cancelled": "canceled",
+    "cancelling": "canceling",
+    "candour": "candor",
+    "cannibalise": "cannibalize",
+    "cannibalised": "cannibalized",
+    "cannibalises": "cannibalizes",
+    "cannibalising": "cannibalizing",
+    "canonise": "canonize",
+    "canonised": "canonized",
+    "canonises": "canonizes",
+    "canonising": "canonizing",
+    "capitalise": "capitalize",
+    "capitalised": "capitalized",
+    "capitalises": "capitalizes",
+    "capitalising": "capitalizing",
+    "caramelise": "caramelize",
+    "caramelised": "caramelized",
+    "caramelises": "caramelizes",
+    "caramelising": "caramelizing",
+    "carbonise": "carbonize",
+    "carbonised": "carbonized",
+    "carbonises": "carbonizes",
+    "carbonising": "carbonizing",
+    "carolled": "caroled",
+    "carolling": "caroling",
+    "catalogue": "catalog",
+    "catalogued": "cataloged",
+    "catalogues": "catalogs",
+    "cataloguing": "cataloging",
+    "catalyse": "catalyze",
+    "catalysed": "catalyzed",
+    "catalyses": "catalyzes",
+    "catalysing": "catalyzing",
+    "categorise": "categorize",
+    "categorised": "categorized",
+    "categorises": "categorizes",
+    "categorising": "categorizing",
+    "cauterise": "cauterize",
+    "cauterised": "cauterized",
+    "cauterises": "cauterizes",
+    "cauterising": "cauterizing",
+    "cavilled": "caviled",
+    "cavilling": "caviling",
+    "centigramme": "centigram",
+    "centigrammes": "centigrams",
+    "centilitre": "centiliter",
+    "centilitres": "centiliters",
+    "centimetre": "centimeter",
+    "centimetres": "centimeters",
+    "centralise": "centralize",
+    "centralised": "centralized",
+    "centralises": "centralizes",
+    "centralising": "centralizing",
+    "centre": "center",
+    "centred": "centered",
+    "centrefold": "centerfold",
+    "centrefolds": "centerfolds",
+    "centrepiece": "centerpiece",
+    "centrepieces": "centerpieces",
+    "centres": "centers",
+    "channelled": "channeled",
+    "channelling": "channeling",
+    "characterise": "characterize",
+    "characterised": "characterized",
+    "characterises": "characterizes",
+    "characterising": "characterizing",
+    "cheque": "check",
+    "chequebook": "checkbook",
+    "chequebooks": "checkbooks",
+    "chequered": "checkered",
+    "cheques": "checks",
+    "chilli": "chili",
+    "chimaera": "chimera",
+    "chimaeras": "chimeras",
+    "chiselled": "chiseled",
+    "chiselling": "chiseling",
+    "circularise": "circularize",
+    "circularised": "circularized",
+    "circularises": "circularizes",
+    "circularising": "circularizing",
+    "civilise": "civilize",
+    "civilised": "civilized",
+    "civilises": "civilizes",
+    "civilising": "civilizing",
+    "clamour": "clamor",
+    "clamoured": "clamored",
+    "clamouring": "clamoring",
+    "clamours": "clamors",
+    "clangour": "clangor",
+    "clarinettist": "clarinetist",
+    "clarinettists": "clarinetists",
+    "collectivise": "collectivize",
+    "collectivised": "collectivized",
+    "collectivises": "collectivizes",
+    "collectivising": "collectivizing",
+    "colonisation": "colonization",
+    "colonise": "colonize",
+    "colonised": "colonized",
+    "coloniser": "colonizer",
+    "colonisers": "colonizers",
+    "colonises": "colonizes",
+    "colonising": "colonizing",
+    "colour": "color",
+    "colourant": "colorant",
+    "colourants": "colorants",
+    "coloured": "colored",
+    "coloureds": "coloreds",
+    "colourful": "colorful",
+    "colourfully": "colorfully",
+    "colouring": "coloring",
+    "colourize": "colorize",
+    "colourized": "colorized",
+    "colourizes": "colorizes",
+    "colourizing": "colorizing",
+    "colourless": "colorless",
+    "colours": "colors",
+    "commercialise": "commercialize",
+    "commercialised": "commercialized",
+    "commercialises": "commercializes",
+    "commercialising": "commercializing",
+    "compartmentalise": "compartmentalize",
+    "compartmentalised": "compartmentalized",
+    "compartmentalises": "compartmentalizes",
+    "compartmentalising": "compartmentalizing",
+    "computerise": "computerize",
+    "computerised": "computerized",
+    "computerises": "computerizes",
+    "computerising": "computerizing",
+    "conceptualise": "conceptualize",
+    "conceptualised": "conceptualized",
+    "conceptualises": "conceptualizes",
+    "conceptualising": "conceptualizing",
+    "connexion": "connection",
+    "connexions": "connections",
+    "contextualise": "contextualize",
+    "contextualised": "contextualized",
+    "contextualises": "contextualizes",
+    "contextualising": "contextualizing",
+    "cosier": "cozier",
+    "cosies": "cozies",
+    "cosiest": "coziest",
+    "cosily": "cozily",
+    "cosiness": "coziness",
+    "cosy": "cozy",
+    "councillor": "councilor",
+    "councillors": "councilors",
+    "counselled": "counseled",
+    "counselling": "counseling",
+    "counsellor": "counselor",
+    "counsellors": "counselors",
+    "crenelated": "crenellated",
+    "criminalise": "criminalize",
+    "criminalised": "criminalized",
+    "criminalises": "criminalizes",
+    "criminalising": "criminalizing",
+    "criticise": "criticize",
+    "criticised": "criticized",
+    "criticises": "criticizes",
+    "criticising": "criticizing",
+    "crueller": "crueler",
+    "cruellest": "cruelest",
+    "crystallisation": "crystallization",
+    "crystallise": "crystallize",
+    "crystallised": "crystallized",
+    "crystallises": "crystallizes",
+    "crystallising": "crystallizing",
+    "cudgelled": "cudgeled",
+    "cudgelling": "cudgeling",
+    "customise": "customize",
+    "customised": "customized",
+    "customises": "customizes",
+    "customising": "customizing",
+    "cypher": "cipher",
+    "cyphers": "ciphers",
+    "decentralisation": "decentralization",
+    "decentralise": "decentralize",
+    "decentralised": "decentralized",
+    "decentralises": "decentralizes",
+    "decentralising": "decentralizing",
+    "decriminalisation": "decriminalization",
+    "decriminalise": "decriminalize",
+    "decriminalised": "decriminalized",
+    "decriminalises": "decriminalizes",
+    "decriminalising": "decriminalizing",
+    "defence": "defense",
+    "defenceless": "defenseless",
+    "defences": "defenses",
+    "dehumanisation": "dehumanization",
+    "dehumanise": "dehumanize",
+    "dehumanised": "dehumanized",
+    "dehumanises": "dehumanizes",
+    "dehumanising": "dehumanizing",
+    "demeanour": "demeanor",
+    "demilitarisation": "demilitarization",
+    "demilitarise": "demilitarize",
+    "demilitarised": "demilitarized",
+    "demilitarises": "demilitarizes",
+    "demilitarising": "demilitarizing",
+    "demobilisation": "demobilization",
+    "demobilise": "demobilize",
+    "demobilised": "demobilized",
+    "demobilises": "demobilizes",
+    "demobilising": "demobilizing",
+    "democratisation": "democratization",
+    "democratise": "democratize",
+    "democratised": "democratized",
+    "democratises": "democratizes",
+    "democratising": "democratizing",
+    "demonise": "demonize",
+    "demonised": "demonized",
+    "demonises": "demonizes",
+    "demonising": "demonizing",
+    "demoralisation": "demoralization",
+    "demoralise": "demoralize",
+    "demoralised": "demoralized",
+    "demoralises": "demoralizes",
+    "demoralising": "demoralizing",
+    "denationalisation": "denationalization",
+    "denationalise": "denationalize",
+    "denationalised": "denationalized",
+    "denationalises": "denationalizes",
+    "denationalising": "denationalizing",
+    "deodorise": "deodorize",
+    "deodorised": "deodorized",
+    "deodorises": "deodorizes",
+    "deodorising": "deodorizing",
+    "depersonalise": "depersonalize",
+    "depersonalised": "depersonalized",
+    "depersonalises": "depersonalizes",
+    "depersonalising": "depersonalizing",
+    "deputise": "deputize",
+    "deputised": "deputized",
+    "deputises": "deputizes",
+    "deputising": "deputizing",
+    "desensitisation": "desensitization",
+    "desensitise": "desensitize",
+    "desensitised": "desensitized",
+    "desensitises": "desensitizes",
+    "desensitising": "desensitizing",
+    "destabilisation": "destabilization",
+    "destabilise": "destabilize",
+    "destabilised": "destabilized",
+    "destabilises": "destabilizes",
+    "destabilising": "destabilizing",
+    "dialled": "dialed",
+    "dialling": "dialing",
+    "dialogue": "dialog",
+    "dialogues": "dialogs",
+    "diarrhoea": "diarrhea",
+    "digitise": "digitize",
+    "digitised": "digitized",
+    "digitises": "digitizes",
+    "digitising": "digitizing",
+    "disc": "disk",
+    "discolour": "discolor",
+    "discoloured": "discolored",
+    "discolouring": "discoloring",
+    "discolours": "discolors",
+    "discs": "disks",
+    "disembowelled": "disemboweled",
+    "disembowelling": "disemboweling",
+    "disfavour": "disfavor",
+    "dishevelled": "disheveled",
+    "dishonour": "dishonor",
+    "dishonourable": "dishonorable",
+    "dishonourably": "dishonorably",
+    "dishonoured": "dishonored",
+    "dishonouring": "dishonoring",
+    "dishonours": "dishonors",
+    "disorganisation": "disorganization",
+    "disorganised": "disorganized",
+    "distil": "distill",
+    "distils": "distills",
+    "dramatisation": "dramatization",
+    "dramatisations": "dramatizations",
+    "dramatise": "dramatize",
+    "dramatised": "dramatized",
+    "dramatises": "dramatizes",
+    "dramatising": "dramatizing",
+    "draught": "draft",
+    "draughtboard": "draftboard",
+    "draughtboards": "draftboards",
+    "draughtier": "draftier",
+    "draughtiest": "draftiest",
+    "draughts": "drafts",
+    "draughtsman": "draftsman",
+    "draughtsmanship": "draftsmanship",
+    "draughtsmen": "draftsmen",
+    "draughtswoman": "draftswoman",
+    "draughtswomen": "draftswomen",
+    "draughty": "drafty",
+    "drivelled": "driveled",
+    "drivelling": "driveling",
+    "duelled": "dueled",
+    "duelling": "dueling",
+    "economise": "economize",
+    "economised": "economized",
+    "economises": "economizes",
+    "economising": "economizing",
+    "edoema": "edema",
+    "editorialise": "editorialize",
+    "editorialised": "editorialized",
+    "editorialises": "editorializes",
+    "editorialising": "editorializing",
+    "empathise": "empathize",
+    "empathised": "empathized",
+    "empathises": "empathizes",
+    "empathising": "empathizing",
+    "emphasise": "emphasize",
+    "emphasised": "emphasized",
+    "emphasises": "emphasizes",
+    "emphasising": "emphasizing",
+    "enamelled": "enameled",
+    "enamelling": "enameling",
+    "enamoured": "enamored",
+    "encyclopaedia": "encyclopedia",
+    "encyclopaedias": "encyclopedias",
+    "encyclopaedic": "encyclopedic",
+    "endeavour": "endeavor",
+    "endeavoured": "endeavored",
+    "endeavouring": "endeavoring",
+    "endeavours": "endeavors",
+    "energise": "energize",
+    "energised": "energized",
+    "energises": "energizes",
+    "energising": "energizing",
+    "enrol": "enroll",
+    "enrols": "enrolls",
+    "enthral": "enthrall",
+    "enthrals": "enthralls",
+    "epaulette": "epaulet",
+    "epaulettes": "epaulets",
+    "epicentre": "epicenter",
+    "epicentres": "epicenters",
+    "epilogue": "epilog",
+    "epilogues": "epilogs",
+    "epitomise": "epitomize",
+    "epitomised": "epitomized",
+    "epitomises": "epitomizes",
+    "epitomising": "epitomizing",
+    "equalisation": "equalization",
+    "equalise": "equalize",
+    "equalised": "equalized",
+    "equaliser": "equalizer",
+    "equalisers": "equalizers",
+    "equalises": "equalizes",
+    "equalising": "equalizing",
+    "eulogise": "eulogize",
+    "eulogised": "eulogized",
+    "eulogises": "eulogizes",
+    "eulogising": "eulogizing",
+    "evangelise": "evangelize",
+    "evangelised": "evangelized",
+    "evangelises": "evangelizes",
+    "evangelising": "evangelizing",
+    "exorcise": "exorcize",
+    "exorcised": "exorcized",
+    "exorcises": "exorcizes",
+    "exorcising": "exorcizing",
+    "extemporisation": "extemporization",
+    "extemporise": "extemporize",
+    "extemporised": "extemporized",
+    "extemporises": "extemporizes",
+    "extemporising": "extemporizing",
+    "externalisation": "externalization",
+    "externalisations": "externalizations",
+    "externalise": "externalize",
+    "externalised": "externalized",
+    "externalises": "externalizes",
+    "externalising": "externalizing",
+    "factorise": "factorize",
+    "factorised": "factorized",
+    "factorises": "factorizes",
+    "factorising": "factorizing",
+    "faecal": "fecal",
+    "faeces": "feces",
+    "familiarisation": "familiarization",
+    "familiarise": "familiarize",
+    "familiarised": "familiarized",
+    "familiarises": "familiarizes",
+    "familiarising": "familiarizing",
+    "fantasise": "fantasize",
+    "fantasised": "fantasized",
+    "fantasises": "fantasizes",
+    "fantasising": "fantasizing",
+    "favour": "favor",
+    "favourable": "favorable",
+    "favourably": "favorably",
+    "favoured": "favored",
+    "favouring": "favoring",
+    "favourite": "favorite",
+    "favourites": "favorites",
+    "favouritism": "favoritism",
+    "favours": "favors",
+    "feminise": "feminize",
+    "feminised": "feminized",
+    "feminises": "feminizes",
+    "feminising": "feminizing",
+    "fertilisation": "fertilization",
+    "fertilise": "fertilize",
+    "fertilised": "fertilized",
+    "fertiliser": "fertilizer",
+    "fertilisers": "fertilizers",
+    "fertilises": "fertilizes",
+    "fertilising": "fertilizing",
+    "fervour": "fervor",
+    "fibre": "fiber",
+    "fibreglass": "fiberglass",
+    "fibres": "fibers",
+    "fictionalisation": "fictionalization",
+    "fictionalisations": "fictionalizations",
+    "fictionalise": "fictionalize",
+    "fictionalised": "fictionalized",
+    "fictionalises": "fictionalizes",
+    "fictionalising": "fictionalizing",
+    "fillet": "filet",
+    "filleted": "fileted",
+    "filleting": "fileting",
+    "fillets": "filets",
+    "finalisation": "finalization",
+    "finalise": "finalize",
+    "finalised": "finalized",
+    "finalises": "finalizes",
+    "finalising": "finalizing",
+    "flautist": "flutist",
+    "flautists": "flutists",
+    "flavour": "flavor",
+    "flavoured": "flavored",
+    "flavouring": "flavoring",
+    "flavourings": "flavorings",
+    "flavourless": "flavorless",
+    "flavours": "flavors",
+    "flavoursome": "flavorsome",
+    "flyer / flier": "flier / flyer",
+    "foetal": "fetal",
+    "foetid": "fetid",
+    "foetus": "fetus",
+    "foetuses": "fetuses",
+    "formalisation": "formalization",
+    "formalise": "formalize",
+    "formalised": "formalized",
+    "formalises": "formalizes",
+    "formalising": "formalizing",
+    "fossilisation": "fossilization",
+    "fossilise": "fossilize",
+    "fossilised": "fossilized",
+    "fossilises": "fossilizes",
+    "fossilising": "fossilizing",
+    "fraternisation": "fraternization",
+    "fraternise": "fraternize",
+    "fraternised": "fraternized",
+    "fraternises": "fraternizes",
+    "fraternising": "fraternizing",
+    "fulfil": "fulfill",
+    "fulfilment": "fulfillment",
+    "fulfils": "fulfills",
+    "funnelled": "funneled",
+    "funnelling": "funneling",
+    "galvanise": "galvanize",
+    "galvanised": "galvanized",
+    "galvanises": "galvanizes",
+    "galvanising": "galvanizing",
+    "gambolled": "gamboled",
+    "gambolling": "gamboling",
+    "gaol": "jail",
+    "gaolbird": "jailbird",
+    "gaolbirds": "jailbirds",
+    "gaolbreak": "jailbreak",
+    "gaolbreaks": "jailbreaks",
+    "gaoled": "jailed",
+    "gaoler": "jailer",
+    "gaolers": "jailers",
+    "gaoling": "jailing",
+    "gaols": "jails",
+    "gasses": "gases",
+    "gage": "gauge",
+    "gaged": "gauged",
+    "gages": "gauges",
+    "gaging": "gauging",
+    "generalisation": "generalization",
+    "generalisations": "generalizations",
+    "generalise": "generalize",
+    "generalised": "generalized",
+    "generalises": "generalizes",
+    "generalising": "generalizing",
+    "ghettoise": "ghettoize",
+    "ghettoised": "ghettoized",
+    "ghettoises": "ghettoizes",
+    "ghettoising": "ghettoizing",
+    "gipsies": "gypsies",
+    "glamorise": "glamorize",
+    "glamorised": "glamorized",
+    "glamorises": "glamorizes",
+    "glamorising": "glamorizing",
+    "glamor": "glamour",
+    "globalisation": "globalization",
+    "globalise": "globalize",
+    "globalised": "globalized",
+    "globalises": "globalizes",
+    "globalising": "globalizing",
+    "glueing": "gluing",
+    "goitre": "goiter",
+    "goitres": "goiters",
+    "gonorrhoea": "gonorrhea",
+    "gramme": "gram",
+    "grammes": "grams",
+    "gravelled": "graveled",
+    "grey": "gray",
+    "greyed": "grayed",
+    "greying": "graying",
+    "greyish": "grayish",
+    "greyness": "grayness",
+    "greys": "grays",
+    "grovelled": "groveled",
+    "grovelling": "groveling",
+    "groyne": "groin",
+    "groynes": "groins",
+    "gruelling": "grueling",
+    "gruellingly": "gruelingly",
+    "gryphon": "griffin",
+    "gryphons": "griffins",
+    "gynaecological": "gynecological",
+    "gynaecologist": "gynecologist",
+    "gynaecologists": "gynecologists",
+    "gynaecology": "gynecology",
+    "haematological": "hematological",
+    "haematologist": "hematologist",
+    "haematologists": "hematologists",
+    "haematology": "hematology",
+    "haemoglobin": "hemoglobin",
+    "haemophilia": "hemophilia",
+    "haemophiliac": "hemophiliac",
+    "haemophiliacs": "hemophiliacs",
+    "haemorrhage": "hemorrhage",
+    "haemorrhaged": "hemorrhaged",
+    "haemorrhages": "hemorrhages",
+    "haemorrhaging": "hemorrhaging",
+    "haemorrhoids": "hemorrhoids",
+    "harbour": "harbor",
+    "harboured": "harbored",
+    "harbouring": "harboring",
+    "harbours": "harbors",
+    "harmonisation": "harmonization",
+    "harmonise": "harmonize",
+    "harmonised": "harmonized",
+    "harmonises": "harmonizes",
+    "harmonising": "harmonizing",
+    "homoeopath": "homeopath",
+    "homoeopathic": "homeopathic",
+    "homoeopaths": "homeopaths",
+    "homoeopathy": "homeopathy",
+    "homogenise": "homogenize",
+    "homogenised": "homogenized",
+    "homogenises": "homogenizes",
+    "homogenising": "homogenizing",
+    "honour": "honor",
+    "honourable": "honorable",
+    "honourably": "honorably",
+    "honoured": "honored",
+    "honouring": "honoring",
+    "honours": "honors",
+    "hospitalisation": "hospitalization",
+    "hospitalise": "hospitalize",
+    "hospitalised": "hospitalized",
+    "hospitalises": "hospitalizes",
+    "hospitalising": "hospitalizing",
+    "humanise": "humanize",
+    "humanised": "humanized",
+    "humanises": "humanizes",
+    "humanising": "humanizing",
+    "humour": "humor",
+    "humoured": "humored",
+    "humouring": "humoring",
+    "humourless": "humorless",
+    "humours": "humors",
+    "hybridise": "hybridize",
+    "hybridised": "hybridized",
+    "hybridises": "hybridizes",
+    "hybridising": "hybridizing",
+    "hypnotise": "hypnotize",
+    "hypnotised": "hypnotized",
+    "hypnotises": "hypnotizes",
+    "hypnotising": "hypnotizing",
+    "hypothesise": "hypothesize",
+    "hypothesised": "hypothesized",
+    "hypothesises": "hypothesizes",
+    "hypothesising": "hypothesizing",
+    "idealisation": "idealization",
+    "idealise": "idealize",
+    "idealised": "idealized",
+    "idealises": "idealizes",
+    "idealising": "idealizing",
+    "idolise": "idolize",
+    "idolised": "idolized",
+    "idolises": "idolizes",
+    "idolising": "idolizing",
+    "immobilisation": "immobilization",
+    "immobilise": "immobilize",
+    "immobilised": "immobilized",
+    "immobiliser": "immobilizer",
+    "immobilisers": "immobilizers",
+    "immobilises": "immobilizes",
+    "immobilising": "immobilizing",
+    "immortalise": "immortalize",
+    "immortalised": "immortalized",
+    "immortalises": "immortalizes",
+    "immortalising": "immortalizing",
+    "immunisation": "immunization",
+    "immunise": "immunize",
+    "immunised": "immunized",
+    "immunises": "immunizes",
+    "immunising": "immunizing",
+    "impanelled": "impaneled",
+    "impanelling": "impaneling",
+    "imperilled": "imperiled",
+    "imperilling": "imperiling",
+    "individualise": "individualize",
+    "individualised": "individualized",
+    "individualises": "individualizes",
+    "individualising": "individualizing",
+    "industrialise": "industrialize",
+    "industrialised": "industrialized",
+    "industrialises": "industrializes",
+    "industrialising": "industrializing",
+    "inflexion": "inflection",
+    "inflexions": "inflections",
+    "initialise": "initialize",
+    "initialised": "initialized",
+    "initialises": "initializes",
+    "initialising": "initializing",
+    "initialled": "initialed",
+    "initialling": "initialing",
+    "instal": "install",
+    "instalment": "installment",
+    "instalments": "installments",
+    "instals": "installs",
+    "instil": "instill",
+    "instils": "instills",
+    "institutionalisation": "institutionalization",
+    "institutionalise": "institutionalize",
+    "institutionalised": "institutionalized",
+    "institutionalises": "institutionalizes",
+    "institutionalising": "institutionalizing",
+    "intellectualise": "intellectualize",
+    "intellectualised": "intellectualized",
+    "intellectualises": "intellectualizes",
+    "intellectualising": "intellectualizing",
+    "internalisation": "internalization",
+    "internalise": "internalize",
+    "internalised": "internalized",
+    "internalises": "internalizes",
+    "internalising": "internalizing",
+    "internationalisation": "internationalization",
+    "internationalise": "internationalize",
+    "internationalised": "internationalized",
+    "internationalises": "internationalizes",
+    "internationalising": "internationalizing",
+    "ionisation": "ionization",
+    "ionise": "ionize",
+    "ionised": "ionized",
+    "ioniser": "ionizer",
+    "ionisers": "ionizers",
+    "ionises": "ionizes",
+    "ionising": "ionizing",
+    "italicise": "italicize",
+    "italicised": "italicized",
+    "italicises": "italicizes",
+    "italicising": "italicizing",
+    "itemise": "itemize",
+    "itemised": "itemized",
+    "itemises": "itemizes",
+    "itemising": "itemizing",
+    "jeopardise": "jeopardize",
+    "jeopardised": "jeopardized",
+    "jeopardises": "jeopardizes",
+    "jeopardising": "jeopardizing",
+    "jewelled": "jeweled",
+    "jeweller": "jeweler",
+    "jewellers": "jewelers",
+    "jewellery": "jewelry",
+    "judgement": "judgment",
+    "kilogramme": "kilogram",
+    "kilogrammes": "kilograms",
+    "kilometre": "kilometer",
+    "kilometres": "kilometers",
+    "labelled": "labeled",
+    "labelling": "labeling",
+    "labour": "labor",
+    "laboured": "labored",
+    "labourer": "laborer",
+    "labourers": "laborers",
+    "labouring": "laboring",
+    "labours": "labors",
+    "lacklustre": "lackluster",
+    "legalisation": "legalization",
+    "legalise": "legalize",
+    "legalised": "legalized",
+    "legalises": "legalizes",
+    "legalising": "legalizing",
+    "legitimise": "legitimize",
+    "legitimised": "legitimized",
+    "legitimises": "legitimizes",
+    "legitimising": "legitimizing",
+    "leukaemia": "leukemia",
+    "levelled": "leveled",
+    "leveller": "leveler",
+    "levellers": "levelers",
+    "levelling": "leveling",
+    "libelled": "libeled",
+    "libelling": "libeling",
+    "libellous": "libelous",
+    "liberalisation": "liberalization",
+    "liberalise": "liberalize",
+    "liberalised": "liberalized",
+    "liberalises": "liberalizes",
+    "liberalising": "liberalizing",
+    "licence": "license",
+    "licenced": "licensed",
+    "licences": "licenses",
+    "licencing": "licensing",
+    "likeable": "likable",
+    "lionisation": "lionization",
+    "lionise": "lionize",
+    "lionised": "lionized",
+    "lionises": "lionizes",
+    "lionising": "lionizing",
+    "liquidise": "liquidize",
+    "liquidised": "liquidized",
+    "liquidiser": "liquidizer",
+    "liquidisers": "liquidizers",
+    "liquidises": "liquidizes",
+    "liquidising": "liquidizing",
+    "litre": "liter",
+    "litres": "liters",
+    "localise": "localize",
+    "localised": "localized",
+    "localises": "localizes",
+    "localising": "localizing",
+    "louvre": "louver",
+    "louvred": "louvered",
+    "louvres": "louvers",
+    "lustre": "luster",
+    "magnetise": "magnetize",
+    "magnetised": "magnetized",
+    "magnetises": "magnetizes",
+    "magnetising": "magnetizing",
+    "manoeuvrability": "maneuverability",
+    "manoeuvrable": "maneuverable",
+    "manoeuvre": "maneuver",
+    "manoeuvred": "maneuvered",
+    "manoeuvres": "maneuvers",
+    "manoeuvring": "maneuvering",
+    "manoeuvrings": "maneuverings",
+    "marginalisation": "marginalization",
+    "marginalise": "marginalize",
+    "marginalised": "marginalized",
+    "marginalises": "marginalizes",
+    "marginalising": "marginalizing",
+    "marshalled": "marshaled",
+    "marshalling": "marshaling",
+    "marvelled": "marveled",
+    "marvelling": "marveling",
+    "marvellous": "marvelous",
+    "marvellously": "marvelously",
+    "materialisation": "materialization",
+    "materialise": "materialize",
+    "materialised": "materialized",
+    "materialises": "materializes",
+    "materialising": "materializing",
+    "maximisation": "maximization",
+    "maximise": "maximize",
+    "maximised": "maximized",
+    "maximises": "maximizes",
+    "maximising": "maximizing",
+    "meagre": "meager",
+    "mechanisation": "mechanization",
+    "mechanise": "mechanize",
+    "mechanised": "mechanized",
+    "mechanises": "mechanizes",
+    "mechanising": "mechanizing",
+    "mediaeval": "medieval",
+    "memorialise": "memorialize",
+    "memorialised": "memorialized",
+    "memorialises": "memorializes",
+    "memorialising": "memorializing",
+    "memorise": "memorize",
+    "memorised": "memorized",
+    "memorises": "memorizes",
+    "memorising": "memorizing",
+    "mesmerise": "mesmerize",
+    "mesmerised": "mesmerized",
+    "mesmerises": "mesmerizes",
+    "mesmerising": "mesmerizing",
+    "metabolise": "metabolize",
+    "metabolised": "metabolized",
+    "metabolises": "metabolizes",
+    "metabolising": "metabolizing",
+    "metre": "meter",
+    "metres": "meters",
+    "micrometre": "micrometer",
+    "micrometres": "micrometers",
+    "militarise": "militarize",
+    "militarised": "militarized",
+    "militarises": "militarizes",
+    "militarising": "militarizing",
+    "milligramme": "milligram",
+    "milligrammes": "milligrams",
+    "millilitre": "milliliter",
+    "millilitres": "milliliters",
+    "millimetre": "millimeter",
+    "millimetres": "millimeters",
+    "miniaturisation": "miniaturization",
+    "miniaturise": "miniaturize",
+    "miniaturised": "miniaturized",
+    "miniaturises": "miniaturizes",
+    "miniaturising": "miniaturizing",
+    "minibusses": "minibuses",
+    "minimise": "minimize",
+    "minimised": "minimized",
+    "minimises": "minimizes",
+    "minimising": "minimizing",
+    "misbehaviour": "misbehavior",
+    "misdemeanour": "misdemeanor",
+    "misdemeanours": "misdemeanors",
+    "misspelt": "misspelled",
+    "mitre": "miter",
+    "mitres": "miters",
+    "mobilisation": "mobilization",
+    "mobilise": "mobilize",
+    "mobilised": "mobilized",
+    "mobilises": "mobilizes",
+    "mobilising": "mobilizing",
+    "modelled": "modeled",
+    "modeller": "modeler",
+    "modellers": "modelers",
+    "modelling": "modeling",
+    "modernise": "modernize",
+    "modernised": "modernized",
+    "modernises": "modernizes",
+    "modernising": "modernizing",
+    "moisturise": "moisturize",
+    "moisturised": "moisturized",
+    "moisturiser": "moisturizer",
+    "moisturisers": "moisturizers",
+    "moisturises": "moisturizes",
+    "moisturising": "moisturizing",
+    "monologue": "monolog",
+    "monologues": "monologs",
+    "monopolisation": "monopolization",
+    "monopolise": "monopolize",
+    "monopolised": "monopolized",
+    "monopolises": "monopolizes",
+    "monopolising": "monopolizing",
+    "moralise": "moralize",
+    "moralised": "moralized",
+    "moralises": "moralizes",
+    "moralising": "moralizing",
+    "motorised": "motorized",
+    "mould": "mold",
+    "moulded": "molded",
+    "moulder": "molder",
+    "mouldered": "moldered",
+    "mouldering": "moldering",
+    "moulders": "molders",
+    "mouldier": "moldier",
+    "mouldiest": "moldiest",
+    "moulding": "molding",
+    "mouldings": "moldings",
+    "moulds": "molds",
+    "mouldy": "moldy",
+    "moult": "molt",
+    "moulted": "molted",
+    "moulting": "molting",
+    "moults": "molts",
+    "moustache": "mustache",
+    "moustached": "mustached",
+    "moustaches": "mustaches",
+    "moustachioed": "mustachioed",
+    "multicoloured": "multicolored",
+    "nationalisation": "nationalization",
+    "nationalisations": "nationalizations",
+    "nationalise": "nationalize",
+    "nationalised": "nationalized",
+    "nationalises": "nationalizes",
+    "nationalising": "nationalizing",
+    "naturalisation": "naturalization",
+    "naturalise": "naturalize",
+    "naturalised": "naturalized",
+    "naturalises": "naturalizes",
+    "naturalising": "naturalizing",
+    "neighbour": "neighbor",
+    "neighbourhood": "neighborhood",
+    "neighbourhoods": "neighborhoods",
+    "neighbouring": "neighboring",
+    "neighbourliness": "neighborliness",
+    "neighbourly": "neighborly",
+    "neighbours": "neighbors",
+    "neutralisation": "neutralization",
+    "neutralise": "neutralize",
+    "neutralised": "neutralized",
+    "neutralises": "neutralizes",
+    "neutralising": "neutralizing",
+    "normalisation": "normalization",
+    "normalise": "normalize",
+    "normalised": "normalized",
+    "normalises": "normalizes",
+    "normalising": "normalizing",
+    "odour": "odor",
+    "odourless": "odorless",
+    "odours": "odors",
+    "oesophagus": "esophagus",
+    "oesophaguses": "esophaguses",
+    "oestrogen": "estrogen",
+    "offence": "offense",
+    "offences": "offenses",
+    "omelette": "omelet",
+    "omelettes": "omelets",
+    "optimise": "optimize",
+    "optimised": "optimized",
+    "optimises": "optimizes",
+    "optimising": "optimizing",
+    "organisation": "organization",
+    "organisational": "organizational",
+    "organisations": "organizations",
+    "organise": "organize",
+    "organised": "organized",
+    "organiser": "organizer",
+    "organisers": "organizers",
+    "organises": "organizes",
+    "organising": "organizing",
+    "orthopaedic": "orthopedic",
+    "orthopaedics": "orthopedics",
+    "ostracise": "ostracize",
+    "ostracised": "ostracized",
+    "ostracises": "ostracizes",
+    "ostracising": "ostracizing",
+    "outmanoeuvre": "outmaneuver",
+    "outmanoeuvred": "outmaneuvered",
+    "outmanoeuvres": "outmaneuvers",
+    "outmanoeuvring": "outmaneuvering",
+    "overemphasise": "overemphasize",
+    "overemphasised": "overemphasized",
+    "overemphasises": "overemphasizes",
+    "overemphasising": "overemphasizing",
+    "oxidisation": "oxidization",
+    "oxidise": "oxidize",
+    "oxidised": "oxidized",
+    "oxidises": "oxidizes",
+    "oxidising": "oxidizing",
+    "paederast": "pederast",
+    "paederasts": "pederasts",
+    "paediatric": "pediatric",
+    "paediatrician": "pediatrician",
+    "paediatricians": "pediatricians",
+    "paediatrics": "pediatrics",
+    "paedophile": "pedophile",
+    "paedophiles": "pedophiles",
+    "paedophilia": "pedophilia",
+    "palaeolithic": "paleolithic",
+    "palaeontologist": "paleontologist",
+    "palaeontologists": "paleontologists",
+    "palaeontology": "paleontology",
+    "panelled": "paneled",
+    "panelling": "paneling",
+    "panellist": "panelist",
+    "panellists": "panelists",
+    "paralyse": "paralyze",
+    "paralysed": "paralyzed",
+    "paralyses": "paralyzes",
+    "paralysing": "paralyzing",
+    "parcelled": "parceled",
+    "parcelling": "parceling",
+    "parlour": "parlor",
+    "parlours": "parlors",
+    "particularise": "particularize",
+    "particularised": "particularized",
+    "particularises": "particularizes",
+    "particularising": "particularizing",
+    "passivisation": "passivization",
+    "passivise": "passivize",
+    "passivised": "passivized",
+    "passivises": "passivizes",
+    "passivising": "passivizing",
+    "pasteurisation": "pasteurization",
+    "pasteurise": "pasteurize",
+    "pasteurised": "pasteurized",
+    "pasteurises": "pasteurizes",
+    "pasteurising": "pasteurizing",
+    "patronise": "patronize",
+    "patronised": "patronized",
+    "patronises": "patronizes",
+    "patronising": "patronizing",
+    "patronisingly": "patronizingly",
+    "pedalled": "pedaled",
+    "pedalling": "pedaling",
+    "pedestrianisation": "pedestrianization",
+    "pedestrianise": "pedestrianize",
+    "pedestrianised": "pedestrianized",
+    "pedestrianises": "pedestrianizes",
+    "pedestrianising": "pedestrianizing",
+    "penalise": "penalize",
+    "penalised": "penalized",
+    "penalises": "penalizes",
+    "penalising": "penalizing",
+    "pencilled": "penciled",
+    "pencilling": "penciling",
+    "personalise": "personalize",
+    "personalised": "personalized",
+    "personalises": "personalizes",
+    "personalising": "personalizing",
+    "pharmacopoeia": "pharmacopeia",
+    "pharmacopoeias": "pharmacopeias",
+    "philosophise": "philosophize",
+    "philosophised": "philosophized",
+    "philosophises": "philosophizes",
+    "philosophising": "philosophizing",
+    "philtre": "filter",
+    "philtres": "filters",
+    "phoney": "phony",
+    "plagiarise": "plagiarize",
+    "plagiarised": "plagiarized",
+    "plagiarises": "plagiarizes",
+    "plagiarising": "plagiarizing",
+    "plough": "plow",
+    "ploughed": "plowed",
+    "ploughing": "plowing",
+    "ploughman": "plowman",
+    "ploughmen": "plowmen",
+    "ploughs": "plows",
+    "ploughshare": "plowshare",
+    "ploughshares": "plowshares",
+    "polarisation": "polarization",
+    "polarise": "polarize",
+    "polarised": "polarized",
+    "polarises": "polarizes",
+    "polarising": "polarizing",
+    "politicisation": "politicization",
+    "politicise": "politicize",
+    "politicised": "politicized",
+    "politicises": "politicizes",
+    "politicising": "politicizing",
+    "popularisation": "popularization",
+    "popularise": "popularize",
+    "popularised": "popularized",
+    "popularises": "popularizes",
+    "popularising": "popularizing",
+    "pouffe": "pouf",
+    "pouffes": "poufs",
+    "practise": "practice",
+    "practised": "practiced",
+    "practises": "practices",
+    "practising": "practicing",
+    "praesidium": "presidium",
+    "praesidiums": "presidiums",
+    "pressurisation": "pressurization",
+    "pressurise": "pressurize",
+    "pressurised": "pressurized",
+    "pressurises": "pressurizes",
+    "pressurising": "pressurizing",
+    "pretence": "pretense",
+    "pretences": "pretenses",
+    "primaeval": "primeval",
+    "prioritisation": "prioritization",
+    "prioritise": "prioritize",
+    "prioritised": "prioritized",
+    "prioritises": "prioritizes",
+    "prioritising": "prioritizing",
+    "privatisation": "privatization",
+    "privatisations": "privatizations",
+    "privatise": "privatize",
+    "privatised": "privatized",
+    "privatises": "privatizes",
+    "privatising": "privatizing",
+    "professionalisation": "professionalization",
+    "professionalise": "professionalize",
+    "professionalised": "professionalized",
+    "professionalises": "professionalizes",
+    "professionalising": "professionalizing",
+    "programme": "program",
+    "programmes": "programs",
+    "prologue": "prolog",
+    "prologues": "prologs",
+    "propagandise": "propagandize",
+    "propagandised": "propagandized",
+    "propagandises": "propagandizes",
+    "propagandising": "propagandizing",
+    "proselytise": "proselytize",
+    "proselytised": "proselytized",
+    "proselytiser": "proselytizer",
+    "proselytisers": "proselytizers",
+    "proselytises": "proselytizes",
+    "proselytising": "proselytizing",
+    "psychoanalyse": "psychoanalyze",
+    "psychoanalysed": "psychoanalyzed",
+    "psychoanalyses": "psychoanalyzes",
+    "psychoanalysing": "psychoanalyzing",
+    "publicise": "publicize",
+    "publicised": "publicized",
+    "publicises": "publicizes",
+    "publicising": "publicizing",
+    "pulverisation": "pulverization",
+    "pulverise": "pulverize",
+    "pulverised": "pulverized",
+    "pulverises": "pulverizes",
+    "pulverising": "pulverizing",
+    "pummelled": "pummel",
+    "pummelling": "pummeled",
+    "pyjama": "pajama",
+    "pyjamas": "pajamas",
+    "pzazz": "pizzazz",
+    "quarrelled": "quarreled",
+    "quarrelling": "quarreling",
+    "radicalise": "radicalize",
+    "radicalised": "radicalized",
+    "radicalises": "radicalizes",
+    "radicalising": "radicalizing",
+    "rancour": "rancor",
+    "randomise": "randomize",
+    "randomised": "randomized",
+    "randomises": "randomizes",
+    "randomising": "randomizing",
+    "rationalisation": "rationalization",
+    "rationalisations": "rationalizations",
+    "rationalise": "rationalize",
+    "rationalised": "rationalized",
+    "rationalises": "rationalizes",
+    "rationalising": "rationalizing",
+    "ravelled": "raveled",
+    "ravelling": "raveling",
+    "realisable": "realizable",
+    "realisation": "realization",
+    "realisations": "realizations",
+    "realise": "realize",
+    "realised": "realized",
+    "realises": "realizes",
+    "realising": "realizing",
+    "recognisable": "recognizable",
+    "recognisably": "recognizably",
+    "recognisance": "recognizance",
+    "recognise": "recognize",
+    "recognised": "recognized",
+    "recognises": "recognizes",
+    "recognising": "recognizing",
+    "reconnoitre": "reconnoiter",
+    "reconnoitred": "reconnoitered",
+    "reconnoitres": "reconnoiters",
+    "reconnoitring": "reconnoitering",
+    "refuelled": "refueled",
+    "refuelling": "refueling",
+    "regularisation": "regularization",
+    "regularise": "regularize",
+    "regularised": "regularized",
+    "regularises": "regularizes",
+    "regularising": "regularizing",
+    "remodelled": "remodeled",
+    "remodelling": "remodeling",
+    "remould": "remold",
+    "remoulded": "remolded",
+    "remoulding": "remolding",
+    "remoulds": "remolds",
+    "reorganisation": "reorganization",
+    "reorganisations": "reorganizations",
+    "reorganise": "reorganize",
+    "reorganised": "reorganized",
+    "reorganises": "reorganizes",
+    "reorganising": "reorganizing",
+    "revelled": "reveled",
+    "reveller": "reveler",
+    "revellers": "revelers",
+    "revelling": "reveling",
+    "revitalise": "revitalize",
+    "revitalised": "revitalized",
+    "revitalises": "revitalizes",
+    "revitalising": "revitalizing",
+    "revolutionise": "revolutionize",
+    "revolutionised": "revolutionized",
+    "revolutionises": "revolutionizes",
+    "revolutionising": "revolutionizing",
+    "rhapsodise": "rhapsodize",
+    "rhapsodised": "rhapsodized",
+    "rhapsodises": "rhapsodizes",
+    "rhapsodising": "rhapsodizing",
+    "rigour": "rigor",
+    "rigours": "rigors",
+    "ritualised": "ritualized",
+    "rivalled": "rivaled",
+    "rivalling": "rivaling",
+    "romanticise": "romanticize",
+    "romanticised": "romanticized",
+    "romanticises": "romanticizes",
+    "romanticising": "romanticizing",
+    "rumour": "rumor",
+    "rumoured": "rumored",
+    "rumours": "rumors",
+    "sabre": "saber",
+    "sabres": "sabers",
+    "saltpetre": "saltpeter",
+    "sanitise": "sanitize",
+    "sanitised": "sanitized",
+    "sanitises": "sanitizes",
+    "sanitising": "sanitizing",
+    "satirise": "satirize",
+    "satirised": "satirized",
+    "satirises": "satirizes",
+    "satirising": "satirizing",
+    "saviour": "savior",
+    "saviours": "saviors",
+    "savour": "savor",
+    "savoured": "savored",
+    "savouries": "savories",
+    "savouring": "savoring",
+    "savours": "savors",
+    "savoury": "savory",
+    "scandalise": "scandalize",
+    "scandalised": "scandalized",
+    "scandalises": "scandalizes",
+    "scandalising": "scandalizing",
+    "sceptic": "skeptic",
+    "sceptical": "skeptical",
+    "sceptically": "skeptically",
+    "scepticism": "skepticism",
+    "sceptics": "skeptics",
+    "sceptre": "scepter",
+    "sceptres": "scepters",
+    "scrutinise": "scrutinize",
+    "scrutinised": "scrutinized",
+    "scrutinises": "scrutinizes",
+    "scrutinising": "scrutinizing",
+    "secularisation": "secularization",
+    "secularise": "secularize",
+    "secularised": "secularized",
+    "secularises": "secularizes",
+    "secularising": "secularizing",
+    "sensationalise": "sensationalize",
+    "sensationalised": "sensationalized",
+    "sensationalises": "sensationalizes",
+    "sensationalising": "sensationalizing",
+    "sensitise": "sensitize",
+    "sensitised": "sensitized",
+    "sensitises": "sensitizes",
+    "sensitising": "sensitizing",
+    "sentimentalise": "sentimentalize",
+    "sentimentalised": "sentimentalized",
+    "sentimentalises": "sentimentalizes",
+    "sentimentalising": "sentimentalizing",
+    "sepulchre": "sepulcher",
+    "sepulchres": "sepulchers",
+    "serialisation": "serialization",
+    "serialisations": "serializations",
+    "serialise": "serialize",
+    "serialised": "serialized",
+    "serialises": "serializes",
+    "serialising": "serializing",
+    "sermonise": "sermonize",
+    "sermonised": "sermonized",
+    "sermonises": "sermonizes",
+    "sermonising": "sermonizing",
+    "sheikh": "sheik",
+    "shovelled": "shoveled",
+    "shovelling": "shoveling",
+    "shrivelled": "shriveled",
+    "shrivelling": "shriveling",
+    "signalise": "signalize",
+    "signalised": "signalized",
+    "signalises": "signalizes",
+    "signalising": "signalizing",
+    "signalled": "signaled",
+    "signalling": "signaling",
+    "smoulder": "smolder",
+    "smouldered": "smoldered",
+    "smouldering": "smoldering",
+    "smoulders": "smolders",
+    "snivelled": "sniveled",
+    "snivelling": "sniveling",
+    "snorkelled": "snorkeled",
+    "snorkelling": "snorkeling",
+    "snowplough": "snowplow",
+    "snowploughs": "snowplow",
+    "socialisation": "socialization",
+    "socialise": "socialize",
+    "socialised": "socialized",
+    "socialises": "socializes",
+    "socialising": "socializing",
+    "sodomise": "sodomize",
+    "sodomised": "sodomized",
+    "sodomises": "sodomizes",
+    "sodomising": "sodomizing",
+    "solemnise": "solemnize",
+    "solemnised": "solemnized",
+    "solemnises": "solemnizes",
+    "solemnising": "solemnizing",
+    "sombre": "somber",
+    "specialisation": "specialization",
+    "specialisations": "specializations",
+    "specialise": "specialize",
+    "specialised": "specialized",
+    "specialises": "specializes",
+    "specialising": "specializing",
+    "spectre": "specter",
+    "spectres": "specters",
+    "spiralled": "spiraled",
+    "spiralling": "spiraling",
+    "splendour": "splendor",
+    "splendours": "splendors",
+    "squirrelled": "squirreled",
+    "squirrelling": "squirreling",
+    "stabilisation": "stabilization",
+    "stabilise": "stabilize",
+    "stabilised": "stabilized",
+    "stabiliser": "stabilizer",
+    "stabilisers": "stabilizers",
+    "stabilises": "stabilizes",
+    "stabilising": "stabilizing",
+    "standardisation": "standardization",
+    "standardise": "standardize",
+    "standardised": "standardized",
+    "standardises": "standardizes",
+    "standardising": "standardizing",
+    "stencilled": "stenciled",
+    "stencilling": "stenciling",
+    "sterilisation": "sterilization",
+    "sterilisations": "sterilizations",
+    "sterilise": "sterilize",
+    "sterilised": "sterilized",
+    "steriliser": "sterilizer",
+    "sterilisers": "sterilizers",
+    "sterilises": "sterilizes",
+    "sterilising": "sterilizing",
+    "stigmatisation": "stigmatization",
+    "stigmatise": "stigmatize",
+    "stigmatised": "stigmatized",
+    "stigmatises": "stigmatizes",
+    "stigmatising": "stigmatizing",
+    "storey": "story",
+    "storeys": "stories",
+    "subsidisation": "subsidization",
+    "subsidise": "subsidize",
+    "subsidised": "subsidized",
+    "subsidiser": "subsidizer",
+    "subsidisers": "subsidizers",
+    "subsidises": "subsidizes",
+    "subsidising": "subsidizing",
+    "succour": "succor",
+    "succoured": "succored",
+    "succouring": "succoring",
+    "succours": "succors",
+    "sulphate": "sulfate",
+    "sulphates": "sulfates",
+    "sulphide": "sulfide",
+    "sulphides": "sulfides",
+    "sulphur": "sulfur",
+    "sulphurous": "sulfurous",
+    "summarise": "summarize",
+    "summarised": "summarized",
+    "summarises": "summarizes",
+    "summarising": "summarizing",
+    "swivelled": "swiveled",
+    "swivelling": "swiveling",
+    "symbolise": "symbolize",
+    "symbolised": "symbolized",
+    "symbolises": "symbolizes",
+    "symbolising": "symbolizing",
+    "sympathise": "sympathize",
+    "sympathised": "sympathized",
+    "sympathiser": "sympathizer",
+    "sympathisers": "sympathizers",
+    "sympathises": "sympathizes",
+    "sympathising": "sympathizing",
+    "synchronisation": "synchronization",
+    "synchronise": "synchronize",
+    "synchronised": "synchronized",
+    "synchronises": "synchronizes",
+    "synchronising": "synchronizing",
+    "synthesise": "synthesize",
+    "synthesised": "synthesized",
+    "synthesiser": "synthesizer",
+    "synthesisers": "synthesizers",
+    "synthesises": "synthesizes",
+    "synthesising": "synthesizing",
+    "syphon": "siphon",
+    "syphoned": "siphoned",
+    "syphoning": "siphoning",
+    "syphons": "siphons",
+    "systematisation": "systematization",
+    "systematise": "systematize",
+    "systematised": "systematized",
+    "systematises": "systematizes",
+    "systematising": "systematizing",
+    "tantalise": "tantalize",
+    "tantalised": "tantalized",
+    "tantalises": "tantalizes",
+    "tantalising": "tantalizing",
+    "tantalisingly": "tantalizingly",
+    "tasselled": "tasseled",
+    "technicolour": "technicolor",
+    "temporise": "temporize",
+    "temporised": "temporized",
+    "temporises": "temporizes",
+    "temporising": "temporizing",
+    "tenderise": "tenderize",
+    "tenderised": "tenderized",
+    "tenderises": "tenderizes",
+    "tenderising": "tenderizing",
+    "terrorise": "terrorize",
+    "terrorised": "terrorized",
+    "terrorises": "terrorizes",
+    "terrorising": "terrorizing",
+    "theatre": "theater",
+    "theatregoer": "theatergoer",
+    "theatregoers": "theatergoers",
+    "theatres": "theaters",
+    "theorise": "theorize",
+    "theorised": "theorized",
+    "theorises": "theorizes",
+    "theorising": "theorizing",
+    "tonne": "ton",
+    "tonnes": "tons",
+    "towelled": "toweled",
+    "towelling": "toweling",
+    "toxaemia": "toxemia",
+    "tranquillise": "tranquilize",
+    "tranquillised": "tranquilized",
+    "tranquilliser": "tranquilizer",
+    "tranquillisers": "tranquilizers",
+    "tranquillises": "tranquilizes",
+    "tranquillising": "tranquilizing",
+    "tranquillity": "tranquility",
+    "tranquillize": "tranquilize",
+    "tranquillized": "tranquilized",
+    "tranquillizer": "tranquilizer",
+    "tranquillizers": "tranquilizers",
+    "tranquillizes": "tranquilizes",
+    "tranquillizing": "tranquilizing",
+    "tranquilly": "tranquility",
+    "transistorised": "transistorized",
+    "traumatise": "traumatize",
+    "traumatised": "traumatized",
+    "traumatises": "traumatizes",
+    "traumatising": "traumatizing",
+    "travelled": "traveled",
+    "traveller": "traveler",
+    "travellers": "travelers",
+    "travelling": "traveling",
+    "travelog": "travelogue",
+    "travelogs": "travelogues",
+    "trialled": "trialed",
+    "trialling": "trialing",
+    "tricolour": "tricolor",
+    "tricolours": "tricolors",
+    "trivialise": "trivialize",
+    "trivialised": "trivialized",
+    "trivialises": "trivializes",
+    "trivialising": "trivializing",
+    "tumour": "tumor",
+    "tumours": "tumors",
+    "tunnelled": "tunneled",
+    "tunnelling": "tunneling",
+    "tyrannise": "tyrannize",
+    "tyrannised": "tyrannized",
+    "tyrannises": "tyrannizes",
+    "tyrannising": "tyrannizing",
+    "tyre": "tire",
+    "tyres": "tires",
+    "unauthorised": "unauthorized",
+    "uncivilised": "uncivilized",
+    "underutilised": "underutilized",
+    "unequalled": "unequaled",
+    "unfavourable": "unfavorable",
+    "unfavourably": "unfavorably",
+    "unionisation": "unionization",
+    "unionise": "unionize",
+    "unionised": "unionized",
+    "unionises": "unionizes",
+    "unionising": "unionizing",
+    "unorganised": "unorganized",
+    "unravelled": "unraveled",
+    "unravelling": "unraveling",
+    "unrecognisable": "unrecognizable",
+    "unrecognised": "unrecognized",
+    "unrivalled": "unrivaled",
+    "unsavoury": "unsavory",
+    "untrammelled": "untrammeled",
+    "urbanisation": "urbanization",
+    "urbanise": "urbanize",
+    "urbanised": "urbanized",
+    "urbanises": "urbanizes",
+    "urbanising": "urbanizing",
+    "utilisable": "utilizable",
+    "utilisation": "utilization",
+    "utilise": "utilize",
+    "utilised": "utilized",
+    "utilises": "utilizes",
+    "utilising": "utilizing",
+    "valour": "valor",
+    "vandalise": "vandalize",
+    "vandalised": "vandalized",
+    "vandalises": "vandalizes",
+    "vandalising": "vandalizing",
+    "vaporisation": "vaporization",
+    "vaporise": "vaporize",
+    "vaporised": "vaporized",
+    "vaporises": "vaporizes",
+    "vaporising": "vaporizing",
+    "vapour": "vapor",
+    "vapours": "vapors",
+    "verbalise": "verbalize",
+    "verbalised": "verbalized",
+    "verbalises": "verbalizes",
+    "verbalising": "verbalizing",
+    "victimisation": "victimization",
+    "victimise": "victimize",
+    "victimised": "victimized",
+    "victimises": "victimizes",
+    "victimising": "victimizing",
+    "videodisc": "videodisk",
+    "videodiscs": "videodisks",
+    "vigour": "vigor",
+    "visualisation": "visualization",
+    "visualisations": "visualizations",
+    "visualise": "visualize",
+    "visualised": "visualized",
+    "visualises": "visualizes",
+    "visualising": "visualizing",
+    "vocalisation": "vocalization",
+    "vocalisations": "vocalizations",
+    "vocalise": "vocalize",
+    "vocalised": "vocalized",
+    "vocalises": "vocalizes",
+    "vocalising": "vocalizing",
+    "vulcanised": "vulcanized",
+    "vulgarisation": "vulgarization",
+    "vulgarise": "vulgarize",
+    "vulgarised": "vulgarized",
+    "vulgarises": "vulgarizes",
+    "vulgarising": "vulgarizing",
+    "waggon": "wagon",
+    "waggons": "wagons",
+    "watercolour": "watercolor",
+    "watercolours": "watercolors",
+    "weaselled": "weaseled",
+    "weaselling": "weaseling",
+    "westernisation": "westernization",
+    "westernise": "westernize",
+    "westernised": "westernized",
+    "westernises": "westernizes",
+    "westernising": "westernizing",
+    "womanise": "womanize",
+    "womanised": "womanized",
+    "womaniser": "womanizer",
+    "womanisers": "womanizers",
+    "womanises": "womanizes",
+    "womanising": "womanizing",
+    "woollen": "woolen",
+    "woollens": "woolens",
+    "woollies": "woolies",
+    "woolly": "wooly",
+    "worshipped": "worshiped",
+    "worshipping": "worshiping",
+    "worshipper": "worshiper",
+    "yodelled": "yodeled",
+    "yodelling": "yodeling",
+    "yoghourt": "yogurt",
+    "yoghourts": "yogurts",
+    "yoghurt": "yogurt",
+    "yoghurts": "yogurts",
+    "mhm": "hmm",
+    "mm": "hmm",
+    "mmm": "hmm"
+}
\ No newline at end of file
diff --git a/utils/digital_human/musetalk/whisper/whisper/normalizers/english.py b/utils/digital_human/musetalk/whisper/whisper/normalizers/english.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5c2bb4ebef1a04b9ab14970c10c3fb0d4e948d4
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/normalizers/english.py
@@ -0,0 +1,543 @@
+import json
+import os
+import re
+from fractions import Fraction
+from typing import Iterator, List, Match, Optional, Union
+
+from more_itertools import windowed
+
+from .basic import remove_symbols_and_diacritics
+
+
+class EnglishNumberNormalizer:
+    """
+    Convert any spelled-out numbers into arabic numbers, while handling:
+
+    - remove any commas
+    - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc.
+    - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars`
+    - spell out `one` and `ones`
+    - interpret successive single-digit numbers as nominal: `one oh one` -> `101`
+    """
+
+    def __init__(self):
+        super().__init__()
+
+        self.zeros = {"o", "oh", "zero"}
+        self.ones = {
+            name: i
+            for i, name in enumerate(
+                [
+                    "one",
+                    "two",
+                    "three",
+                    "four",
+                    "five",
+                    "six",
+                    "seven",
+                    "eight",
+                    "nine",
+                    "ten",
+                    "eleven",
+                    "twelve",
+                    "thirteen",
+                    "fourteen",
+                    "fifteen",
+                    "sixteen",
+                    "seventeen",
+                    "eighteen",
+                    "nineteen",
+                ],
+                start=1,
+            )
+        }
+        self.ones_plural = {
+            "sixes" if name == "six" else name + "s": (value, "s")
+            for name, value in self.ones.items()
+        }
+        self.ones_ordinal = {
+            "zeroth": (0, "th"),
+            "first": (1, "st"),
+            "second": (2, "nd"),
+            "third": (3, "rd"),
+            "fifth": (5, "th"),
+            "twelfth": (12, "th"),
+            **{
+                name + ("h" if name.endswith("t") else "th"): (value, "th")
+                for name, value in self.ones.items()
+                if value > 3 and value != 5 and value != 12
+            },
+        }
+        self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal}
+
+        self.tens = {
+            "twenty": 20,
+            "thirty": 30,
+            "forty": 40,
+            "fifty": 50,
+            "sixty": 60,
+            "seventy": 70,
+            "eighty": 80,
+            "ninety": 90,
+        }
+        self.tens_plural = {
+            name.replace("y", "ies"): (value, "s") for name, value in self.tens.items()
+        }
+        self.tens_ordinal = {
+            name.replace("y", "ieth"): (value, "th") for name, value in self.tens.items()
+        }
+        self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal}
+
+        self.multipliers = {
+            "hundred": 100,
+            "thousand": 1_000,
+            "million": 1_000_000,
+            "billion": 1_000_000_000,
+            "trillion": 1_000_000_000_000,
+            "quadrillion": 1_000_000_000_000_000,
+            "quintillion": 1_000_000_000_000_000_000,
+            "sextillion": 1_000_000_000_000_000_000_000,
+            "septillion": 1_000_000_000_000_000_000_000_000,
+            "octillion": 1_000_000_000_000_000_000_000_000_000,
+            "nonillion": 1_000_000_000_000_000_000_000_000_000_000,
+            "decillion": 1_000_000_000_000_000_000_000_000_000_000_000,
+        }
+        self.multipliers_plural = {
+            name + "s": (value, "s") for name, value in self.multipliers.items()
+        }
+        self.multipliers_ordinal = {
+            name + "th": (value, "th") for name, value in self.multipliers.items()
+        }
+        self.multipliers_suffixed = {**self.multipliers_plural, **self.multipliers_ordinal}
+        self.decimals = {*self.ones, *self.tens, *self.zeros}
+
+        self.preceding_prefixers = {
+            "minus": "-",
+            "negative": "-",
+            "plus": "+",
+            "positive": "+",
+        }
+        self.following_prefixers = {
+            "pound": "£",
+            "pounds": "£",
+            "euro": "€",
+            "euros": "€",
+            "dollar": "$",
+            "dollars": "$",
+            "cent": "¢",
+            "cents": "¢",
+        }
+        self.prefixes = set(
+            list(self.preceding_prefixers.values()) + list(self.following_prefixers.values())
+        )
+        self.suffixers = {
+            "per": {"cent": "%"},
+            "percent": "%",
+        }
+        self.specials = {"and", "double", "triple", "point"}
+
+        self.words = set(
+            [
+                key
+                for mapping in [
+                    self.zeros,
+                    self.ones,
+                    self.ones_suffixed,
+                    self.tens,
+                    self.tens_suffixed,
+                    self.multipliers,
+                    self.multipliers_suffixed,
+                    self.preceding_prefixers,
+                    self.following_prefixers,
+                    self.suffixers,
+                    self.specials,
+                ]
+                for key in mapping
+            ]
+        )
+        self.literal_words = {"one", "ones"}
+
+    def process_words(self, words: List[str]) -> Iterator[str]:
+        prefix: Optional[str] = None
+        value: Optional[Union[str, int]] = None
+        skip = False
+
+        def to_fraction(s: str):
+            try:
+                return Fraction(s)
+            except ValueError:
+                return None
+
+        def output(result: Union[str, int]):
+            nonlocal prefix, value
+            result = str(result)
+            if prefix is not None:
+                result = prefix + result
+            value = None
+            prefix = None
+            return result
+
+        if len(words) == 0:
+            return
+
+        for prev, current, next in windowed([None] + words + [None], 3):
+            if skip:
+                skip = False
+                continue
+
+            next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next)
+            has_prefix = current[0] in self.prefixes
+            current_without_prefix = current[1:] if has_prefix else current
+            if re.match(r"^\d+(\.\d+)?$", current_without_prefix):
+                # arabic numbers (potentially with signs and fractions)
+                f = to_fraction(current_without_prefix)
+                assert f is not None
+                if value is not None:
+                    if isinstance(value, str) and value.endswith("."):
+                        # concatenate decimals / ip address components
+                        value = str(value) + str(current)
+                        continue
+                    else:
+                        yield output(value)
+
+                prefix = current[0] if has_prefix else prefix
+                if f.denominator == 1:
+                    value = f.numerator  # store integers as int
+                else:
+                    value = current_without_prefix
+            elif current not in self.words:
+                # non-numeric words
+                if value is not None:
+                    yield output(value)
+                yield output(current)
+            elif current in self.zeros:
+                value = str(value or "") + "0"
+            elif current in self.ones:
+                ones = self.ones[current]
+
+                if value is None:
+                    value = ones
+                elif isinstance(value, str) or prev in self.ones:
+                    if prev in self.tens and ones < 10:  # replace the last zero with the digit
+                        assert value[-1] == "0"
+                        value = value[:-1] + str(ones)
+                    else:
+                        value = str(value) + str(ones)
+                elif ones < 10:
+                    if value % 10 == 0:
+                        value += ones
+                    else:
+                        value = str(value) + str(ones)
+                else:  # eleven to nineteen
+                    if value % 100 == 0:
+                        value += ones
+                    else:
+                        value = str(value) + str(ones)
+            elif current in self.ones_suffixed:
+                # ordinal or cardinal; yield the number right away
+                ones, suffix = self.ones_suffixed[current]
+                if value is None:
+                    yield output(str(ones) + suffix)
+                elif isinstance(value, str) or prev in self.ones:
+                    if prev in self.tens and ones < 10:
+                        assert value[-1] == "0"
+                        yield output(value[:-1] + str(ones) + suffix)
+                    else:
+                        yield output(str(value) + str(ones) + suffix)
+                elif ones < 10:
+                    if value % 10 == 0:
+                        yield output(str(value + ones) + suffix)
+                    else:
+                        yield output(str(value) + str(ones) + suffix)
+                else:  # eleven to nineteen
+                    if value % 100 == 0:
+                        yield output(str(value + ones) + suffix)
+                    else:
+                        yield output(str(value) + str(ones) + suffix)
+                value = None
+            elif current in self.tens:
+                tens = self.tens[current]
+                if value is None:
+                    value = tens
+                elif isinstance(value, str):
+                    value = str(value) + str(tens)
+                else:
+                    if value % 100 == 0:
+                        value += tens
+                    else:
+                        value = str(value) + str(tens)
+            elif current in self.tens_suffixed:
+                # ordinal or cardinal; yield the number right away
+                tens, suffix = self.tens_suffixed[current]
+                if value is None:
+                    yield output(str(tens) + suffix)
+                elif isinstance(value, str):
+                    yield output(str(value) + str(tens) + suffix)
+                else:
+                    if value % 100 == 0:
+                        yield output(str(value + tens) + suffix)
+                    else:
+                        yield output(str(value) + str(tens) + suffix)
+            elif current in self.multipliers:
+                multiplier = self.multipliers[current]
+                if value is None:
+                    value = multiplier
+                elif isinstance(value, str) or value == 0:
+                    f = to_fraction(value)
+                    p = f * multiplier if f is not None else None
+                    if f is not None and p.denominator == 1:
+                        value = p.numerator
+                    else:
+                        yield output(value)
+                        value = multiplier
+                else:
+                    before = value // 1000 * 1000
+                    residual = value % 1000
+                    value = before + residual * multiplier
+            elif current in self.multipliers_suffixed:
+                multiplier, suffix = self.multipliers_suffixed[current]
+                if value is None:
+                    yield output(str(multiplier) + suffix)
+                elif isinstance(value, str):
+                    f = to_fraction(value)
+                    p = f * multiplier if f is not None else None
+                    if f is not None and p.denominator == 1:
+                        yield output(str(p.numerator) + suffix)
+                    else:
+                        yield output(value)
+                        yield output(str(multiplier) + suffix)
+                else:  # int
+                    before = value // 1000 * 1000
+                    residual = value % 1000
+                    value = before + residual * multiplier
+                    yield output(str(value) + suffix)
+                value = None
+            elif current in self.preceding_prefixers:
+                # apply prefix (positive, minus, etc.) if it precedes a number
+                if value is not None:
+                    yield output(value)
+
+                if next in self.words or next_is_numeric:
+                    prefix = self.preceding_prefixers[current]
+                else:
+                    yield output(current)
+            elif current in self.following_prefixers:
+                # apply prefix (dollars, cents, etc.) only after a number
+                if value is not None:
+                    prefix = self.following_prefixers[current]
+                    yield output(value)
+                else:
+                    yield output(current)
+            elif current in self.suffixers:
+                # apply suffix symbols (percent -> '%')
+                if value is not None:
+                    suffix = self.suffixers[current]
+                    if isinstance(suffix, dict):
+                        if next in suffix:
+                            yield output(str(value) + suffix[next])
+                            skip = True
+                        else:
+                            yield output(value)
+                            yield output(current)
+                    else:
+                        yield output(str(value) + suffix)
+                else:
+                    yield output(current)
+            elif current in self.specials:
+                if next not in self.words and not next_is_numeric:
+                    # apply special handling only if the next word can be numeric
+                    if value is not None:
+                        yield output(value)
+                    yield output(current)
+                elif current == "and":
+                    # ignore "and" after hundreds, thousands, etc.
+                    if prev not in self.multipliers:
+                        if value is not None:
+                            yield output(value)
+                        yield output(current)
+                elif current == "double" or current == "triple":
+                    if next in self.ones or next in self.zeros:
+                        repeats = 2 if current == "double" else 3
+                        ones = self.ones.get(next, 0)
+                        value = str(value or "") + str(ones) * repeats
+                        skip = True
+                    else:
+                        if value is not None:
+                            yield output(value)
+                        yield output(current)
+                elif current == "point":
+                    if next in self.decimals or next_is_numeric:
+                        value = str(value or "") + "."
+                else:
+                    # should all have been covered at this point
+                    raise ValueError(f"Unexpected token: {current}")
+            else:
+                # all should have been covered at this point
+                raise ValueError(f"Unexpected token: {current}")
+
+        if value is not None:
+            yield output(value)
+
+    def preprocess(self, s: str):
+        # replace "<number> and a half" with "<number> point five"
+        results = []
+
+        segments = re.split(r"\band\s+a\s+half\b", s)
+        for i, segment in enumerate(segments):
+            if len(segment.strip()) == 0:
+                continue
+            if i == len(segments) - 1:
+                results.append(segment)
+            else:
+                results.append(segment)
+                last_word = segment.rsplit(maxsplit=2)[-1]
+                if last_word in self.decimals or last_word in self.multipliers:
+                    results.append("point five")
+                else:
+                    results.append("and a half")
+
+        s = " ".join(results)
+
+        # put a space at number/letter boundary
+        s = re.sub(r"([a-z])([0-9])", r"\1 \2", s)
+        s = re.sub(r"([0-9])([a-z])", r"\1 \2", s)
+
+        # but remove spaces which could be a suffix
+        s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s)
+
+        return s
+
+    def postprocess(self, s: str):
+        def combine_cents(m: Match):
+            try:
+                currency = m.group(1)
+                integer = m.group(2)
+                cents = int(m.group(3))
+                return f"{currency}{integer}.{cents:02d}"
+            except ValueError:
+                return m.string
+
+        def extract_cents(m: Match):
+            try:
+                return f"¢{int(m.group(1))}"
+            except ValueError:
+                return m.string
+
+        # apply currency postprocessing; "$2 and ¢7" -> "$2.07"
+        s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s)
+        s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s)
+
+        # write "one(s)" instead of "1(s)", just for the readability
+        s = re.sub(r"\b1(s?)\b", r"one\1", s)
+
+        return s
+
+    def __call__(self, s: str):
+        s = self.preprocess(s)
+        s = " ".join(word for word in self.process_words(s.split()) if word is not None)
+        s = self.postprocess(s)
+
+        return s
+
+
+class EnglishSpellingNormalizer:
+    """
+    Applies British-American spelling mappings as listed in [1].
+
+    [1] https://www.tysto.com/uk-us-spelling-list.html
+    """
+
+    def __init__(self):
+        mapping_path = os.path.join(os.path.dirname(__file__), "english.json")
+        self.mapping = json.load(open(mapping_path))
+
+    def __call__(self, s: str):
+        return " ".join(self.mapping.get(word, word) for word in s.split())
+
+
+class EnglishTextNormalizer:
+    def __init__(self):
+        self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b"
+        self.replacers = {
+            # common contractions
+            r"\bwon't\b": "will not",
+            r"\bcan't\b": "can not",
+            r"\blet's\b": "let us",
+            r"\bain't\b": "aint",
+            r"\by'all\b": "you all",
+            r"\bwanna\b": "want to",
+            r"\bgotta\b": "got to",
+            r"\bgonna\b": "going to",
+            r"\bi'ma\b": "i am going to",
+            r"\bimma\b": "i am going to",
+            r"\bwoulda\b": "would have",
+            r"\bcoulda\b": "could have",
+            r"\bshoulda\b": "should have",
+            r"\bma'am\b": "madam",
+            # contractions in titles/prefixes
+            r"\bmr\b": "mister ",
+            r"\bmrs\b": "missus ",
+            r"\bst\b": "saint ",
+            r"\bdr\b": "doctor ",
+            r"\bprof\b": "professor ",
+            r"\bcapt\b": "captain ",
+            r"\bgov\b": "governor ",
+            r"\bald\b": "alderman ",
+            r"\bgen\b": "general ",
+            r"\bsen\b": "senator ",
+            r"\brep\b": "representative ",
+            r"\bpres\b": "president ",
+            r"\brev\b": "reverend ",
+            r"\bhon\b": "honorable ",
+            r"\basst\b": "assistant ",
+            r"\bassoc\b": "associate ",
+            r"\blt\b": "lieutenant ",
+            r"\bcol\b": "colonel ",
+            r"\bjr\b": "junior ",
+            r"\bsr\b": "senior ",
+            r"\besq\b": "esquire ",
+            # prefect tenses, ideally it should be any past participles, but it's harder..
+            r"'d been\b": " had been",
+            r"'s been\b": " has been",
+            r"'d gone\b": " had gone",
+            r"'s gone\b": " has gone",
+            r"'d done\b": " had done",  # "'s done" is ambiguous
+            r"'s got\b": " has got",
+            # general contractions
+            r"n't\b": " not",
+            r"'re\b": " are",
+            r"'s\b": " is",
+            r"'d\b": " would",
+            r"'ll\b": " will",
+            r"'t\b": " not",
+            r"'ve\b": " have",
+            r"'m\b": " am",
+        }
+        self.standardize_numbers = EnglishNumberNormalizer()
+        self.standardize_spellings = EnglishSpellingNormalizer()
+
+    def __call__(self, s: str):
+        s = s.lower()
+
+        s = re.sub(r"[<\[][^>\]]*[>\]]", "", s)  # remove words between brackets
+        s = re.sub(r"\(([^)]+?)\)", "", s)  # remove words between parenthesis
+        s = re.sub(self.ignore_patterns, "", s)
+        s = re.sub(r"\s+'", "'", s)  # standardize when there's a space before an apostrophe
+
+        for pattern, replacement in self.replacers.items():
+            s = re.sub(pattern, replacement, s)
+
+        s = re.sub(r"(\d),(\d)", r"\1\2", s)  # remove commas between digits
+        s = re.sub(r"\.([^0-9]|$)", r" \1", s)  # remove periods not followed by numbers
+        s = remove_symbols_and_diacritics(s, keep=".%$¢€£")  # keep some symbols for numerics
+
+        s = self.standardize_numbers(s)
+        s = self.standardize_spellings(s)
+
+        # now remove prefix/suffix symbols that are not preceded/followed by numbers
+        s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s)
+        s = re.sub(r"([^0-9])%", r"\1 ", s)
+
+        s = re.sub(r"\s+", " ", s)  # replace any successive whitespace characters with a space
+
+        return s
diff --git a/utils/digital_human/musetalk/whisper/whisper/tokenizer.py b/utils/digital_human/musetalk/whisper/whisper/tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..88142c43c9ca0d951a36b5b30baf54dd7bc7efbf
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/tokenizer.py
@@ -0,0 +1,331 @@
+import os
+from dataclasses import dataclass
+from functools import lru_cache
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from transformers import GPT2TokenizerFast
+
+LANGUAGES = {
+    "en": "english",
+    "zh": "chinese",
+    "de": "german",
+    "es": "spanish",
+    "ru": "russian",
+    "ko": "korean",
+    "fr": "french",
+    "ja": "japanese",
+    "pt": "portuguese",
+    "tr": "turkish",
+    "pl": "polish",
+    "ca": "catalan",
+    "nl": "dutch",
+    "ar": "arabic",
+    "sv": "swedish",
+    "it": "italian",
+    "id": "indonesian",
+    "hi": "hindi",
+    "fi": "finnish",
+    "vi": "vietnamese",
+    "iw": "hebrew",
+    "uk": "ukrainian",
+    "el": "greek",
+    "ms": "malay",
+    "cs": "czech",
+    "ro": "romanian",
+    "da": "danish",
+    "hu": "hungarian",
+    "ta": "tamil",
+    "no": "norwegian",
+    "th": "thai",
+    "ur": "urdu",
+    "hr": "croatian",
+    "bg": "bulgarian",
+    "lt": "lithuanian",
+    "la": "latin",
+    "mi": "maori",
+    "ml": "malayalam",
+    "cy": "welsh",
+    "sk": "slovak",
+    "te": "telugu",
+    "fa": "persian",
+    "lv": "latvian",
+    "bn": "bengali",
+    "sr": "serbian",
+    "az": "azerbaijani",
+    "sl": "slovenian",
+    "kn": "kannada",
+    "et": "estonian",
+    "mk": "macedonian",
+    "br": "breton",
+    "eu": "basque",
+    "is": "icelandic",
+    "hy": "armenian",
+    "ne": "nepali",
+    "mn": "mongolian",
+    "bs": "bosnian",
+    "kk": "kazakh",
+    "sq": "albanian",
+    "sw": "swahili",
+    "gl": "galician",
+    "mr": "marathi",
+    "pa": "punjabi",
+    "si": "sinhala",
+    "km": "khmer",
+    "sn": "shona",
+    "yo": "yoruba",
+    "so": "somali",
+    "af": "afrikaans",
+    "oc": "occitan",
+    "ka": "georgian",
+    "be": "belarusian",
+    "tg": "tajik",
+    "sd": "sindhi",
+    "gu": "gujarati",
+    "am": "amharic",
+    "yi": "yiddish",
+    "lo": "lao",
+    "uz": "uzbek",
+    "fo": "faroese",
+    "ht": "haitian creole",
+    "ps": "pashto",
+    "tk": "turkmen",
+    "nn": "nynorsk",
+    "mt": "maltese",
+    "sa": "sanskrit",
+    "lb": "luxembourgish",
+    "my": "myanmar",
+    "bo": "tibetan",
+    "tl": "tagalog",
+    "mg": "malagasy",
+    "as": "assamese",
+    "tt": "tatar",
+    "haw": "hawaiian",
+    "ln": "lingala",
+    "ha": "hausa",
+    "ba": "bashkir",
+    "jw": "javanese",
+    "su": "sundanese",
+}
+
+# language code lookup by name, with a few language aliases
+TO_LANGUAGE_CODE = {
+    **{language: code for code, language in LANGUAGES.items()},
+    "burmese": "my",
+    "valencian": "ca",
+    "flemish": "nl",
+    "haitian": "ht",
+    "letzeburgesch": "lb",
+    "pushto": "ps",
+    "panjabi": "pa",
+    "moldavian": "ro",
+    "moldovan": "ro",
+    "sinhalese": "si",
+    "castilian": "es",
+}
+
+
+@dataclass(frozen=True)
+class Tokenizer:
+    """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens"""
+
+    tokenizer: "GPT2TokenizerFast"
+    language: Optional[str]
+    sot_sequence: Tuple[int]
+
+    def encode(self, text, **kwargs):
+        return self.tokenizer.encode(text, **kwargs)
+
+    def decode(self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs):
+        return self.tokenizer.decode(token_ids, **kwargs)
+
+    def decode_with_timestamps(self, tokens) -> str:
+        """
+        Timestamp tokens are above the special tokens' id range and are ignored by `decode()`.
+        This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
+        """
+        outputs = [[]]
+        for token in tokens:
+            if token >= self.timestamp_begin:
+                timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>"
+                outputs.append(timestamp)
+                outputs.append([])
+            else:
+                outputs[-1].append(token)
+        outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs]
+        return "".join(outputs)
+
+    @property
+    @lru_cache()
+    def eot(self) -> int:
+        return self.tokenizer.eos_token_id
+
+    @property
+    @lru_cache()
+    def sot(self) -> int:
+        return self._get_single_token_id("<|startoftranscript|>")
+
+    @property
+    @lru_cache()
+    def sot_lm(self) -> int:
+        return self._get_single_token_id("<|startoflm|>")
+
+    @property
+    @lru_cache()
+    def sot_prev(self) -> int:
+        return self._get_single_token_id("<|startofprev|>")
+
+    @property
+    @lru_cache()
+    def no_speech(self) -> int:
+        return self._get_single_token_id("<|nospeech|>")
+
+    @property
+    @lru_cache()
+    def no_timestamps(self) -> int:
+        return self._get_single_token_id("<|notimestamps|>")
+
+    @property
+    @lru_cache()
+    def timestamp_begin(self) -> int:
+        return self.tokenizer.all_special_ids[-1] + 1
+
+    @property
+    @lru_cache()
+    def language_token(self) -> int:
+        """Returns the token id corresponding to the value of the `language` field"""
+        if self.language is None:
+            raise ValueError(f"This tokenizer does not have language token configured")
+
+        additional_tokens = dict(
+            zip(
+                self.tokenizer.additional_special_tokens,
+                self.tokenizer.additional_special_tokens_ids,
+            )
+        )
+        candidate = f"<|{self.language}|>"
+        if candidate in additional_tokens:
+            return additional_tokens[candidate]
+
+        raise KeyError(f"Language {self.language} not found in tokenizer.")
+
+    @property
+    @lru_cache()
+    def all_language_tokens(self) -> Tuple[int]:
+        result = []
+        for token, token_id in zip(
+            self.tokenizer.additional_special_tokens,
+            self.tokenizer.additional_special_tokens_ids,
+        ):
+            if token.strip("<|>") in LANGUAGES:
+                result.append(token_id)
+        return tuple(result)
+
+    @property
+    @lru_cache()
+    def all_language_codes(self) -> Tuple[str]:
+        return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens)
+
+    @property
+    @lru_cache()
+    def sot_sequence_including_notimestamps(self) -> Tuple[int]:
+        return tuple(list(self.sot_sequence) + [self.no_timestamps])
+
+    @property
+    @lru_cache()
+    def non_speech_tokens(self) -> Tuple[int]:
+        """
+        Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
+        annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
+
+        - ♪♪♪
+        - ( SPEAKING FOREIGN LANGUAGE )
+        - [DAVID] Hey there,
+
+        keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
+        """
+        symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』")
+        symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
+
+        # symbols that may be a single token or multiple tokens depending on the tokenizer.
+        # In case they're multiple tokens, suppress the first token, which is safe because:
+        # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
+        # in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
+        miscellaneous = set("♩♪♫♬♭♮♯")
+        assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
+
+        # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
+        result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]}
+        for symbol in symbols + list(miscellaneous):
+            for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]:
+                if len(tokens) == 1 or symbol in miscellaneous:
+                    result.add(tokens[0])
+
+        return tuple(sorted(result))
+
+    def _get_single_token_id(self, text) -> int:
+        tokens = self.tokenizer.encode(text)
+        assert len(tokens) == 1, f"{text} is not encoded as a single token"
+        return tokens[0]
+
+
+@lru_cache(maxsize=None)
+def build_tokenizer(name: str = "gpt2"):
+    os.environ["TOKENIZERS_PARALLELISM"] = "false"
+    path = os.path.join(os.path.dirname(__file__), "assets", name)
+    tokenizer = GPT2TokenizerFast.from_pretrained(path)
+
+    specials = [
+        "<|startoftranscript|>",
+        *[f"<|{lang}|>" for lang in LANGUAGES.keys()],
+        "<|translate|>",
+        "<|transcribe|>",
+        "<|startoflm|>",
+        "<|startofprev|>",
+        "<|nospeech|>",
+        "<|notimestamps|>",
+    ]
+
+    tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
+    return tokenizer
+
+
+@lru_cache(maxsize=None)
+def get_tokenizer(
+    multilingual: bool,
+    *,
+    task: Optional[str] = None,  # Literal["transcribe", "translate", None]
+    language: Optional[str] = None,
+) -> Tokenizer:
+    if language is not None:
+        language = language.lower()
+        if language not in LANGUAGES:
+            if language in TO_LANGUAGE_CODE:
+                language = TO_LANGUAGE_CODE[language]
+            else:
+                raise ValueError(f"Unsupported language: {language}")
+
+    if multilingual:
+        tokenizer_name = "multilingual"
+        task = task or "transcribe"
+        language = language or "en"
+    else:
+        tokenizer_name = "gpt2"
+        task = None
+        language = None
+
+    tokenizer = build_tokenizer(name=tokenizer_name)
+    all_special_ids: List[int] = tokenizer.all_special_ids
+    sot: int = all_special_ids[1]
+    translate: int = all_special_ids[-6]
+    transcribe: int = all_special_ids[-5]
+
+    langs = tuple(LANGUAGES.keys())
+    sot_sequence = [sot]
+    if language is not None:
+        sot_sequence.append(sot + 1 + langs.index(language))
+    if task is not None:
+        sot_sequence.append(transcribe if task == "transcribe" else translate)
+
+    return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence))
diff --git a/utils/digital_human/musetalk/whisper/whisper/transcribe.py b/utils/digital_human/musetalk/whisper/whisper/transcribe.py
new file mode 100644
index 0000000000000000000000000000000000000000..745e77554b90c63db69ce7da40156676c7a990de
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/transcribe.py
@@ -0,0 +1,207 @@
+import argparse
+import os
+import warnings
+from typing import List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+import torch
+import tqdm
+
+from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram
+from .decoding import DecodingOptions, DecodingResult
+from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
+from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, write_txt, write_vtt, write_srt
+
+if TYPE_CHECKING:
+    from .model import Whisper
+
+
+def transcribe(
+        model: "Whisper",
+        audio: Union[str, np.ndarray, torch.Tensor],
+        *,
+        verbose: Optional[bool] = None,
+        temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
+        compression_ratio_threshold: Optional[float] = 2.4,
+        logprob_threshold: Optional[float] = -1.0,
+        no_speech_threshold: Optional[float] = 0.6,
+        condition_on_previous_text: bool = True,
+        force_extraction: bool = False,
+        **decode_options,
+):
+    """
+    Transcribe an audio file using Whisper
+
+    Parameters
+    ----------
+    model: Whisper
+        The Whisper model instance
+
+    audio: Union[str, np.ndarray, torch.Tensor]
+        The path to the audio file to open, or the audio waveform
+
+    verbose: bool
+        Whether to display the text being decoded to the console. If True, displays all the details,
+        If False, displays minimal details. If None, does not display anything
+
+    temperature: Union[float, Tuple[float, ...]]
+        Temperature for sampling. It can be a tuple of temperatures, which will be successfully used
+        upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.
+
+    compression_ratio_threshold: float
+        If the gzip compression ratio is above this value, treat as failed
+
+    logprob_threshold: float
+        If the average log probability over sampled tokens is below this value, treat as failed
+
+    no_speech_threshold: float
+        If the no_speech probability is higher than this value AND the average log probability
+        over sampled tokens is below `logprob_threshold`, consider the segment as silent
+
+    condition_on_previous_text: bool
+        if True, the previous output of the model is provided as a prompt for the next window;
+        disabling may make the text inconsistent across windows, but the model becomes less prone to
+        getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
+
+    decode_options: dict
+        Keyword arguments to construct `DecodingOptions` instances
+
+    Returns
+    -------
+    A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
+    the spoken language ("language"), which is detected when `decode_options["language"]` is None.
+    """
+    dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32
+    if model.device == torch.device("cpu"):
+        if torch.cuda.is_available():
+            warnings.warn("Performing inference on CPU when CUDA is available")
+        if dtype == torch.float16:
+            warnings.warn("FP16 is not supported on CPU; using FP32 instead")
+            dtype = torch.float32
+
+    if dtype == torch.float32:
+        decode_options["fp16"] = False
+
+    mel = log_mel_spectrogram(audio)
+   
+    all_segments = []
+    def add_segment(
+            *, start: float, end: float, encoder_embeddings
+    ):
+      
+        all_segments.append(
+            {
+                "start": start,
+                "end": end,
+                "encoder_embeddings":encoder_embeddings,
+            }
+        )
+    # show the progress bar when verbose is False (otherwise the transcribed text will be printed)
+    num_frames = mel.shape[-1]
+    seek = 0
+    previous_seek_value = seek
+    sample_skip = 3000 # 
+    with tqdm.tqdm(total=num_frames, unit='frames', disable=verbose is not False) as pbar:
+        while seek < num_frames:
+            # seek是开始的帧数
+            end_seek = min(seek + sample_skip, num_frames)
+            segment = pad_or_trim(mel[:,seek:seek+sample_skip], N_FRAMES).to(model.device).to(dtype)
+            
+            single = segment.ndim == 2
+            if single:
+                segment = segment.unsqueeze(0)
+            if dtype == torch.float16:
+                segment = segment.half()
+            audio_features, embeddings  = model.encoder(segment, include_embeddings = True)
+            
+            encoder_embeddings = embeddings
+            #print(f"encoder_embeddings shape {encoder_embeddings.shape}")
+            add_segment(
+                start=seek,
+                end=end_seek,
+                #text_tokens=tokens,
+                #result=result,
+                encoder_embeddings=encoder_embeddings,
+            )
+            seek+=sample_skip
+    
+    return dict(segments=all_segments)
+
+
+def cli():
+    from . import available_models
+
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
+    parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use")
+    parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
+    parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
+    parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
+    parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")
+
+    parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
+    parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection")
+
+    parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
+    parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")
+    parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero")
+    parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search")
+    parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default")
+
+    parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
+    parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
+    parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
+    parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
+
+    parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
+    parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
+    parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
+    parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
+    parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
+
+    args = parser.parse_args().__dict__
+    model_name: str = args.pop("model")
+    model_dir: str = args.pop("model_dir")
+    output_dir: str = args.pop("output_dir")
+    device: str = args.pop("device")
+    os.makedirs(output_dir, exist_ok=True)
+
+    if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
+        if args["language"] is not None:
+            warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.")
+        args["language"] = "en"
+
+    temperature = args.pop("temperature")
+    temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback")
+    if temperature_increment_on_fallback is not None:
+        temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
+    else:
+        temperature = [temperature]
+
+    threads = args.pop("threads")
+    if threads > 0:
+        torch.set_num_threads(threads)
+
+    from . import load_model
+    model = load_model(model_name, device=device, download_root=model_dir)
+
+    for audio_path in args.pop("audio"):
+        result = transcribe(model, audio_path, temperature=temperature, **args)
+
+        audio_basename = os.path.basename(audio_path)
+
+        # save TXT
+        with open(os.path.join(output_dir, audio_basename + ".txt"), "w", encoding="utf-8") as txt:
+            write_txt(result["segments"], file=txt)
+
+        # save VTT
+        with open(os.path.join(output_dir, audio_basename + ".vtt"), "w", encoding="utf-8") as vtt:
+            write_vtt(result["segments"], file=vtt)
+
+        # save SRT
+        with open(os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8") as srt:
+            write_srt(result["segments"], file=srt)
+
+
+if __name__ == '__main__':
+    cli()
diff --git a/utils/digital_human/musetalk/whisper/whisper/utils.py b/utils/digital_human/musetalk/whisper/whisper/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..87c91f2e969c4d2ebfc311266bced76a52cb3ef3
--- /dev/null
+++ b/utils/digital_human/musetalk/whisper/whisper/utils.py
@@ -0,0 +1,87 @@
+import zlib
+from typing import Iterator, TextIO
+
+
+def exact_div(x, y):
+    assert x % y == 0
+    return x // y
+
+
+def str2bool(string):
+    str2val = {"True": True, "False": False}
+    if string in str2val:
+        return str2val[string]
+    else:
+        raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
+
+
+def optional_int(string):
+    return None if string == "None" else int(string)
+
+
+def optional_float(string):
+    return None if string == "None" else float(string)
+
+
+def compression_ratio(text) -> float:
+    return len(text) / len(zlib.compress(text.encode("utf-8")))
+
+
+def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'):
+    assert seconds >= 0, "non-negative timestamp expected"
+    milliseconds = round(seconds * 1000.0)
+
+    hours = milliseconds // 3_600_000
+    milliseconds -= hours * 3_600_000
+
+    minutes = milliseconds // 60_000
+    milliseconds -= minutes * 60_000
+
+    seconds = milliseconds // 1_000
+    milliseconds -= seconds * 1_000
+
+    hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
+    return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
+
+
+def write_txt(transcript: Iterator[dict], file: TextIO):
+    for segment in transcript:
+        print(segment['text'].strip(), file=file, flush=True)
+
+
+def write_vtt(transcript: Iterator[dict], file: TextIO):
+    print("WEBVTT\n", file=file)
+    for segment in transcript:
+        print(
+            f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
+            f"{segment['text'].strip().replace('-->', '->')}\n",
+            file=file,
+            flush=True,
+        )
+
+
+def write_srt(transcript: Iterator[dict], file: TextIO):
+    """
+    Write a transcript to a file in SRT format.
+
+    Example usage:
+        from pathlib import Path
+        from whisper.utils import write_srt
+
+        result = transcribe(model, audio_path, temperature=temperature, **args)
+
+        # save SRT
+        audio_basename = Path(audio_path).stem
+        with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt:
+            write_srt(result["segments"], file=srt)
+    """
+    for i, segment in enumerate(transcript, start=1):
+        # write srt lines
+        print(
+            f"{i}\n"
+            f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> "
+            f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n"
+            f"{segment['text'].strip().replace('-->', '->')}\n",
+            file=file,
+            flush=True,
+        )
diff --git a/utils/digital_human/realtime_inference.py b/utils/digital_human/realtime_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea9932ce97e38f2d106b32f00aab5df9bb7e6b55
--- /dev/null
+++ b/utils/digital_human/realtime_inference.py
@@ -0,0 +1,481 @@
+import copy
+from datetime import datetime
+import glob
+import json
+import os
+import pickle
+import queue
+import shutil
+import threading
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from typing import List, Optional
+
+import cv2
+import numpy as np
+import streamlit as st
+import torch
+import wget
+from tqdm import tqdm
+
+from utils.digital_human.musetalk.models.unet import PositionalEncoding, UNet
+from utils.digital_human.musetalk.models.vae import VAE
+from utils.digital_human.musetalk.utils.blending import get_image_blending, get_image_prepare_material, init_face_parsing_model
+from utils.digital_human.musetalk.utils.face_parsing import FaceParsing
+from utils.digital_human.musetalk.utils.preprocessing import get_landmark_and_bbox, read_imgs
+from utils.digital_human.musetalk.utils.utils import datagen, load_all_model
+from utils.digital_human.musetalk.whisper.audio2feature import Audio2Feature
+
+
+def setup_ffmpeg_env(model_dir):
+    # wget https://johnvansickle.com/ffmpeg/releases/ffmpeg-release-amd64-static.tar.xz
+    # xz -d ffmpeg-release-amd64-static.tar.xz
+    # tar -xvf ffmpeg-release-amd64-static.tar
+
+    ffmpeg_file_name = "ffmpeg-release-amd64-static"
+    ffmpeg_root = Path(model_dir).joinpath(f"drivers").absolute()
+    Path(ffmpeg_root).mkdir(exist_ok=True, parents=True)
+
+    ffmpeg_dir = None
+    for ffmpeg_dir_path in Path(ffmpeg_root).iterdir():
+        if not ffmpeg_dir_path.is_dir():
+            continue
+        ffmpeg_dir = str(ffmpeg_dir_path)
+
+    if ffmpeg_dir is None:
+        os.system(
+            f"cd {str(ffmpeg_root)} && wget https://johnvansickle.com/ffmpeg/releases/{ffmpeg_file_name}.tar.xz && xz -d {ffmpeg_file_name}.tar.xz && tar -xvf {ffmpeg_file_name}.tar"
+        )
+
+    for ffmpeg_dir_path in Path(ffmpeg_root).iterdir():
+        if not ffmpeg_dir_path.is_dir():
+            continue
+        ffmpeg_dir = str(ffmpeg_dir_path)
+        break
+    print(f"setting ffmpeg dir: {ffmpeg_dir}")
+    if str(ffmpeg_dir) not in os.getenv("PATH"):
+        print(f"add ffmpeg to path : {str(ffmpeg_dir)}")
+        os.environ["PATH"] = f"{str(ffmpeg_dir)}:{os.environ['PATH']}"
+
+
+def init_digital_model(model_dir, use_float16=False):
+
+    os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
+    from huggingface_hub import snapshot_download
+
+    # 直接下载单个文件
+    muse_talk_model_path = snapshot_download(repo_id="TMElyralab/MuseTalk", local_dir=model_dir)
+    sd_model_path = snapshot_download(repo_id="stabilityai/sd-vae-ft-mse", local_dir=Path(model_dir).joinpath("sd-vae-ft-mse"))
+
+    whisper_pth_path = Path(model_dir).joinpath(r"whisper/tiny.pt")
+    whisper_pth_path.parent.mkdir(parents=True, exist_ok=True)
+    if not whisper_pth_path.exists():
+
+        wget.download(
+            url="https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
+            out=str(whisper_pth_path),
+        )
+
+    # load model weights
+    print("Loading models...")
+    audio_processor, vae, unet, pe = load_all_model(
+        audio2feature_model_path=str(whisper_pth_path),
+        vae_model_path=sd_model_path,
+        unet_model_dict={
+            "unet_config": str(Path(muse_talk_model_path).joinpath("musetalk", "musetalk.json")),
+            "model_path": str(Path(muse_talk_model_path).joinpath("musetalk", "pytorch_model.bin")),
+        },
+    )
+
+    if use_float16 is True:
+        pe = pe.half()
+        vae.vae = vae.vae.half()
+        unet.model = unet.model.half()
+    print("Loaded models done !...")
+    return audio_processor, vae, unet, pe
+
+
+def load_pose_model(model_dir):
+
+    from mmpose.apis import init_model
+
+    os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
+    from huggingface_hub import hf_hub_download
+
+    # 直接下载单个文件
+    dw_pose_path = hf_hub_download(
+        repo_id="yzd-v/DWPose",
+        filename="dw-ll_ucoco_384.pth",
+        local_dir=Path(model_dir).joinpath("dwpose"),
+    )
+
+    config_file = r"./utils/digital_human/musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py"
+    pose_model = init_model(config_file, dw_pose_path, device="cuda")
+
+    return pose_model
+
+
+def load_face_parsing_model(model_dir):
+
+    os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
+    from huggingface_hub import hf_hub_download
+
+    model_dir = Path(model_dir).joinpath("face-parse-bisent")
+    model_dir.mkdir(parents=True, exist_ok=True)
+
+    resnet_path = Path(model_dir).joinpath("resnet18-5c106cde.pth")
+    if not resnet_path.exists():
+
+        wget.download(
+            url="https://download.pytorch.org/models/resnet18-5c106cde.pth",
+            out=str(resnet_path),
+        )
+
+    # 79999_iter.pth 地址: https://drive.google.com/open?id=154JgKpzCPW82qINcVieuPH3fZ2e0P812
+    # 非官方
+    _ = hf_hub_download(
+        repo_id="ManyOtherFunctions/face-parse-bisent",
+        filename="79999_iter.pth",
+        local_dir=str(model_dir),
+    )
+
+    face_parsing_model = init_face_parsing_model(
+        resnet_path=str(resnet_path),
+        face_model_pth=Path(model_dir).joinpath("79999_iter.pth"),
+    )
+    return face_parsing_model
+
+
+def video2imgs(vid_path, save_path, ext=".png", cut_frame=10000000):
+    cap = cv2.VideoCapture(vid_path)
+    count = 0
+    while True:
+        if count > cut_frame:
+            break
+        ret, frame = cap.read()
+        if ret:
+            cv2.imwrite(f"{save_path}/{count:08d}.png", frame)
+            count += 1
+        else:
+            break
+
+
+def osmakedirs(path_list):
+    for path in path_list:
+        os.makedirs(path) if not os.path.exists(path) else None
+
+
+@dataclass
+class HandlerDigitalHuman:
+    audio_processor: Optional[Audio2Feature] = None
+    vae: Optional[VAE] = None
+    unet: Optional[UNet] = None
+    pe: Optional[PositionalEncoding] = None
+    face_parsing_model: Optional[FaceParsing] = None
+    frame_list_cycle: Optional[List] = None
+    coord_list_cycle: Optional[List] = None
+    input_latent_list_cycle: Optional[List] = None
+    mask_coords_list_cycle: Optional[List] = None
+    mask_list_cycle: Optional[List] = None
+    fps: int = 25
+    bbox_shift: int = 0
+    use_float16: bool = False
+
+
+@torch.no_grad()
+class Avatar:
+    def __init__(self, avatar_id, work_dir, model_dir, video_path, bbox_shift, batch_size, fps, preparation_force):
+        self.avatar_id = avatar_id
+        self.video_path = video_path
+        self.bbox_shift = bbox_shift
+        self.avatar_path = work_dir
+        self.model_dir = model_dir
+        self.full_imgs_path = f"{self.avatar_path}/full_imgs"
+        self.coords_path = f"{self.avatar_path}/coords.pkl"
+        self.latents_out_path = f"{self.avatar_path}/latents.pt"
+        self.video_out_path = f"{self.avatar_path}/vid_output/"
+        self.mask_out_path = f"{self.avatar_path}/mask"
+        self.mask_coords_path = f"{self.avatar_path}/mask_coords.pkl"
+        self.avatar_info_path = f"{self.avatar_path}/avator_info.json"
+        self.avatar_info = {"avatar_id": avatar_id, "video_path": video_path, "bbox_shift": bbox_shift}
+        self.preparation_force = preparation_force
+        self.batch_size = batch_size
+        self.idx = 0
+
+        # 模型初始化，防止 pose 导致 OOM，放到最后加载
+        face_parsing_model = load_face_parsing_model(self.model_dir)
+        audio_processor, vae, unet, pe = init_digital_model(self.model_dir, use_float16=False)
+        pe = pe.half()
+        vae.vae = vae.vae.half()
+        unet.model = unet.model.half()
+
+        self.init(vae_model=vae, face_parsing_model=face_parsing_model)
+
+        self.model_handler = HandlerDigitalHuman(
+            audio_processor=audio_processor,
+            vae=vae,
+            unet=unet,
+            pe=pe,
+            face_parsing_model=face_parsing_model,
+            frame_list_cycle=self.frame_list_cycle,
+            coord_list_cycle=self.coord_list_cycle,
+            input_latent_list_cycle=self.input_latent_list_cycle,
+            mask_coords_list_cycle=self.mask_coords_list_cycle,
+            mask_list_cycle=self.mask_list_cycle,
+            fps=fps,
+            bbox_shift=bbox_shift,
+        )
+
+    def init(self, vae_model, face_parsing_model):
+        need_to_prepare = False
+
+        if self.preparation_force and os.path.exists(self.avatar_path):
+            shutil.rmtree(self.avatar_path)
+            need_to_prepare = True
+        elif not os.path.exists(self.avatar_path):
+            # 预处理文件不存在，需要进行预处理
+            need_to_prepare = True
+        elif os.path.exists(self.avatar_path):
+            # 预处理文件存在，判断 bbox_shift 是否匹配，不匹配需要重新进行预处理
+            with open(self.avatar_info_path, "r") as f:
+                avatar_info = json.load(f)
+            if avatar_info["bbox_shift"] != self.avatar_info["bbox_shift"]:
+                need_to_prepare = True
+                shutil.rmtree(self.avatar_path)
+
+        if need_to_prepare is False:
+            # 对文件再进行一个判断，避免中途出错导致文件没生成全
+            for prepare_file in [
+                self.full_imgs_path,
+                self.coords_path,
+                self.latents_out_path,
+                self.video_out_path,
+                self.mask_out_path,
+                self.mask_coords_path,
+                self.avatar_info_path,
+            ]:
+                if not os.path.exists(prepare_file):
+                    # 如有文件不存在，则需要重新生成
+                    print(f"Missing file {prepare_file}, will process prerpare...")
+                    need_to_prepare = True
+                    shutil.rmtree(self.avatar_path)
+                    break
+
+        if need_to_prepare:
+            print("*********************************")
+            print(f"  creating avator: {self.avatar_id}")
+            print("*********************************")
+            osmakedirs([self.avatar_path, self.full_imgs_path, self.video_out_path, self.mask_out_path])
+            self.prepare_material(vae_model=vae_model, face_parsing_model=face_parsing_model)
+
+        self.input_latent_list_cycle = torch.load(self.latents_out_path)
+        with open(self.coords_path, "rb") as f:
+            self.coord_list_cycle = pickle.load(f)
+        input_img_list = glob.glob(os.path.join(self.full_imgs_path, "*.[jpJP][pnPN]*[gG]"))
+        input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
+        self.frame_list_cycle = read_imgs(input_img_list)
+        with open(self.mask_coords_path, "rb") as f:
+            self.mask_coords_list_cycle = pickle.load(f)
+        input_mask_list = glob.glob(os.path.join(self.mask_out_path, "*.[jpJP][pnPN]*[gG]"))
+        input_mask_list = sorted(input_mask_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
+        self.mask_list_cycle = read_imgs(input_mask_list)
+
+    def prepare_material(self, vae_model, face_parsing_model):
+        print("preparing data materials ... ...")
+        with open(self.avatar_info_path, "w") as f:
+            json.dump(self.avatar_info, f)
+
+        if os.path.isfile(self.video_path):
+            video2imgs(self.video_path, self.full_imgs_path, ext="png")
+        else:
+            print(f"copy files in {self.video_path}")
+            files = os.listdir(self.video_path)
+            files.sort()
+            files = [file for file in files if file.split(".")[-1] == "png"]
+            for filename in files:
+                shutil.copyfile(f"{self.video_path}/{filename}", f"{self.full_imgs_path}/{filename}")
+        input_img_list = sorted(glob.glob(os.path.join(self.full_imgs_path, "*.[jpJP][pnPN]*[gG]")))
+
+        print("extracting landmarks...")
+        pose_model = load_pose_model(self.model_dir)
+        coord_list, frame_list = get_landmark_and_bbox(input_img_list, pose_model, self.bbox_shift)
+        del pose_model
+
+        input_latent_list = []
+        idx = -1
+        # maker if the bbox is not sufficient
+        coord_placeholder = (0.0, 0.0, 0.0, 0.0)
+        for bbox, frame in zip(coord_list, frame_list):
+            idx = idx + 1
+            if bbox == coord_placeholder:
+                continue
+            x1, y1, x2, y2 = bbox
+            crop_frame = frame[y1:y2, x1:x2]
+            resized_crop_frame = cv2.resize(crop_frame, (256, 256), interpolation=cv2.INTER_LANCZOS4)
+            latents = vae_model.get_latents_for_unet(resized_crop_frame)
+            input_latent_list.append(latents)
+
+        self.frame_list_cycle = frame_list + frame_list[::-1]
+        self.coord_list_cycle = coord_list + coord_list[::-1]
+        self.input_latent_list_cycle = input_latent_list + input_latent_list[::-1]
+        self.mask_coords_list_cycle = []
+        self.mask_list_cycle = []
+
+        for i, frame in enumerate(tqdm(self.frame_list_cycle)):
+            cv2.imwrite(f"{self.full_imgs_path}/{str(i).zfill(8)}.png", frame)
+
+            face_box = self.coord_list_cycle[i]
+            mask, crop_box = get_image_prepare_material(frame, face_box, face_parsing_model)
+            cv2.imwrite(f"{self.mask_out_path}/{str(i).zfill(8)}.png", mask)
+            self.mask_coords_list_cycle += [crop_box]
+            self.mask_list_cycle.append(mask)
+
+        with open(self.mask_coords_path, "wb") as f:
+            pickle.dump(self.mask_coords_list_cycle, f)
+
+        with open(self.coords_path, "wb") as f:
+            pickle.dump(self.coord_list_cycle, f)
+
+        torch.save(self.input_latent_list_cycle, os.path.join(self.latents_out_path))
+
+    def process_frames(self, res_frame_queue, video_len, skip_save_images, save_dir_name):
+        print(video_len)
+        while True:
+            if self.idx >= video_len - 1:
+                break
+            try:
+                res_frame = res_frame_queue.get(block=True, timeout=1)
+            except queue.Empty:
+                continue
+
+            bbox = self.coord_list_cycle[self.idx % (len(self.coord_list_cycle))]
+            ori_frame = copy.deepcopy(self.frame_list_cycle[self.idx % (len(self.frame_list_cycle))])
+            x1, y1, x2, y2 = bbox
+            try:
+                res_frame = cv2.resize(res_frame.astype(np.uint8), (x2 - x1, y2 - y1))
+            except:
+                continue
+            mask = self.mask_list_cycle[self.idx % (len(self.mask_list_cycle))]
+            mask_crop_box = self.mask_coords_list_cycle[self.idx % (len(self.mask_coords_list_cycle))]
+            # combine_frame = get_image(ori_frame,res_frame,bbox)
+            combine_frame = get_image_blending(ori_frame, res_frame, bbox, mask, mask_crop_box)
+
+            if skip_save_images is False:
+                cv2.imwrite(f"{self.avatar_path}/{save_dir_name}/{str(self.idx).zfill(8)}.png", combine_frame)
+            self.idx = self.idx + 1
+
+    def inference(self, audio_path, output_vid, fps, skip_save_images=False):
+
+        tmp_tag = "tmp_" + datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
+
+        os.makedirs(self.avatar_path + f"/{tmp_tag}", exist_ok=True)
+        print("start inference")
+        ############################################## extract audio feature ##############################################
+        start_time = time.time()
+        whisper_feature = self.model_handler.audio_processor.audio2feat(audio_path)
+        whisper_chunks = self.model_handler.audio_processor.feature2chunks(feature_array=whisper_feature, fps=fps)
+        print(f"processing audio:{audio_path} costs {(time.time() - start_time) * 1000}ms")
+        ############################################## inference batch by batch ##############################################
+        video_num = len(whisper_chunks)
+        res_frame_queue = queue.Queue()
+        self.idx = 0
+        # # Create a sub-thread and start it
+        process_thread = threading.Thread(
+            target=self.process_frames, args=(res_frame_queue, video_num, skip_save_images, tmp_tag)
+        )
+        process_thread.start()
+
+        gen = datagen(whisper_chunks, self.input_latent_list_cycle, self.batch_size)
+        start_time = time.time()
+
+        for i, (whisper_batch, latent_batch) in enumerate(tqdm(gen, total=int(np.ceil(float(video_num) / self.batch_size)))):
+            audio_feature_batch = torch.from_numpy(whisper_batch)
+            audio_feature_batch = audio_feature_batch.to(
+                device=self.model_handler.unet.device, dtype=self.model_handler.unet.model.dtype
+            )
+            audio_feature_batch = self.model_handler.pe(audio_feature_batch)
+            latent_batch = latent_batch.to(dtype=self.model_handler.unet.model.dtype)
+
+            timesteps = torch.tensor([0], device="cuda")
+            pred_latents = self.model_handler.unet.model(
+                latent_batch, timesteps, encoder_hidden_states=audio_feature_batch
+            ).sample
+            recon = self.model_handler.vae.decode_latents(pred_latents)
+            for res_frame in recon:
+                res_frame_queue.put(res_frame)
+        # Close the queue and sub-thread after all tasks are completed
+        process_thread.join()
+
+        print("Total process time of {} frames including saving images = {}s".format(video_num, time.time() - start_time))
+
+        cmd_img2video = f"ffmpeg -y -v warning -r {fps} -f image2 -i {self.avatar_path}/{tmp_tag}/%08d.png -vcodec libx264 -vf format=rgb24,scale=out_color_matrix=bt709,format=yuv420p -crf 18 {self.avatar_path}/{tmp_tag}.mp4"
+        print(cmd_img2video)
+        os.system(cmd_img2video)
+
+        # output_vid = os.path.join(self.video_out_path, out_vid_name + ".mp4")  # on
+        cmd_combine_audio = f"ffmpeg -y -v warning -i {audio_path} -i {self.avatar_path}/{tmp_tag}.mp4 {output_vid}"
+        print(cmd_combine_audio)
+        os.system(cmd_combine_audio)
+
+        os.remove(f"{self.avatar_path}/{tmp_tag}.mp4")
+        shutil.rmtree(f"{self.avatar_path}/{tmp_tag}")
+        print(f"result is save to {output_vid}")
+
+        return str(output_vid)
+
+
+@st.cache_resource
+def digital_human_preprocess(model_dir, use_float16, video_path, work_dir, fps, bbox_shift):
+
+    avatar = Avatar(
+        avatar_id="lelemiao",
+        work_dir=work_dir,
+        model_dir=model_dir,
+        video_path=video_path,
+        bbox_shift=bbox_shift,
+        batch_size=8,
+        fps=fps,
+        preparation_force=False,
+    )
+
+    setup_ffmpeg_env(model_dir)
+
+    return avatar
+
+
+@torch.no_grad()
+def gen_digital_human_video(
+    avatar_handler: Avatar,
+    audio_path,
+    work_dir,
+    video_path,
+    fps,
+):
+    output_vid_image_dir = Path(avatar_handler.video_out_path).joinpath(f"{Path(video_path).stem}+{Path(audio_path).stem}")
+    output_vid_file_path = output_vid_image_dir.with_suffix(".mp4")
+    output_vid = avatar_handler.inference(
+        audio_path=audio_path,  # wav file
+        output_vid=str(output_vid_file_path),
+        fps=fps,
+        skip_save_images=False,
+    )
+
+    return output_vid
+
+
+if __name__ == "__main__":
+
+    data_preparation = False
+    video_path = "./work_dirs/tts_wavs/2024-06-05-20-48-53.wav"
+    bbox_shift = 5
+    avatar = Avatar(
+        avatar_id="lelemiao", video_path=video_path, bbox_shift=bbox_shift, batch_size=4, preparation=data_preparation
+    )
+
+    avatar.inference(
+        audio_path=r"./work_dirs/tts_wavs/2024-06-05-20-48-53.wav",
+        out_vid_name="avatar_1",
+        fps=25,
+        skip_save_images=False,
+    )
diff --git a/utils/infer/__init__.py b/utils/infer/__init__.py
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diff --git a/utils/infer/lmdeploy_infer.py b/utils/infer/lmdeploy_infer.py
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index 0000000000000000000000000000000000000000..da705c0f272a7629bebaffc541b394f8b3dd777f
--- /dev/null
+++ b/utils/infer/lmdeploy_infer.py
@@ -0,0 +1,234 @@
+import json
+import logging
+
+import streamlit as st
+import torch
+from lagent.actions import ActionExecutor
+from lagent.agents.internlm2_agent import Internlm2Protocol
+from lagent.schema import ActionReturn, AgentReturn
+from lmdeploy import GenerationConfig
+
+from utils.digital_human.digital_human_worker import gen_digital_human_video_in_spinner
+from utils.rag.rag_worker import build_rag_prompt
+from utils.tts.tts_worker import gen_tts_in_spinner
+
+
+
+def prepare_generation_config(skip_special_tokens=True):
+
+    gen_config = GenerationConfig(
+        top_p=0.8,
+        temperature=0.7,
+        repetition_penalty=1.005,
+        skip_special_tokens=skip_special_tokens,
+    )  # top_k=40, min_new_tokens=200
+    return gen_config
+
+
+def combine_history(prompt, meta_instruction, history_msg=None, first_input_str=""):
+    total_prompt = [{"role": "system", "content": meta_instruction}]
+
+    if first_input_str != "":
+        total_prompt.append({"role": "user", "content": first_input_str})
+
+    if history_msg is not None:
+        for message in history_msg:
+            total_prompt.append({"role": message["role"], "content": message["content"]})
+
+    total_prompt.append({"role": "user", "content": prompt})
+    return [total_prompt]
+
+'''
+@st.cache_resource
+# def init_handlers(departure_place, delivery_company_name):
+def init_handlers():
+    # from utils.agent.delivery_time_query import DeliveryTimeQueryAction  # isort:skip
+
+    META_CN = "当开启工具以及代码时，根据需求选择合适的工具进行调用"
+
+    INTERPRETER_CN = (
+        "你现在已经能够在一个有状态的 Jupyter 笔记本环境中运行 Python 代码。"
+        "当你向 python 发送含有 Python 代码的消息时，它将在该环境中执行。"
+        "这个工具适用于多种场景，如数据分析或处理（包括数据操作、统计分析、图表绘制），"
+        "复杂的计算问题（解决数学和物理难题），编程示例（理解编程概念或特性），"
+        "文本处理和分析（比如文本解析和自然语言处理），"
+        "机器学习和数据科学（用于展示模型训练和数据可视化），"
+        "以及文件操作和数据导入（处理CSV、JSON等格式的文件）。"
+    )
+
+    PLUGIN_CN = (
+        "你可以使用如下工具："
+        "\n{prompt}\n"
+        "如果你已经获得足够信息，请直接给出答案. 避免不必要的工具调用! "
+        "同时注意你可以使用的工具，不要随意捏造！"
+    )
+
+    protocol_handler = Internlm2Protocol(
+        meta_prompt=META_CN,
+        interpreter_prompt=INTERPRETER_CN,
+        plugin_prompt=PLUGIN_CN,
+        tool=dict(
+            begin="{start_token}{name}\n",
+            start_token="<|action_start|>",
+            name_map=dict(plugin="<|plugin|>", interpreter="<|interpreter|>"),
+            belong="assistant",
+            end="<|action_end|>\n",
+        ),
+    )
+    
+    action_list = [
+        DeliveryTimeQueryAction(
+            departure_place=departure_place,
+            delivery_company_name=delivery_company_name,
+        ),
+    ]
+    
+    # plugin_map = {action.name: action for action in action_list}
+    # plugin_name = [action.name for action in action_list]
+    # plugin_action = [plugin_map[name] for name in plugin_name]
+    # action_executor = ActionExecutor(actions=plugin_action)
+
+    # return action_executor, protocol_handler
+'''
+
+# def get_agent_result(model_pipe, prompt_input, departure_place, delivery_company_name):
+def get_agent_result(model_pipe, prompt_input):
+
+    # action_executor, protocol_handler = init_handlers(departure_place, delivery_company_name)
+
+    inner_history = [{"role": "user", "content": prompt_input}]  # NOTE TEST ！！！
+    interpreter_executor = None
+    max_turn = 7
+    for _ in range(max_turn):
+
+        prompt = protocol_handler.format(  # 生成 agent prompt
+            inner_step=inner_history,
+            plugin_executor=action_executor,
+            interpreter_executor=interpreter_executor,
+        )
+        cur_response = ""
+
+        agent_return = AgentReturn()
+        for item in model_pipe.stream_infer(prompt, gen_config=prepare_generation_config(skip_special_tokens=False)):
+            if "~" in item.text:
+                item.text = item.text.replace("~", "。").replace("。。", "。")
+
+            cur_response += item.text
+
+            name, language, action = protocol_handler.parse(
+                message=cur_response,
+                plugin_executor=action_executor,
+                interpreter_executor=interpreter_executor,
+            )
+            if name:  # "plugin"
+                if name == "plugin":
+                    if action_executor:
+                        executor = action_executor
+                    else:
+                        logging.info(msg="No plugin is instantiated!")
+                        continue
+                    try:
+                        action = json.loads(action)
+                    except Exception as e:
+                        logging.info(msg=f"Invaild action {e}")
+                        continue
+                elif name == "interpreter":
+                    if interpreter_executor:
+                        executor = interpreter_executor
+                    else:
+                        logging.info(msg="No interpreter is instantiated!")
+                        continue
+                agent_return.response = action
+
+        print(f"Agent response: {cur_response}")
+
+        if name:
+            print(f"Agent action: {action}")
+            action_return: ActionReturn = executor(action["name"], action["parameters"])
+
+            try:
+                return_str = action_return.result[0]["content"]
+                return return_str
+            except Exception as e:
+                return ""
+
+        if not name:
+            agent_return.response = language
+            break
+
+    return ""
+
+
+def get_turbomind_response(
+    prompt,
+    meta_instruction,
+    user_avator,
+    robot_avator,
+    model_pipe,
+    session_messages,
+    add_session_msg=True,
+    first_input_str="",
+    rag_retriever=None,
+    product_name="",
+    enable_agent=True,
+    # departure_place=None,
+    # delivery_company_name=None,
+):
+
+    # ====================== Agent ======================
+    agent_response = ""
+    if enable_agent:
+        GENERATE_AGENT_TEMPLATE = (
+            "这是网上获取到的信息：“{}”\n 客户的问题：“{}” \n 请认真阅读信息并运用你的性格进行解答。"  # RAG prompt 模板
+        )
+        # agent_response = get_agent_result(model_pipe, prompt, departure_place, delivery_company_name)
+        agent_response = get_agent_result(model_pipe, prompt)
+        if agent_response != "":
+            agent_response = GENERATE_AGENT_TEMPLATE.format(agent_response, prompt)
+            print(f"Agent response: {agent_response}")
+    prompt_pro = agent_response
+
+    # ====================== RAG ======================
+    if rag_retriever is not None and prompt_pro == "":
+        # 如果 Agent 没有执行，则使用 RAG 查询数据库
+        prompt_pro = build_rag_prompt(rag_retriever, product_name, prompt)
+
+    # ====================== 加上历史信息 ======================
+    real_prompt = combine_history(
+        prompt_pro if prompt_pro != "" else prompt,
+        meta_instruction,
+        history_msg=session_messages,
+        first_input_str=first_input_str,
+    )  # 是否加上历史对话记录
+
+    print(real_prompt)
+
+    # Add user message to chat history
+    if add_session_msg:
+        session_messages.append({"role": "user", "content": prompt, "avatar": user_avator})
+
+    with st.chat_message("assistant", avatar=robot_avator):
+        message_placeholder = st.empty()
+        cur_response = ""
+        for item in model_pipe.stream_infer(real_prompt, gen_config=prepare_generation_config()):
+
+            if "~" in item.text:
+                item.text = item.text.replace("~", "。").replace("。。", "。")
+
+            cur_response += item.text
+            message_placeholder.markdown(cur_response + "▌")
+        message_placeholder.markdown(cur_response)
+
+        tts_save_path = gen_tts_in_spinner(cur_response)  # 一整句生成
+        gen_digital_human_video_in_spinner(tts_save_path)
+
+        # Add robot response to chat history
+        session_messages.append(
+            {
+                "role": "assistant",
+                "content": cur_response,  # pylint: disable=undefined-loop-variable
+                "avatar": robot_avator,
+                "wav": tts_save_path,
+            }
+        )
+    torch.cuda.empty_cache()
diff --git a/utils/infer/load_infer_model.py b/utils/infer/load_infer_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce4308b6c9f51c258353c33e699fdd5c75a18d8b
--- /dev/null
+++ b/utils/infer/load_infer_model.py
@@ -0,0 +1,29 @@
+from pathlib import Path
+
+import streamlit as st
+from lmdeploy import TurbomindEngineConfig, pipeline
+from modelscope import snapshot_download
+
+from utils.web_configs import WEB_CONFIGS
+
+
+@st.cache_resource
+def load_turbomind_model(model_dir):  # hf awq
+
+    print("load model begin.")
+
+    model_format = "hf"
+    if Path(model_dir).stem.endswith("-4bit"):
+        model_format = "awq"
+
+    model_dir = snapshot_download(model_dir, revision="master", cache_dir=WEB_CONFIGS.LLM_MODEL_DIR)
+
+    backend_config = TurbomindEngineConfig(
+        model_format=model_format, session_len=32768, cache_max_entry_count=WEB_CONFIGS.CACHE_MAX_ENTRY_COUNT
+    )
+    pipe = pipeline(model_dir, backend_config=backend_config, log_level="INFO", model_name="internlm2")
+
+    print("load model end.")
+
+    return pipe
+    
diff --git a/utils/model_loader.py b/utils/model_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e8f6e8ecc991721faa812b8092efa728a55e689
--- /dev/null
+++ b/utils/model_loader.py
@@ -0,0 +1,67 @@
+from .web_configs import WEB_CONFIGS
+
+from .rag.rag_worker import load_rag_model
+from .asr.asr_worker import load_asr_model
+from .digital_human.realtime_inference import digital_human_preprocess
+from .infer.load_infer_model import load_turbomind_model
+from .tts.gpt_sovits.inference_gpt_sovits import get_tts_model
+
+
+# ==================================================================
+#                             数字人 模型
+# ==================================================================
+
+if WEB_CONFIGS.ENABLE_DIGITAL_HUMAN:
+    DIGITAL_HUMAN_HANDLER = digital_human_preprocess(
+        model_dir=WEB_CONFIGS.DIGITAL_HUMAN_MODEL_DIR,
+        use_float16=False,
+        video_path=WEB_CONFIGS.DIGITAL_HUMAN_VIDEO_PATH,
+        work_dir=WEB_CONFIGS.DIGITAL_HUMAN_GEN_PATH,
+        fps=WEB_CONFIGS.DIGITAL_HUMAN_FPS,
+        bbox_shift=WEB_CONFIGS.DIGITAL_HUMAN_BBOX_SHIFT,
+    )
+else:
+    DIGITAL_HUMAN_HANDLER = None
+
+
+# ==================================================================
+#                               RAG 模型
+# ==================================================================
+
+if WEB_CONFIGS.ENABLE_RAG:
+    RAG_RETRIEVER = load_rag_model()
+else:
+    RAG_RETRIEVER = None
+
+
+# ==================================================================
+#                               TTS 模型
+# ==================================================================
+
+if WEB_CONFIGS.ENABLE_TTS:
+    # samber
+    # from utils.tts.sambert_hifigan.tts_sambert_hifigan import get_tts_model
+    # TTS_HANDLER = get_tts_model()
+
+    # gpt_sovits
+    TTS_HANDLER = get_tts_model()
+else:
+    TTS_HANDLER = None
+
+
+# ==================================================================
+#                               ASR 模型
+# ==================================================================
+
+if WEB_CONFIGS.ENABLE_ASR:
+    ASR_HANDLER = load_asr_model()
+else:
+    ASR_HANDLER = None
+
+
+# ==================================================================
+#                               LLM 模型
+# ==================================================================
+
+# 加载 LLM 模型
+LLM_MODEL = load_turbomind_model(WEB_CONFIGS.LLM_MODEL_NAME)
diff --git a/utils/rag/__init__.py b/utils/rag/__init__.py
new file mode 100644
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diff --git a/utils/rag/__pycache__/retriever.cpython-310.pyc b/utils/rag/__pycache__/retriever.cpython-310.pyc
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diff --git a/utils/rag/feature_store.py b/utils/rag/feature_store.py
new file mode 100644
index 0000000000000000000000000000000000000000..5549b8e7ec161ce04cca7f382b71faf49662f914
--- /dev/null
+++ b/utils/rag/feature_store.py
@@ -0,0 +1,549 @@
+"""extract feature and search with user query."""
+
+import argparse
+import json
+import os
+import re
+import shutil
+from multiprocessing import Pool
+from pathlib import Path
+from typing import Any, List, Optional
+
+import yaml
+
+# 解决 Warning：huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks…
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+from BCEmbedding.tools.langchain import BCERerank
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import MarkdownHeaderTextSplitter, MarkdownTextSplitter, RecursiveCharacterTextSplitter
+from langchain.vectorstores.faiss import FAISS as Vectorstore
+from langchain_core.documents import Document
+from loguru import logger
+from torch.cuda import empty_cache
+
+try:
+    from utils.rag.file_operation import FileName, FileOperation
+    from utils.rag.retriever import CacheRetriever, Retriever
+except:
+    # 用于 DEBUG
+    from file_operation import FileName, FileOperation
+    from retriever import CacheRetriever, Retriever
+
+
+def read_and_save(file: FileName):
+    if os.path.exists(file.copypath):
+        # already exists, return
+        logger.info("already exist, skip load")
+        return
+    file_opr = FileOperation()
+    logger.info("reading {}, would save to {}".format(file.origin, file.copypath))
+    content, error = file_opr.read(file.origin)
+    if error is not None:
+        logger.error("{} load error: {}".format(file.origin, str(error)))
+        return
+
+    if content is None or len(content) < 1:
+        logger.warning("{} empty, skip save".format(file.origin))
+        return
+
+    with open(file.copypath, "w") as f:
+        f.write(content)
+
+
+def _split_text_with_regex_from_end(text: str, separator: str, keep_separator: bool) -> List[str]:
+    # Now that we have the separator, split the text
+    if separator:
+        if keep_separator:
+            # The parentheses in the pattern keep the delimiters in the result.
+            _splits = re.split(f"({separator})", text)
+            splits = ["".join(i) for i in zip(_splits[0::2], _splits[1::2])]
+            if len(_splits) % 2 == 1:
+                splits += _splits[-1:]
+            # splits = [_splits[0]] + splits
+        else:
+            splits = re.split(separator, text)
+    else:
+        splits = list(text)
+    return [s for s in splits if s != ""]
+
+
+# copy from https://github.com/chatchat-space/Langchain-Chatchat/blob/master/text_splitter/chinese_recursive_text_splitter.py
+class ChineseRecursiveTextSplitter(RecursiveCharacterTextSplitter):
+
+    def __init__(
+        self,
+        separators: Optional[List[str]] = None,
+        keep_separator: bool = True,
+        is_separator_regex: bool = True,
+        **kwargs: Any,
+    ) -> None:
+        """Create a new TextSplitter."""
+        super().__init__(keep_separator=keep_separator, **kwargs)
+        self._separators = separators or ["\n\n", "\n", "。|！|？", "\.\s|\!\s|\?\s", "；|;\s", "，|,\s"]
+        self._is_separator_regex = is_separator_regex
+
+    def _split_text(self, text: str, separators: List[str]) -> List[str]:
+        """Split incoming text and return chunks."""
+        final_chunks = []
+        # Get appropriate separator to use
+        separator = separators[-1]
+        new_separators = []
+        for i, _s in enumerate(separators):
+            _separator = _s if self._is_separator_regex else re.escape(_s)
+            if _s == "":
+                separator = _s
+                break
+            if re.search(_separator, text):
+                separator = _s
+                new_separators = separators[i + 1 :]
+                break
+
+        _separator = separator if self._is_separator_regex else re.escape(separator)
+        splits = _split_text_with_regex_from_end(text, _separator, self._keep_separator)
+
+        # Now go merging things, recursively splitting longer texts.
+        _good_splits = []
+        _separator = "" if self._keep_separator else separator
+        for s in splits:
+            if self._length_function(s) < self._chunk_size:
+                _good_splits.append(s)
+            else:
+                if _good_splits:
+                    merged_text = self._merge_splits(_good_splits, _separator)
+                    final_chunks.extend(merged_text)
+                    _good_splits = []
+                if not new_separators:
+                    final_chunks.append(s)
+                else:
+                    other_info = self._split_text(s, new_separators)
+                    final_chunks.extend(other_info)
+        if _good_splits:
+            merged_text = self._merge_splits(_good_splits, _separator)
+            final_chunks.extend(merged_text)
+        return [re.sub(r"\n{2,}", "\n", chunk.strip()) for chunk in final_chunks if chunk.strip() != ""]
+
+
+class FeatureStore:
+    """Tokenize and extract features from the project's documents, for use in
+    the reject pipeline and response pipeline."""
+
+    def __init__(
+        self, embeddings: HuggingFaceEmbeddings, reranker: BCERerank, config_path: str = "rag_config.yaml", language: str = "zh"
+    ) -> None:
+        """Init with model device type and config."""
+        self.config_path = config_path
+        self.reject_throttle = -1
+        self.language = language
+        with open(config_path, "r", encoding="utf-8") as f:
+            config = yaml.safe_load(f)["feature_store"]
+            self.reject_throttle = config["reject_throttle"]
+
+        logger.warning(
+            "!!! If your feature generated by `text2vec-large-chinese` before 20240208, please rerun `python3 -m huixiangdou.service.feature_store`"  # noqa E501
+        )
+
+        logger.debug("loading text2vec model..")
+        self.embeddings = embeddings
+        self.reranker = reranker
+        self.compression_retriever = None
+        self.rejecter = None
+        self.retriever = None
+        self.md_splitter = MarkdownTextSplitter(chunk_size=768, chunk_overlap=32)
+
+        if language == "zh":
+            self.text_splitter = ChineseRecursiveTextSplitter(
+                keep_separator=True, is_separator_regex=True, chunk_size=768, chunk_overlap=32
+            )
+        else:
+            self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=768, chunk_overlap=32)
+
+        self.head_splitter = MarkdownHeaderTextSplitter(
+            headers_to_split_on=[
+                ("#", "Header 1"),
+                ("##", "Header 2"),
+                ("###", "Header 3"),
+            ]
+        )
+
+    def split_md(self, text: str, source: None):
+        """Split the markdown document in a nested way, first extracting the
+        header.
+
+        If the extraction result exceeds 1024, split it again according to
+        length.
+        """
+        docs = self.head_splitter.split_text(text)
+
+        final = []
+        for doc in docs:
+            header = ""
+            if len(doc.metadata) > 0:
+                if "Header 1" in doc.metadata:
+                    header += doc.metadata["Header 1"]
+                if "Header 2" in doc.metadata:
+                    header += " "
+                    header += doc.metadata["Header 2"]
+                if "Header 3" in doc.metadata:
+                    header += " "
+                    header += doc.metadata["Header 3"]
+
+            if len(doc.page_content) >= 1024:
+                subdocs = self.md_splitter.create_documents([doc.page_content])
+                for subdoc in subdocs:
+                    if len(subdoc.page_content) >= 10:
+                        final.append("{} {}".format(header, subdoc.page_content.lower()))
+            elif len(doc.page_content) >= 10:
+                final.append("{} {}".format(header, doc.page_content.lower()))  # noqa E501
+
+        for item in final:
+            if len(item) >= 1024:
+                logger.debug("source {} split length {}".format(source, len(item)))
+        return final
+
+    def clean_md(self, text: str):
+        """Remove parts of the markdown document that do not contain the key
+        question words, such as code blocks, URL links, etc."""
+        # remove ref
+        pattern_ref = r"\[(.*?)\]\(.*?\)"
+        new_text = re.sub(pattern_ref, r"\1", text)
+
+        # remove code block
+        pattern_code = r"```.*?```"
+        new_text = re.sub(pattern_code, "", new_text, flags=re.DOTALL)
+
+        # remove underline
+        new_text = re.sub("_{5,}", "", new_text)
+
+        # remove table
+        # new_text = re.sub('\|.*?\|\n\| *\:.*\: *\|.*\n(\|.*\|.*\n)*', '', new_text, flags=re.DOTALL)   # noqa E501
+
+        # use lower
+        new_text = new_text.lower()
+        return new_text
+
+    def get_md_documents(self, file: FileName):
+        documents = []
+        length = 0
+        text = ""
+        with open(file.copypath, encoding="utf8") as f:
+            text = f.read()
+        text = file.prefix + "\n" + self.clean_md(text)
+        if len(text) <= 1:
+            return [], length
+
+        chunks = self.split_md(text=text, source=os.path.abspath(file.copypath))
+        for chunk in chunks:
+            new_doc = Document(page_content=chunk, metadata={"source": file.basename, "read": file.copypath})
+            length += len(chunk)
+            documents.append(new_doc)
+        return documents, length
+
+    def get_text_documents(self, text: str, file: FileName):
+        if len(text) <= 1:
+            return []
+        chunks = self.text_splitter.create_documents([text])
+        documents = []
+        for chunk in chunks:
+            # `source` is for return references
+            # `read` is for LLM response
+            chunk.metadata = {"source": file.basename, "read": file.copypath}
+            documents.append(chunk)
+        return documents
+
+    def ingress_response(self, files: list, work_dir: str):
+        """Extract the features required for the response pipeline based on the
+        document."""
+        feature_dir = os.path.join(work_dir, "db_response")
+        if not os.path.exists(feature_dir):
+            os.makedirs(feature_dir)
+
+        # logger.info('glob {} in dir {}'.format(files, file_dir))
+        file_opr = FileOperation()
+        documents = []
+
+        for i, file in enumerate(files):
+            logger.debug("{}/{}.. {}".format(i + 1, len(files), file.basename))
+            if not file.state:
+                continue
+
+            if file._type == "md":
+                md_documents, md_length = self.get_md_documents(file)
+                documents += md_documents
+                logger.info("{} content length {}".format(file._type, md_length))
+                file.reason = str(md_length)
+
+            else:
+                # now read pdf/word/excel/ppt text
+                text, error = file_opr.read(file.copypath)
+                if error is not None:
+                    file.state = False
+                    file.reason = str(error)
+                    continue
+                file.reason = str(len(text))
+                logger.info("{} content length {}".format(file._type, len(text)))
+                text = file.prefix + text
+                documents += self.get_text_documents(text, file)
+
+        if len(documents) < 1:
+            return
+        vs = Vectorstore.from_documents(documents, self.embeddings)
+        vs.save_local(feature_dir)
+
+    def ingress_reject(self, files: list, work_dir: str):
+        """Extract the features required for the reject pipeline based on
+        documents."""
+        feature_dir = os.path.join(work_dir, "db_reject")
+        if not os.path.exists(feature_dir):
+            os.makedirs(feature_dir)
+
+        documents = []
+        file_opr = FileOperation()
+
+        logger.debug("ingress reject..")
+        for i, file in enumerate(files):
+            if not file.state:
+                continue
+
+            if file._type == "md":
+                # reject base not clean md
+                text = file.basename + "\n"
+                with open(file.copypath, encoding="utf8") as f:
+                    text += f.read()
+                if len(text) <= 1:
+                    continue
+
+                chunks = self.split_md(text=text, source=os.path.abspath(file.copypath))
+                for chunk in chunks:
+                    new_doc = Document(page_content=chunk, metadata={"source": file.basename, "read": file.copypath})
+                    documents.append(new_doc)
+
+            else:
+                text, error = file_opr.read(file.copypath)
+                if error is not None:
+                    continue
+                text = file.basename + text
+                documents += self.get_text_documents(text, file)
+
+        if len(documents) < 1:
+            return
+        vs = Vectorstore.from_documents(documents, self.embeddings)
+        vs.save_local(feature_dir)
+
+    def preprocess(self, files: list, work_dir: str):
+        """Preprocesses files in a given directory. Copies each file to
+        'preprocess' with new name formed by joining all subdirectories with
+        '_'.
+
+        Args:
+            files (list): original file list.
+            work_dir (str): Working directory where preprocessed files will be stored.  # noqa E501
+
+        Returns:
+            str: Path to the directory where preprocessed markdown files are saved.
+
+        Raises:
+            Exception: Raise an exception if no markdown files are found in the provided repository directory.  # noqa E501
+        """
+        preproc_dir = os.path.join(work_dir, "preprocess")
+        if not os.path.exists(preproc_dir):
+            os.makedirs(preproc_dir)
+
+        pool = Pool(processes=16)
+        file_opr = FileOperation()
+        for idx, file in enumerate(files):
+            if not os.path.exists(file.origin):
+                file.state = False
+                file.reason = "skip not exist"
+                continue
+
+            if file._type == "image":
+                file.state = False
+                file.reason = "skip image"
+
+            elif file._type in ["pdf", "word", "excel", "ppt", "html"]:
+                # read pdf/word/excel file and save to text format
+                md5 = file_opr.md5(file.origin)
+                file.copypath = os.path.join(preproc_dir, "{}.text".format(md5))
+                pool.apply_async(read_and_save, (file,))
+
+            elif file._type in ["md", "text"]:
+                # rename text files to new dir
+                md5 = file_opr.md5(file.origin)
+                file.copypath = os.path.join(preproc_dir, file.origin.replace("/", "_")[-84:])
+                try:
+                    shutil.copy(file.origin, file.copypath)
+                    file.state = True
+                    file.reason = "preprocessed"
+                except Exception as e:
+                    file.state = False
+                    file.reason = str(e)
+
+            else:
+                file.state = False
+                file.reason = "skip unknown format"
+        pool.close()
+        logger.debug("waiting for preprocess read finish..")
+        pool.join()
+
+        # check process result
+        for file in files:
+            if file._type in ["pdf", "word", "excel"]:
+                if os.path.exists(file.copypath):
+                    file.state = True
+                    file.reason = "preprocessed"
+                else:
+                    file.state = False
+                    file.reason = "read error"
+
+    def initialize(self, files: list, work_dir: str):
+        """Initializes response and reject feature store.
+
+        Only needs to be called once. Also calculates the optimal threshold
+        based on provided good and bad question examples, and saves it in the
+        configuration file.
+        """
+        logger.info("initialize response and reject feature store, you only need call this once.")  # noqa E501
+        self.preprocess(files=files, work_dir=work_dir)
+        self.ingress_response(files=files, work_dir=work_dir)
+        self.ingress_reject(files=files, work_dir=work_dir)
+
+
+def parse_args():
+    """Parse command-line arguments."""
+    parser = argparse.ArgumentParser(description="Feature store for processing directories.")
+    parser.add_argument("--work_dir", type=str, default="work_dir", help="Working directory.")
+    parser.add_argument("--repo_dir", type=str, default="repodir", help="Root directory where the repositories are located.")
+    parser.add_argument(
+        "--config_path", default="rag_config.yaml", help="Feature store configuration path. Default value is rag_config.yaml"
+    )
+    # parser.add_argument(
+    #     "--good_questions",
+    #     default="resource/good_questions.json",
+    #     help="Positive examples in the dataset. Default value is resource/good_questions.json",  # noqa E251  # noqa E501
+    # )
+    # parser.add_argument(
+    #     "--bad_questions",
+    #     default="resource/bad_questions.json",
+    #     help="Negative examples json path. Default value is resource/bad_questions.json",  # noqa E251  # noqa E501
+    # )
+    # parser.add_argument("--sample", help="Input an json file, save reject and search output.")
+    args = parser.parse_args()
+    return args
+
+
+# def test_reject(retriever: Retriever, sample: str = None):
+#     """Simple test reject pipeline."""
+#     if sample is None:
+#         real_questions = [
+#             "SAM 10个T 的训练集，怎么比比较公平呢~？速度上还有缺陷吧？",
+#             "想问下，如果只是推理的话，amp的fp16是不会省显存么，我看parameter仍然是float32，开和不开推理的显存占用都是一样的。能不能直接用把数据和model都 .half() 代替呢，相比之下amp好在哪里",  # noqa E501
+#             "mmdeploy支持ncnn vulkan部署么，我只找到了ncnn cpu 版本",
+#             "大佬们，如果我想在高空检测安全帽，我应该用 mmdetection 还是 mmrotate",
+#             "请问 ncnn 全称是什么",
+#             "有啥中文的 text to speech 模型吗?",
+#             "今天中午吃什么？",
+#             "huixiangdou 是什么？",
+#             "mmpose 如何安装？",
+#             "使用科研仪器需要注意什么？",
+#         ]
+#     else:
+#         with open(sample) as f:
+#             real_questions = json.load(f)
+
+#     for example in real_questions:
+#         reject, _ = retriever.is_reject(example)
+
+#         if reject:
+#             logger.error(f"reject query: {example}")
+#         else:
+#             logger.warning(f"process query: {example}")
+
+#         if sample is not None:
+#             if reject:
+#                 with open("workdir/negative.txt", "a+") as f:
+#                     f.write(example)
+#                     f.write("\n")
+#             else:
+#                 with open("workdir/positive.txt", "a+") as f:
+#                     f.write(example)
+#                     f.write("\n")
+
+#     empty_cache()
+
+
+def test_query(retriever: Retriever, sample: str = None):
+    """Simple test response pipeline."""
+    if sample is not None:
+        with open(sample) as f:
+            real_questions = json.load(f)
+        logger.add("logs/feature_store_query.log", rotation="4MB")
+    else:
+        real_questions = ["mmpose installation", "how to use std::vector ?"]
+
+    for example in real_questions:
+        example = example[0:400]
+        print(retriever.query(example))
+        empty_cache()
+
+    empty_cache()
+
+
+def fix_system_error():
+    """
+    Fix `No module named 'faiss.swigfaiss_avx2`
+    """
+    import os
+    from pathlib import Path
+
+    import faiss
+
+    if Path(faiss.__file__).parent.joinpath("swigfaiss_avx2.py").exists():
+        return
+
+    print("Fixing faiss error...")
+    os.system(f"cd {Path(faiss.__file__).parent} && ln -s swigfaiss.py swigfaiss_avx2.py")
+
+
+def gen_vector_db(config_path, source_dir, work_dir, test_mode=False, update_reject=False):
+
+    # 解决 faiss 导入问题
+    fix_system_error()
+
+    # 必须是绝对路径，否则加载会有问题
+    work_dir = str(Path(work_dir).absolute())
+
+    cache = CacheRetriever(config_path=config_path)
+
+    # 生成向量数据库
+    fs_init = FeatureStore(embeddings=cache.embeddings, reranker=cache.reranker, config_path=config_path)
+
+    # walk all files in repo dir
+    file_opr = FileOperation()
+    files = file_opr.scan_dir(repo_dir=source_dir)
+    fs_init.initialize(files=files, work_dir=work_dir)
+    file_opr.summarize(files)
+    del fs_init
+
+    # update reject throttle
+    if update_reject:
+        # 目前没有用到这块，可忽略
+        retriever = cache.get(config_path=config_path, work_dir=work_dir)
+        with open(os.path.join("resource", "good_questions.json")) as f:
+            good_questions = json.load(f)
+        with open(os.path.join("resource", "bad_questions.json")) as f:
+            bad_questions = json.load(f)
+        retriever.update_throttle(config_path=config_path, good_questions=good_questions, bad_questions=bad_questions)
+
+        cache.pop("default")
+
+    if test_mode:
+        # test
+        retriever = cache.get(config_path=config_path, work_dir=work_dir)
+        # test_reject(retriever, args.sample)
+        test_query(retriever, args.sample)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    gen_vector_db(args.config_path, args.repo_dir, args.work_dir, test_mode=True)
diff --git a/utils/rag/file_operation.py b/utils/rag/file_operation.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c56f0535e7d615d745f108205561c58ece93d69
--- /dev/null
+++ b/utils/rag/file_operation.py
@@ -0,0 +1,228 @@
+import hashlib
+import os
+
+import pandas as pd
+from bs4 import BeautifulSoup
+from loguru import logger
+
+
+class FileName:
+    """Record file original name, state and copied filepath with text
+    format."""
+
+    def __init__(self, root: str, filename: str, _type: str):
+        self.root = root
+        self.prefix = filename.replace("/", "_")
+        self.basename = os.path.basename(filename)
+        self.origin = os.path.join(root, filename)
+        self.copypath = ""
+        self._type = _type
+        self.state = True
+        self.reason = ""
+
+    def __str__(self):
+        return "{},{},{},{}\n".format(self.basename, self.copypath, self.state, self.reason)
+
+
+class FileOperation:
+    """Encapsulate all file reading operations."""
+
+    def __init__(self):
+        self.image_suffix = [".jpg", ".jpeg", ".png", ".bmp"]
+        self.md_suffix = ".md"
+        self.text_suffix = [".txt", ".text"]
+        self.excel_suffix = [".xlsx", ".xls", ".csv"]
+        self.pdf_suffix = ".pdf"
+        self.ppt_suffix = ".pptx"
+        self.html_suffix = [".html", ".htm", ".shtml", ".xhtml"]
+        self.word_suffix = [".docx", ".doc"]
+        self.normal_suffix = (
+            [self.md_suffix]
+            + self.text_suffix
+            + self.excel_suffix
+            + [self.pdf_suffix]
+            + self.word_suffix
+            + [self.ppt_suffix]
+            + self.html_suffix
+        )
+
+    def get_type(self, filepath: str):
+        filepath = filepath.lower()
+        if filepath.endswith(self.pdf_suffix):
+            return "pdf"
+
+        if filepath.endswith(self.md_suffix):
+            return "md"
+
+        if filepath.endswith(self.ppt_suffix):
+            return "ppt"
+
+        for suffix in self.image_suffix:
+            if filepath.endswith(suffix):
+                return "image"
+
+        for suffix in self.text_suffix:
+            if filepath.endswith(suffix):
+                return "text"
+
+        for suffix in self.word_suffix:
+            if filepath.endswith(suffix):
+                return "word"
+
+        for suffix in self.excel_suffix:
+            if filepath.endswith(suffix):
+                return "excel"
+
+        for suffix in self.html_suffix:
+            if filepath.endswith(suffix):
+                return "html"
+        return None
+
+    def md5(self, filepath: str):
+        hash_object = hashlib.sha256()
+        with open(filepath, "rb") as file:
+            chunk_size = 8192
+            while chunk := file.read(chunk_size):
+                hash_object.update(chunk)
+
+        return hash_object.hexdigest()[0:8]
+
+    def summarize(self, files: list):
+        success = 0
+        skip = 0
+        failed = 0
+
+        for file in files:
+            if file.state:
+                success += 1
+            elif file.reason == "skip":
+                skip += 1
+            else:
+                logger.info("{} {}".format(file.origin, file.reason))
+                failed += 1
+
+            logger.info("{} {}".format(file.reason, file.copypath))
+        logger.info("累计{}文件，成功{}个，跳过{}个，异常{}个".format(len(files), success, skip, failed))
+
+    def scan_dir(self, repo_dir: str):
+        files = []
+        for root, _, filenames in os.walk(repo_dir):
+            for filename in filenames:
+                _type = self.get_type(filename)
+                if _type is not None:
+                    files.append(FileName(root=root, filename=filename, _type=_type))
+        return files
+
+    def read_pdf(self, filepath: str):
+        # load pdf and serialize table
+
+        # TODO fitz 安装有些不兼容，后续按需完善
+        import fitz
+
+        text = ""
+        with fitz.open(filepath) as pages:
+            for page in pages:
+                text += page.get_text()
+                tables = page.find_tables()
+                for table in tables:
+                    tablename = "_".join(filter(lambda x: x is not None and "Col" not in x, table.header.names))
+                    pan = table.to_pandas()
+                    json_text = pan.dropna(axis=1).to_json(force_ascii=False)
+                    text += tablename
+                    text += "\n"
+                    text += json_text
+                    text += "\n"
+        return text
+
+    def read_excel(self, filepath: str):
+        table = None
+        if filepath.endswith(".csv"):
+            table = pd.read_csv(filepath)
+        else:
+            table = pd.read_excel(filepath)
+        if table is None:
+            return ""
+        json_text = table.dropna(axis=1).to_json(force_ascii=False)
+        return json_text
+
+    def read(self, filepath: str):
+        file_type = self.get_type(filepath)
+
+        text = ""
+
+        if not os.path.exists(filepath):
+            return text, None
+
+        try:
+
+            if file_type == "md" or file_type == "text":
+                with open(filepath) as f:
+                    text = f.read()
+
+            elif file_type == "pdf":
+                text += self.read_pdf(filepath)
+
+            elif file_type == "excel":
+                text += self.read_excel(filepath)
+
+            elif file_type == "word" or file_type == "ppt":
+                # https://stackoverflow.com/questions/36001482/read-doc-file-with-python
+                # https://textract.readthedocs.io/en/latest/installation.html
+
+                # TODO textract 在 pip 高于 24.1 后安装不了，因为其库自身原因，后续按需进行完善
+                # 可自行安装 pip install textract==1.6.5
+                import textract  # for word and ppt
+
+                text = textract.process(filepath).decode("utf8")
+                if file_type == "ppt":
+                    text = text.replace("\n", " ")
+
+            elif file_type == "html":
+                with open(filepath) as f:
+                    soup = BeautifulSoup(f.read(), "html.parser")
+                    text += soup.text
+
+        except Exception as e:
+            logger.error((filepath, str(e)))
+            return "", e
+        text = text.replace("\n\n", "\n")
+        text = text.replace("\n\n", "\n")
+        text = text.replace("\n\n", "\n")
+        text = text.replace("  ", " ")
+        text = text.replace("  ", " ")
+        text = text.replace("  ", " ")
+        return text, None
+
+
+if __name__ == "__main__":
+
+    def get_pdf_files(directory):
+        pdf_files = []
+        # 遍历目录
+        for root, dirs, files in os.walk(directory):
+            for file in files:
+                # 检查文件扩展名是否为.pdf
+                if file.lower().endswith(".pdf"):
+                    # 将完整路径添加到列表中
+                    pdf_files.append(os.path.abspath(os.path.join(root, file)))
+        return pdf_files
+
+    # 将你想要搜索的目录替换为下面的路径
+    pdf_list = get_pdf_files("/home/khj/huixiangdou-web-online-data/hxd-bad-file")
+
+    # 打印所有找到的PDF文件的绝对路径
+
+    opr = FileOperation()
+    for pdf_path in pdf_list:
+        text, error = opr.read(pdf_path)
+        print("processing {}".format(pdf_path))
+        if error is not None:
+            # pdb.set_trace()
+            print("")
+
+        else:
+            if text is not None:
+                print(len(text))
+            else:
+                # pdb.set_trace()
+                print("")
diff --git a/utils/rag/rag_worker.py b/utils/rag/rag_worker.py
new file mode 100644
index 0000000000000000000000000000000000000000..629ea28311ad5ba2e369d2e485d842e50fad75ca
--- /dev/null
+++ b/utils/rag/rag_worker.py
@@ -0,0 +1,101 @@
+import shutil
+from pathlib import Path
+
+import streamlit as st
+import torch
+import yaml
+
+from utils.rag.feature_store import gen_vector_db
+from utils.rag.retriever import CacheRetriever
+from utils.web_configs import WEB_CONFIGS
+
+# 基础配置
+CONTEXT_MAX_LENGTH = 3000  # 上下文最大长度
+GENERATE_TEMPLATE = "这是说明书：“{}”\n 客户的问题：“{}” \n 请阅读说明并运用你的性格进行解答。"  # RAG prompt 模板
+
+
+def build_rag_prompt(rag_retriever: CacheRetriever, product_name, prompt):
+
+    real_retriever = rag_retriever.get(fs_id="default")
+
+    if isinstance(real_retriever, tuple):
+        print(f" @@@ GOT real_retriever == tuple : {real_retriever}")
+        return ""
+
+    chunk, db_context, references = real_retriever.query(
+        f"商品名：{product_name}。{prompt}", context_max_length=CONTEXT_MAX_LENGTH - 2 * len(GENERATE_TEMPLATE)
+    )
+    print(f"db_context = {db_context}")
+
+    if db_context is not None and len(db_context) > 1:
+        prompt_rag = GENERATE_TEMPLATE.format(db_context, prompt)
+    else:
+        print("db_context get error")
+        prompt_rag = prompt
+
+    print(f"RAG reference = {references}")
+    print("=" * 20)
+
+    return prompt_rag
+
+
+def init_rag_retriever(rag_config: str, db_path: str):
+    torch.cuda.empty_cache()
+
+    retriever = CacheRetriever(config_path=rag_config)
+
+    # 初始化
+    retriever.get(fs_id="default", config_path=rag_config, work_dir=db_path)
+
+    return retriever
+
+
+def gen_rag_db(force_gen=False):
+    """
+    生成向量数据库。
+
+    参数:
+    force_gen - 布尔值，当设置为 True 时，即使数据库已存在也会重新生成数据库。
+    """
+
+    # 检查数据库目录是否存在，如果存在且force_gen为False，则不执行生成操作
+    if Path(WEB_CONFIGS.RAG_VECTOR_DB_DIR).exists() and not force_gen:
+        return
+
+    if force_gen and Path(WEB_CONFIGS.RAG_VECTOR_DB_DIR).exists():
+        shutil.rmtree(WEB_CONFIGS.RAG_VECTOR_DB_DIR)
+
+    # 仅仅遍历 instructions 字段里面的文件
+    if Path(WEB_CONFIGS.PRODUCT_INSTRUCTION_DIR_GEN_DB_TMP).exists():
+        shutil.rmtree(WEB_CONFIGS.PRODUCT_INSTRUCTION_DIR_GEN_DB_TMP)
+    Path(WEB_CONFIGS.PRODUCT_INSTRUCTION_DIR_GEN_DB_TMP).mkdir(exist_ok=True, parents=True)
+
+    # 读取 yaml 文件，获取所有说明书路径，并移动到 tmp 目录
+    with open(WEB_CONFIGS.PRODUCT_INFO_YAML_PATH, "r", encoding="utf-8") as f:
+        product_info_dict = yaml.safe_load(f)
+    for _, info in product_info_dict.items():
+        shutil.copyfile(
+            info["instruction"], Path(WEB_CONFIGS.PRODUCT_INSTRUCTION_DIR_GEN_DB_TMP).joinpath(Path(info["instruction"]).name)
+        )
+
+    print("Generating rag database, pls wait ...")
+    # 调用函数生成向量数据库
+    gen_vector_db(
+        WEB_CONFIGS.RAG_CONFIG_PATH,
+        str(Path(WEB_CONFIGS.PRODUCT_INSTRUCTION_DIR_GEN_DB_TMP).absolute()),
+        WEB_CONFIGS.RAG_VECTOR_DB_DIR,
+    )
+
+    # 删除过程文件
+    shutil.rmtree(WEB_CONFIGS.PRODUCT_INSTRUCTION_DIR_GEN_DB_TMP)
+
+
+@st.cache_resource
+def load_rag_model():
+    # 生成 rag 数据库
+    gen_rag_db()
+
+    # 加载 rag 模型
+    retriever = init_rag_retriever(rag_config=WEB_CONFIGS.RAG_CONFIG_PATH, db_path=WEB_CONFIGS.RAG_VECTOR_DB_DIR)
+
+    return retriever
diff --git a/utils/rag/retriever.py b/utils/rag/retriever.py
new file mode 100644
index 0000000000000000000000000000000000000000..eaf159066c6a6b37f0fb8993d1f61431b4a3a94d
--- /dev/null
+++ b/utils/rag/retriever.py
@@ -0,0 +1,249 @@
+"""extract feature and search with user query."""
+
+import os
+import time
+
+import numpy as np
+import yaml
+from BCEmbedding.tools.langchain import BCERerank
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.retrievers import ContextualCompressionRetriever
+from langchain.vectorstores.faiss import FAISS as Vectorstore
+from langchain_community.vectorstores.utils import DistanceStrategy
+from loguru import logger
+from modelscope import snapshot_download
+from sklearn.metrics import precision_recall_curve
+
+from utils.web_configs import WEB_CONFIGS
+
+try:
+    from utils.rag.file_operation import FileOperation
+except:
+    # 用于 DEBUG
+    from file_operation import FileOperation
+
+
+class Retriever:
+    """Tokenize and extract features from the project's documents, for use in
+    the reject pipeline and response pipeline."""
+
+    def __init__(self, embeddings, reranker, work_dir: str, reject_throttle: float) -> None:
+        """Init with model device type and config."""
+        self.reject_throttle = reject_throttle
+        self.rejecter = Vectorstore.load_local(
+            os.path.join(work_dir, "db_reject"), embeddings=embeddings, allow_dangerous_deserialization=True
+        )
+        self.retriever = Vectorstore.load_local(
+            os.path.join(work_dir, "db_response"),
+            embeddings=embeddings,
+            allow_dangerous_deserialization=True,
+            distance_strategy=DistanceStrategy.MAX_INNER_PRODUCT,
+        ).as_retriever(search_type="similarity", search_kwargs={"score_threshold": 0.15, "k": 30})
+        self.compression_retriever = ContextualCompressionRetriever(base_compressor=reranker, base_retriever=self.retriever)
+
+    def is_reject(self, question, k=30, disable_throttle=False):
+        """If no search results below the threshold can be found from the
+        database, reject this query."""
+        if disable_throttle:
+            # for searching throttle during update sample
+            docs_with_score = self.rejecter.similarity_search_with_relevance_scores(question, k=1)
+            if len(docs_with_score) < 1:
+                return True, docs_with_score
+            return False, docs_with_score
+        else:
+            # for retrieve result
+            # if no chunk passed the throttle, give the max
+            docs_with_score = self.rejecter.similarity_search_with_relevance_scores(question, k=k)
+            ret = []
+            max_score = -1
+            top1 = None
+            for doc, score in docs_with_score:
+                if score >= self.reject_throttle:
+                    ret.append(doc)
+                if score > max_score:
+                    max_score = score
+                    top1 = (doc, score)
+            reject = False if len(ret) > 0 else True
+            return reject, [top1]
+
+    def update_throttle(self, config_path: str = "config.yaml", good_questions=[], bad_questions=[]):
+        """Update reject throttle based on positive and negative examples."""
+
+        if len(good_questions) == 0 or len(bad_questions) == 0:
+            raise Exception("good and bad question examples cat not be empty.")
+        questions = good_questions + bad_questions
+        predictions = []
+        for question in questions:
+            self.reject_throttle = -1
+            _, docs = self.is_reject(question=question, disable_throttle=True)
+            score = docs[0][1]
+            predictions.append(max(0, score))
+
+        labels = [1 for _ in range(len(good_questions))] + [0 for _ in range(len(bad_questions))]
+        precision, recall, thresholds = precision_recall_curve(labels, predictions)
+
+        # get the best index for sum(precision, recall)
+        sum_precision_recall = precision[:-1] + recall[:-1]
+        index_max = np.argmax(sum_precision_recall)
+        optimal_threshold = max(thresholds[index_max], 0.0)
+
+        with open(config_path, "r", encoding="utf-8") as f:
+            config = yaml.safe_load(f)
+        config["feature_store"]["reject_throttle"] = float(optimal_threshold)
+        with open(config_path, "w", encoding="utf8") as f:
+            yaml.dump(config, f)
+        logger.info(f"The optimal threshold is: {optimal_threshold}, saved it to {config_path}")  # noqa E501
+
+    def query(self, question: str, context_max_length: int = 16000):  # , tracker: QueryTracker = None):
+        """Processes a query and returns the best match from the vector store
+        database. If the question is rejected, returns None.
+
+        Args:
+            question (str): The question asked by the user.
+
+        Returns:
+            str: The best matching chunk, or None.
+            str: The best matching text, or None
+        """
+        print(f"DEBUG -1: enter query")
+
+        if question is None or len(question) < 1:
+            print(f"DEBUG 0: len error")
+
+            return None, None, []
+
+        if len(question) > 512:
+            logger.warning("input too long, truncate to 512")
+            question = question[0:512]
+
+        # reject, docs = self.is_reject(question=question)
+        # assert (len(docs) > 0)
+        # if reject:
+        # return None, None, [docs[0][0].metadata['source']]
+
+        docs = self.compression_retriever.get_relevant_documents(question)
+
+        print(f"DEBUG 1: {docs}")
+
+        # if tracker is not None:
+        #     tracker.log('retrieve', [doc.metadata['source'] for doc in docs])
+        chunks = []
+        context = ""
+        references = []
+
+        # add file text to context, until exceed `context_max_length`
+
+        file_opr = FileOperation()
+        for idx, doc in enumerate(docs):
+            chunk = doc.page_content
+            chunks.append(chunk)
+
+            if "read" not in doc.metadata:
+                logger.error(
+                    "If you are using the version before 20240319, please rerun `python3 -m huixiangdou.service.feature_store`"
+                )
+                raise Exception("huixiangdou version mismatch")
+            file_text, error = file_opr.read(doc.metadata["read"])
+            if error is not None:
+                # read file failed, skip
+                print(f"DEBUG 2: error")
+
+                continue
+
+            source = doc.metadata["source"]
+            logger.info("target {} file length {}".format(source, len(file_text)))
+
+            print(f"DEBUG 3: target {source}, file length {len(file_text)}")
+
+            if len(file_text) + len(context) > context_max_length:
+                if source in references:
+                    continue
+                references.append(source)
+                # add and break
+                add_len = context_max_length - len(context)
+                if add_len <= 0:
+                    break
+                chunk_index = file_text.find(chunk)
+                if chunk_index == -1:
+                    # chunk not in file_text
+                    context += chunk
+                    context += "\n"
+                    context += file_text[0 : add_len - len(chunk) - 1]
+                else:
+                    start_index = max(0, chunk_index - (add_len - len(chunk)))
+                    context += file_text[start_index : start_index + add_len]
+                break
+
+            if source not in references:
+                context += file_text
+                context += "\n"
+                references.append(source)
+
+        context = context[0:context_max_length]
+        logger.debug("query:{} top1 file:{}".format(question, references[0]))
+        return "\n".join(chunks), context, [os.path.basename(r) for r in references]
+
+
+class CacheRetriever:
+
+    def __init__(self, config_path: str, max_len: int = 4):
+        self.cache = dict()
+        self.max_len = max_len
+        with open(config_path, "r", encoding="utf-8") as f:
+            config = yaml.safe_load(f)["feature_store"]
+            embedding_model_path = config["embedding_model_path"]
+            reranker_model_path = config["reranker_model_path"]
+
+        embedding_model_path = snapshot_download(embedding_model_path, cache_dir=WEB_CONFIGS.RAG_MODEL_DIR)
+        reranker_model_path = snapshot_download(reranker_model_path, cache_dir=WEB_CONFIGS.RAG_MODEL_DIR)
+
+        # load text2vec and rerank model
+        logger.info("loading test2vec and rerank models")
+        self.embeddings = HuggingFaceEmbeddings(
+            model_name=embedding_model_path,
+            model_kwargs={"device": "cuda"},
+            encode_kwargs={"batch_size": 1, "normalize_embeddings": True},
+        )
+        self.embeddings.client = self.embeddings.client.half()
+        reranker_args = {"model": reranker_model_path, "top_n": 7, "device": "cuda", "use_fp16": True}
+        self.reranker = BCERerank(**reranker_args)
+
+    def get(self, fs_id: str = "default", config_path="config.yaml", work_dir="workdir"):
+        if fs_id in self.cache:
+            self.cache[fs_id]["time"] = time.time()
+            return self.cache[fs_id]["retriever"]
+
+        if not os.path.exists(work_dir) or not os.path.exists(config_path):
+            return None, "workdir or config.yaml not exist"
+
+        with open(config_path, "r", encoding="utf-8") as f:
+            reject_throttle = yaml.safe_load(f)["feature_store"]["reject_throttle"]
+
+        if len(self.cache) >= self.max_len:
+            # drop the oldest one
+            del_key = None
+            min_time = time.time()
+            for key, value in self.cache.items():
+                cur_time = value["time"]
+                if cur_time < min_time:
+                    min_time = cur_time
+                    del_key = key
+
+            if del_key is not None:
+                del_value = self.cache[del_key]
+                self.cache.pop(del_key)
+                del del_value["retriever"]
+
+        retriever = Retriever(
+            embeddings=self.embeddings, reranker=self.reranker, work_dir=work_dir, reject_throttle=reject_throttle
+        )
+        self.cache[fs_id] = {"retriever": retriever, "time": time.time()}
+        return retriever
+
+    def pop(self, fs_id: str):
+        if fs_id not in self.cache:
+            return
+        del_value = self.cache[fs_id]
+        self.cache.pop(fs_id)
+        # manually free memory
+        del del_value
diff --git a/utils/rag/test_queries.json b/utils/rag/test_queries.json
new file mode 100644
index 0000000000000000000000000000000000000000..40b9539ad50e574f21d6cbdd96a17491818c8f6d
--- /dev/null
+++ b/utils/rag/test_queries.json
@@ -0,0 +1,4 @@
+[
+  "我的商品是牛肉。饲养天数",
+  "我的商品是唇膏。净含量是多少"
+]
\ No newline at end of file
diff --git a/utils/tools.py b/utils/tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b84022c37f7a41eb14bcd8046e0830f49881b4f
--- /dev/null
+++ b/utils/tools.py
@@ -0,0 +1,26 @@
+import cv2
+
+
+def resize_image(image_path, max_height):
+    """
+    缩放图像，保持纵横比，将图像的高度调整为指定的最大高度。
+
+    参数:
+    - image_path: 图像文件的路径。
+    - max_height: 指定的最大高度值。
+
+    返回:
+    - resized_image: 缩放后的图像。
+    """
+
+    # 读取图片
+    image = cv2.imread(image_path)
+    height, width = image.shape[:2]
+
+    # 计算新的宽度，保持纵横比
+    new_width = int(width * max_height / height)
+
+    # 缩放图片
+    resized_image = cv2.resize(image, (new_width, max_height))
+
+    return resized_image
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diff --git a/utils/tts/gpt_sovits/AR/models/t2s_lightning_module.py b/utils/tts/gpt_sovits/AR/models/t2s_lightning_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..f702a3434c3c06ed0c18c91092376524e89d4939
--- /dev/null
+++ b/utils/tts/gpt_sovits/AR/models/t2s_lightning_module.py
@@ -0,0 +1,105 @@
+# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_lightning_module.py
+# reference: https://github.com/lifeiteng/vall-e
+import os
+import sys
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+from typing import Dict
+
+import torch
+from pytorch_lightning import LightningModule
+
+from utils.tts.gpt_sovits.AR.models.t2s_model import Text2SemanticDecoder
+from utils.tts.gpt_sovits.AR.modules.lr_schedulers import \
+    WarmupCosineLRSchedule
+from utils.tts.gpt_sovits.AR.modules.optim import ScaledAdam
+
+
+class Text2SemanticLightningModule(LightningModule):
+    def __init__(self, config, output_dir, is_train=True):
+        super().__init__()
+        self.config = config
+        self.top_k = 3
+        self.model = Text2SemanticDecoder(config=config, top_k=self.top_k)
+        pretrained_s1 = config.get("pretrained_s1")
+        if pretrained_s1 and is_train:
+            # print(self.load_state_dict(torch.load(pretrained_s1,map_location="cpu")["state_dict"]))
+            print(self.load_state_dict(torch.load(pretrained_s1, map_location="cpu")["weight"]))
+        if is_train:
+            self.automatic_optimization = False
+            self.save_hyperparameters()
+            self.eval_dir = output_dir / "eval"
+            self.eval_dir.mkdir(parents=True, exist_ok=True)
+
+    def training_step(self, batch: Dict, batch_idx: int):
+        opt = self.optimizers()
+        scheduler = self.lr_schedulers()
+        forward = self.model.forward if self.config["train"].get("if_dpo", False) == True else self.model.forward_old
+        loss, acc = forward(
+            batch["phoneme_ids"],
+            batch["phoneme_ids_len"],
+            batch["semantic_ids"],
+            batch["semantic_ids_len"],
+            batch["bert_feature"],
+        )
+        self.manual_backward(loss)
+        if batch_idx > 0 and batch_idx % 4 == 0:
+            opt.step()
+            opt.zero_grad()
+            scheduler.step()
+
+        self.log(
+            "total_loss",
+            loss,
+            on_step=True,
+            on_epoch=True,
+            prog_bar=True,
+            sync_dist=True,
+        )
+        self.log(
+            "lr",
+            scheduler.get_last_lr()[0],
+            on_epoch=True,
+            prog_bar=True,
+            sync_dist=True,
+        )
+        self.log(
+            f"top_{self.top_k}_acc",
+            acc,
+            on_step=True,
+            on_epoch=True,
+            prog_bar=True,
+            sync_dist=True,
+        )
+
+    def validation_step(self, batch: Dict, batch_idx: int):
+        return
+
+    def configure_optimizers(self):
+        model_parameters = self.model.parameters()
+        parameters_names = []
+        parameters_names.append([name_param_pair[0] for name_param_pair in self.model.named_parameters()])
+        lm_opt = ScaledAdam(
+            model_parameters,
+            lr=0.01,
+            betas=(0.9, 0.95),
+            clipping_scale=2.0,
+            parameters_names=parameters_names,
+            show_dominant_parameters=False,
+            clipping_update_period=1000,
+        )
+
+        return {
+            "optimizer": lm_opt,
+            "lr_scheduler": {
+                "scheduler": WarmupCosineLRSchedule(
+                    lm_opt,
+                    init_lr=self.config["optimizer"]["lr_init"],
+                    peak_lr=self.config["optimizer"]["lr"],
+                    end_lr=self.config["optimizer"]["lr_end"],
+                    warmup_steps=self.config["optimizer"]["warmup_steps"],
+                    total_steps=self.config["optimizer"]["decay_steps"],
+                )
+            },
+        }
diff --git a/utils/tts/gpt_sovits/AR/models/t2s_model.py b/utils/tts/gpt_sovits/AR/models/t2s_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..9488f08da3d0c396aba6213b779f008c18b6ab51
--- /dev/null
+++ b/utils/tts/gpt_sovits/AR/models/t2s_model.py
@@ -0,0 +1,416 @@
+# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
+# reference: https://github.com/lifeiteng/vall-e
+import time
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torchmetrics.classification import MulticlassAccuracy
+from tqdm import tqdm
+
+from utils.tts.gpt_sovits.AR.models.utils import (dpo_loss, get_batch_logps,
+                                                  make_pad_mask, make_reject_y,
+                                                  sample, topk_sampling)
+from utils.tts.gpt_sovits.AR.modules.embedding import (SinePositionalEmbedding,
+                                                       TokenEmbedding)
+from utils.tts.gpt_sovits.AR.modules.transformer import (
+    LayerNorm, TransformerEncoder, TransformerEncoderLayer)
+
+default_config = {
+    "embedding_dim": 512,
+    "hidden_dim": 512,
+    "num_head": 8,
+    "num_layers": 12,
+    "num_codebook": 8,
+    "p_dropout": 0.0,
+    "vocab_size": 1024 + 1,
+    "phoneme_vocab_size": 512,
+    "EOS": 1024,
+}
+
+
+class Text2SemanticDecoder(nn.Module):
+    def __init__(self, config, norm_first=False, top_k=3):
+        super(Text2SemanticDecoder, self).__init__()
+        self.model_dim = config["model"]["hidden_dim"]
+        self.embedding_dim = config["model"]["embedding_dim"]
+        self.num_head = config["model"]["head"]
+        self.num_layers = config["model"]["n_layer"]
+        self.norm_first = norm_first
+        self.vocab_size = config["model"]["vocab_size"]
+        self.phoneme_vocab_size = config["model"]["phoneme_vocab_size"]
+        self.p_dropout = config["model"]["dropout"]
+        self.EOS = config["model"]["EOS"]
+        self.norm_first = norm_first
+        assert self.EOS == self.vocab_size - 1
+        # should be same as num of kmeans bin
+        # assert self.EOS == 1024
+        self.bert_proj = nn.Linear(1024, self.embedding_dim)
+        self.ar_text_embedding = TokenEmbedding(self.embedding_dim, self.phoneme_vocab_size, self.p_dropout)
+        self.ar_text_position = SinePositionalEmbedding(self.embedding_dim, dropout=0.1, scale=False, alpha=True)
+        self.ar_audio_embedding = TokenEmbedding(self.embedding_dim, self.vocab_size, self.p_dropout)
+        self.ar_audio_position = SinePositionalEmbedding(self.embedding_dim, dropout=0.1, scale=False, alpha=True)
+
+        self.h = TransformerEncoder(
+            TransformerEncoderLayer(
+                d_model=self.model_dim,
+                nhead=self.num_head,
+                dim_feedforward=self.model_dim * 4,
+                dropout=0.1,
+                batch_first=True,
+                norm_first=norm_first,
+            ),
+            num_layers=self.num_layers,
+            norm=LayerNorm(self.model_dim) if norm_first else None,
+        )
+
+        self.ar_predict_layer = nn.Linear(self.model_dim, self.vocab_size, bias=False)
+        self.loss_fct = nn.CrossEntropyLoss(reduction="sum")
+
+        self.ar_accuracy_metric = MulticlassAccuracy(
+            self.vocab_size,
+            top_k=top_k,
+            average="micro",
+            multidim_average="global",
+            ignore_index=self.EOS,
+        )
+
+    def make_input_data(self, x, x_lens, y, y_lens, bert_feature):
+        x = self.ar_text_embedding(x)
+        x = x + self.bert_proj(bert_feature.transpose(1, 2))
+        x = self.ar_text_position(x)
+        x_mask = make_pad_mask(x_lens)
+
+        y_mask = make_pad_mask(y_lens)
+        y_mask_int = y_mask.type(torch.int64)
+        codes = y.type(torch.int64) * (1 - y_mask_int)
+
+        # Training
+        # AR Decoder
+        y, targets = self.pad_y_eos(codes, y_mask_int, eos_id=self.EOS)
+        x_len = x_lens.max()
+        y_len = y_lens.max()
+        y_emb = self.ar_audio_embedding(y)
+        y_pos = self.ar_audio_position(y_emb)
+
+        xy_padding_mask = torch.concat([x_mask, y_mask], dim=1)
+
+        ar_xy_padding_mask = xy_padding_mask
+
+        x_attn_mask = F.pad(
+            torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device),
+            (0, y_len),
+            value=True,
+        )
+
+        y_attn_mask = F.pad(
+            torch.triu(
+                torch.ones(y_len, y_len, dtype=torch.bool, device=x.device),
+                diagonal=1,
+            ),
+            (x_len, 0),
+            value=False,
+        )
+
+        xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0)
+        bsz, src_len = x.shape[0], x_len + y_len
+        _xy_padding_mask = (
+            ar_xy_padding_mask.view(bsz, 1, 1, src_len).expand(-1, self.num_head, -1, -1).reshape(bsz * self.num_head, 1, src_len)
+        )
+        xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)
+        new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)
+        new_attn_mask.masked_fill_(xy_attn_mask, float("-inf"))
+        xy_attn_mask = new_attn_mask
+        # x 和完整的 y 一次性输入模型
+        xy_pos = torch.concat([x, y_pos], dim=1)
+
+        return xy_pos, xy_attn_mask, targets
+
+    def forward(self, x, x_lens, y, y_lens, bert_feature):
+        """
+        x: phoneme_ids
+        y: semantic_ids
+        """
+
+        reject_y, reject_y_lens = make_reject_y(y, y_lens)
+
+        xy_pos, xy_attn_mask, targets = self.make_input_data(x, x_lens, y, y_lens, bert_feature)
+
+        xy_dec, _ = self.h(
+            (xy_pos, None),
+            mask=xy_attn_mask,
+        )
+        x_len = x_lens.max()
+        logits = self.ar_predict_layer(xy_dec[:, x_len:])
+
+        ###### DPO #############
+        reject_xy_pos, reject_xy_attn_mask, reject_targets = self.make_input_data(
+            x, x_lens, reject_y, reject_y_lens, bert_feature
+        )
+
+        reject_xy_dec, _ = self.h(
+            (reject_xy_pos, None),
+            mask=reject_xy_attn_mask,
+        )
+        x_len = x_lens.max()
+        reject_logits = self.ar_predict_layer(reject_xy_dec[:, x_len:])
+
+        # loss
+        # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum
+
+        loss_1 = F.cross_entropy(logits.permute(0, 2, 1), targets, reduction="sum")
+        acc = self.ar_accuracy_metric(logits.permute(0, 2, 1).detach(), targets).item()
+
+        A_logits, R_logits = get_batch_logps(logits, reject_logits, targets, reject_targets)
+        loss_2, _, _ = dpo_loss(A_logits, R_logits, 0, 0, 0.2, reference_free=True)
+
+        loss = loss_1 + loss_2
+
+        return loss, acc
+
+    def forward_old(self, x, x_lens, y, y_lens, bert_feature):
+        """
+        x: phoneme_ids
+        y: semantic_ids
+        """
+        x = self.ar_text_embedding(x)
+        x = x + self.bert_proj(bert_feature.transpose(1, 2))
+        x = self.ar_text_position(x)
+        x_mask = make_pad_mask(x_lens)
+
+        y_mask = make_pad_mask(y_lens)
+        y_mask_int = y_mask.type(torch.int64)
+        codes = y.type(torch.int64) * (1 - y_mask_int)
+
+        # Training
+        # AR Decoder
+        y, targets = self.pad_y_eos(codes, y_mask_int, eos_id=self.EOS)
+        x_len = x_lens.max()
+        y_len = y_lens.max()
+        y_emb = self.ar_audio_embedding(y)
+        y_pos = self.ar_audio_position(y_emb)
+
+        xy_padding_mask = torch.concat([x_mask, y_mask], dim=1)
+        ar_xy_padding_mask = xy_padding_mask
+
+        x_attn_mask = F.pad(
+            torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device),
+            (0, y_len),
+            value=True,
+        )
+        y_attn_mask = F.pad(
+            torch.triu(
+                torch.ones(y_len, y_len, dtype=torch.bool, device=x.device),
+                diagonal=1,
+            ),
+            (x_len, 0),
+            value=False,
+        )
+        xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0)
+        bsz, src_len = x.shape[0], x_len + y_len
+        _xy_padding_mask = (
+            ar_xy_padding_mask.view(bsz, 1, 1, src_len).expand(-1, self.num_head, -1, -1).reshape(bsz * self.num_head, 1, src_len)
+        )
+        xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)
+        new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)
+        new_attn_mask.masked_fill_(xy_attn_mask, float("-inf"))
+        xy_attn_mask = new_attn_mask
+        # x 和完整的 y 一次性输入模型
+        xy_pos = torch.concat([x, y_pos], dim=1)
+        xy_dec, _ = self.h(
+            (xy_pos, None),
+            mask=xy_attn_mask,
+        )
+        logits = self.ar_predict_layer(xy_dec[:, x_len:]).permute(0, 2, 1)
+        # loss
+        # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum
+        loss = F.cross_entropy(logits, targets, reduction="sum")
+        acc = self.ar_accuracy_metric(logits.detach(), targets).item()
+        return loss, acc
+
+    # 需要看下这个函数和 forward 的区别以及没有 semantic 的时候 prompts 输入什么
+    def infer(
+        self,
+        x,
+        x_lens,
+        prompts,
+        bert_feature,
+        top_k: int = -100,
+        early_stop_num: int = -1,
+        temperature: float = 1.0,
+    ):
+        x = self.ar_text_embedding(x)
+        x = x + self.bert_proj(bert_feature.transpose(1, 2))
+        x = self.ar_text_position(x)
+
+        # AR Decoder
+        y = prompts
+        prefix_len = y.shape[1]
+        x_len = x.shape[1]
+        x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
+        stop = False
+        for _ in tqdm(range(1500)):
+            y_emb = self.ar_audio_embedding(y)
+            y_pos = self.ar_audio_position(y_emb)
+            # x 和逐渐增长的 y 一起输入给模型
+            xy_pos = torch.concat([x, y_pos], dim=1)
+            y_len = y.shape[1]
+            x_attn_mask_pad = F.pad(
+                x_attn_mask,
+                (0, y_len),
+                value=True,
+            )
+            y_attn_mask = F.pad(
+                torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
+                (x_len, 0),
+                value=False,
+            )
+            xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(y.device)
+
+            xy_dec, _ = self.h(
+                (xy_pos, None),
+                mask=xy_attn_mask,
+            )
+            logits = self.ar_predict_layer(xy_dec[:, -1])
+            samples = topk_sampling(logits, top_k=top_k, top_p=1.0, temperature=temperature)
+
+            if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
+                print("use early stop num:", early_stop_num)
+                stop = True
+
+            if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
+                # print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
+                stop = True
+            if stop:
+                if prompts.shape[1] == y.shape[1]:
+                    y = torch.concat([y, torch.zeros_like(samples)], dim=1)
+                    print("bad zero prediction")
+                print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
+                break
+            # 本次生成的 semantic_ids 和之前的 y 构成新的 y
+            # print(samples.shape)#[1,1]#第一个1是bs
+            # import os
+            # os._exit(2333)
+            y = torch.concat([y, samples], dim=1)
+        return y
+
+    def pad_y_eos(self, y, y_mask_int, eos_id):
+        targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad(y_mask_int, (0, 1), value=1)
+        # 错位
+        return targets[:, :-1], targets[:, 1:]
+
+    def infer_panel(
+        self,
+        x,  #####全部文本token
+        x_lens,
+        prompts,  ####参考音频token
+        bert_feature,
+        top_k: int = -100,
+        top_p: int = 100,
+        early_stop_num: int = -1,
+        temperature: float = 1.0,
+    ):
+        x = self.ar_text_embedding(x)
+        x = x + self.bert_proj(bert_feature.transpose(1, 2))
+        x = self.ar_text_position(x)
+
+        # AR Decoder
+        y = prompts
+
+        x_len = x.shape[1]
+        x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
+        stop = False
+        # print(1111111,self.num_layers)
+        cache = {
+            "all_stage": self.num_layers,
+            "k": [None] * self.num_layers,  ###根据配置自己手写
+            "v": [None] * self.num_layers,
+            # "xy_pos":None,##y_pos位置编码每次都不一样的没法缓存，每次都要重新拼xy_pos.主要还是写法原因，其实是可以历史统一一样的，但也没啥计算量就不管了
+            "y_emb": None,  ##只需要对最新的samples求emb，再拼历史的就行
+            # "logits":None,###原版就已经只对结尾求再拼接了，不用管
+            # "xy_dec":None,###不需要，本来只需要最后一个做logits
+            "first_infer": 1,
+            "stage": 0,
+        }
+        ###################  first step ##########################
+        if y is not None:
+            y_emb = self.ar_audio_embedding(y)
+            y_len = y_emb.shape[1]
+            prefix_len = y.shape[1]
+            y_pos = self.ar_audio_position(y_emb)
+            xy_pos = torch.concat([x, y_pos], dim=1)
+            cache["y_emb"] = y_emb
+            ref_free = False
+        else:
+            y_emb = None
+            y_len = 0
+            prefix_len = 0
+            y_pos = None
+            xy_pos = x
+            y = torch.zeros(x.shape[0], 0, dtype=torch.int, device=x.device)
+            ref_free = True
+
+        x_attn_mask_pad = F.pad(
+            x_attn_mask,
+            (0, y_len),  ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
+            value=True,
+        )
+        y_attn_mask = F.pad(  ###yy的右上1扩展到左边xy的0,(y,x+y)
+            torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
+            (x_len, 0),
+            value=False,
+        )
+        xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(x.device)
+
+        for idx in tqdm(range(1500)):
+            
+            xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache)
+            logits = self.ar_predict_layer(xy_dec[:, -1])  ##不用改，如果用了cache的默认就是只有一帧，取最后一帧一样的
+            # samples = topk_sampling(logits, top_k=top_k, top_p=1.0, temperature=temperature)
+            if idx == 0:  ###第一次跑不能EOS否则没有了
+                logits = logits[:, :-1]  ###刨除1024终止符号的概率
+            samples = sample(logits[0], y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature)[
+                0
+            ].unsqueeze(0)
+            # 本次生成的 semantic_ids 和之前的 y 构成新的 y
+            # print(samples.shape)#[1,1]#第一个1是bs
+            y = torch.concat([y, samples], dim=1)
+
+            if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
+                print("use early stop num:", early_stop_num)
+                stop = True
+
+            if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
+                # print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
+                stop = True
+            if stop:
+                # if prompts.shape[1] == y.shape[1]:
+                #     y = torch.concat([y, torch.zeros_like(samples)], dim=1)
+                #     print("bad zero prediction")
+                if y.shape[1] == 0:
+                    y = torch.concat([y, torch.zeros_like(samples)], dim=1)
+                    print("bad zero prediction")
+                print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
+                break
+
+            ####################### update next step ###################################
+            cache["first_infer"] = 0
+            if cache["y_emb"] is not None:
+                y_emb = torch.cat([cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], dim=1)
+                cache["y_emb"] = y_emb
+                y_pos = self.ar_audio_position(y_emb)
+                xy_pos = y_pos[:, -1:]
+            else:
+                y_emb = self.ar_audio_embedding(y[:, -1:])
+                cache["y_emb"] = y_emb
+                y_pos = self.ar_audio_position(y_emb)
+                xy_pos = y_pos
+            y_len = y_pos.shape[1]
+
+            ###最右边一列（是错的）
+            # xy_attn_mask=torch.ones((1, x_len+y_len), dtype=torch.bool,device=xy_pos.device)
+            # xy_attn_mask[:,-1]=False
+            ###最下面一行（是对的）
+            xy_attn_mask = torch.zeros((1, x_len + y_len), dtype=torch.bool, device=xy_pos.device)
+        if ref_free:
+            return y[:, :-1], 0
+        return y[:, :-1], idx - 1
diff --git a/utils/tts/gpt_sovits/AR/models/utils.py b/utils/tts/gpt_sovits/AR/models/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9678c7e13d81a6e885187c86d9d11e49cf707957
--- /dev/null
+++ b/utils/tts/gpt_sovits/AR/models/utils.py
@@ -0,0 +1,229 @@
+# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/utils.py
+# reference: https://github.com/lifeiteng/vall-e
+import torch
+import torch.nn.functional as F
+from typing import Tuple
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
+    """
+    Args:
+      lengths:
+        A 1-D tensor containing sentence lengths.
+      max_len:
+        The length of masks.
+    Returns:
+      Return a 2-D bool tensor, where masked positions
+      are filled with `True` and non-masked positions are
+      filled with `False`.
+
+    #>>> lengths = torch.tensor([1, 3, 2, 5])
+    #>>> make_pad_mask(lengths)
+    tensor([[False,  True,  True,  True,  True],
+            [False, False, False,  True,  True],
+            [False, False,  True,  True,  True],
+            [False, False, False, False, False]])
+    """
+    assert lengths.ndim == 1, lengths.ndim
+    max_len = max(max_len, lengths.max())
+    n = lengths.size(0)
+    seq_range = torch.arange(0, max_len, device=lengths.device)
+    expaned_lengths = seq_range.unsqueeze(0).expand(n, max_len)
+
+    return expaned_lengths >= lengths.unsqueeze(-1)
+
+
+# https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py
+def top_k_top_p_filtering(
+    logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1
+):
+    """Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
+    Args:
+        logits: logits distribution shape (batch size, vocabulary size)
+        if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
+        if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
+            Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
+        Make sure we keep at least min_tokens_to_keep per batch example in the output
+    From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
+    """
+    if top_k > 0:
+        top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1))  # Safety check
+        # Remove all tokens with a probability less than the last token of the top-k
+        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+        logits[indices_to_remove] = filter_value
+
+    if top_p < 1.0:
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+
+        # Remove tokens with cumulative probability above the threshold (token with 0 are kept)
+        sorted_indices_to_remove = cumulative_probs > top_p
+        if min_tokens_to_keep > 1:
+            # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
+            sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
+        # Shift the indices to the right to keep also the first token above the threshold
+        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+        sorted_indices_to_remove[..., 0] = 0
+
+        # scatter sorted tensors to original indexing
+        indices_to_remove = sorted_indices_to_remove.scatter(
+            1, sorted_indices, sorted_indices_to_remove
+        )
+        logits[indices_to_remove] = filter_value
+    return logits
+
+
+def topk_sampling(logits, top_k=10, top_p=1.0, temperature=1.0):
+    # temperature: (`optional`) float
+    #     The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.
+    # top_k: (`optional`) int
+    #     The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50.
+    # top_p: (`optional`) float
+    #     The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1.
+
+    # Temperature (higher temperature => more likely to sample low probability tokens)
+    if temperature != 1.0:
+        logits = logits / temperature
+    # Top-p/top-k filtering
+    logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
+    # Sample
+    token = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
+    return token
+
+
+from typing import Optional, Tuple
+
+
+def multinomial_sample_one_no_sync(
+    probs_sort,
+):  # Does multinomial sampling without a cuda synchronization
+    q = torch.empty_like(probs_sort).exponential_(1)
+    return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
+
+
+def logits_to_probs(
+    logits,
+    previous_tokens: Optional[torch.Tensor] = None,
+    temperature: float = 1.0,
+    top_k: Optional[int] = None,
+    top_p: Optional[int] = None,
+    repetition_penalty: float = 1.0,
+):
+    if previous_tokens is not None:
+        previous_tokens = previous_tokens.squeeze()
+    # print(logits.shape,previous_tokens.shape)
+    # pdb.set_trace()
+    if previous_tokens is not None and repetition_penalty != 1.0:
+        previous_tokens = previous_tokens.long()
+        score = torch.gather(logits, dim=0, index=previous_tokens)
+        score = torch.where(
+            score < 0, score * repetition_penalty, score / repetition_penalty
+        )
+        logits.scatter_(dim=0, index=previous_tokens, src=score)
+
+    if top_p is not None and top_p < 1.0:
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cum_probs = torch.cumsum(
+            torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1
+        )
+        sorted_indices_to_remove = cum_probs > top_p
+        sorted_indices_to_remove[0] = False  # keep at least one option
+        indices_to_remove = sorted_indices_to_remove.scatter(
+            dim=0, index=sorted_indices, src=sorted_indices_to_remove
+        )
+        logits = logits.masked_fill(indices_to_remove, -float("Inf"))
+
+    logits = logits / max(temperature, 1e-5)
+
+    if top_k is not None:
+        v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+        pivot = v.select(-1, -1).unsqueeze(-1)
+        logits = torch.where(logits < pivot, -float("Inf"), logits)
+
+    probs = torch.nn.functional.softmax(logits, dim=-1)
+    return probs
+
+
+def sample(
+    logits,
+    previous_tokens: Optional[torch.Tensor] = None,
+    **sampling_kwargs,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    probs = logits_to_probs(
+        logits=logits, previous_tokens=previous_tokens, **sampling_kwargs
+    )
+    idx_next = multinomial_sample_one_no_sync(probs)
+    return idx_next, probs
+
+def dpo_loss(policy_chosen_logps: torch.FloatTensor,
+             policy_rejected_logps: torch.FloatTensor,
+             reference_chosen_logps: torch.FloatTensor,
+             reference_rejected_logps: torch.FloatTensor,
+             beta: float,
+             reference_free: bool = False) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+    pi_logratios = policy_chosen_logps - policy_rejected_logps
+    ref_logratios = reference_chosen_logps - reference_rejected_logps
+
+    if reference_free:
+        ref_logratios = 0
+
+    logits = pi_logratios - ref_logratios
+
+    losses = -F.logsigmoid(beta * logits)
+    chosen_rewards = beta * (policy_chosen_logps - reference_chosen_logps).detach()
+    rejected_rewards = beta * (policy_rejected_logps - reference_rejected_logps).detach()
+
+    return losses.mean(), chosen_rewards, rejected_rewards
+
+def get_batch_logps(logits_target: torch.FloatTensor, logits_reject: torch.FloatTensor, labels_target: torch.LongTensor, labels_reject: torch.LongTensor, average_log_prob: bool = False) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
+
+    # dummy token; we'll ignore the losses on these tokens later
+
+    per_token_logps_target = torch.gather(logits_target.log_softmax(-1), dim=2, index=labels_target.unsqueeze(2)).squeeze(2)
+    per_token_logps_reject = torch.gather(logits_reject.log_softmax(-1), dim=2, index=labels_reject.unsqueeze(2)).squeeze(2)
+
+    return per_token_logps_target.sum(-1), per_token_logps_reject.sum(-1)
+
+def make_reject_y(y_o, y_lens):
+    def repeat_P(y):
+        range_idx, _ = torch.randint(0, len(y), size=(2,)).sort()
+        pre = y[:range_idx[0]]
+        shf = y[range_idx[1]:]
+        range_text = y[range_idx[0]:range_idx[1]]
+        new_y = torch.cat([pre, range_text, range_text, shf])
+        return new_y
+    def lost_P(y):
+        range_idx, _ = torch.randint(0, len(y), size=(2,)).sort()
+        pre = y[:range_idx[0]]
+        shf = y[range_idx[1]:]
+        range_text = y[range_idx[0]:range_idx[1]]
+        new_y = torch.cat([pre, shf])
+        return new_y
+    bs = len(y_lens)
+    reject_y = []
+    reject_y_lens = []
+    for b in range(bs):
+        process_item_idx = torch.randint(0, 1, size=(1, ))[0]
+        if process_item_idx == 0:
+            new_y = repeat_P(y_o[b])
+            reject_y.append(new_y)
+            reject_y_lens.append(len(new_y))
+        elif process_item_idx==1:
+            new_y = lost_P(y_o[b])
+            reject_y.append(new_y)
+            reject_y_lens.append(len(new_y))
+    max_length = max(reject_y_lens)
+    for b in range(bs):
+        pad_length = max_length - reject_y_lens[b]
+        reject_y[b] = torch.cat([reject_y[b], torch.zeros(pad_length, dtype=y_o.dtype, device=y_o.device)], dim=0)
+
+    reject_y = torch.stack(reject_y, dim = 0)
+    reject_y_lens = torch.tensor(reject_y_lens, device=y_lens.device)
+
+    return reject_y, reject_y_lens
diff --git a/utils/tts/gpt_sovits/AR/modules/__init__.py b/utils/tts/gpt_sovits/AR/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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new file mode 100644
index 0000000000000000000000000000000000000000..e4f3ff5762f3013b853d0f932c0ce6c93844800f
--- /dev/null
+++ b/utils/tts/gpt_sovits/AR/modules/activation.py
@@ -0,0 +1,389 @@
+# modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/activation.py
+from typing import Optional, Tuple
+
+import torch
+from torch import Tensor
+from torch.nn import Linear, Module
+from torch.nn import functional as F
+from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
+from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
+from torch.nn.parameter import Parameter
+
+from utils.tts.gpt_sovits.AR.modules.patched_mha_with_cache import \
+    multi_head_attention_forward_patched
+
+F.multi_head_attention_forward = multi_head_attention_forward_patched
+
+
+class MultiheadAttention(Module):
+    r"""Allows the model to jointly attend to information
+    from different representation subspaces as described in the paper:
+    `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_.
+
+    Multi-Head Attention is defined as:
+
+    .. math::
+        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
+
+    where :math:`head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)`.
+
+    ``forward()`` will use a special optimized implementation if all of the following
+    conditions are met:
+
+    - self attention is being computed (i.e., ``query``, ``key``, and ``value`` are the same tensor. This
+      restriction will be loosened in the future.)
+    - Either autograd is disabled (using ``torch.inference_mode`` or ``torch.no_grad``) or no tensor argument ``requires_grad``
+    - training is disabled (using ``.eval()``)
+    - dropout is 0
+    - ``add_bias_kv`` is ``False``
+    - ``add_zero_attn`` is ``False``
+    - ``batch_first`` is ``True`` and the input is batched
+    - ``kdim`` and ``vdim`` are equal to ``embed_dim``
+    - at most one of ``key_padding_mask`` or ``attn_mask`` is passed
+    - if a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ is passed, neither ``key_padding_mask``
+      nor ``attn_mask`` is passed
+
+    If the optimized implementation is in use, a
+    `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ can be passed for
+    ``query``/``key``/``value`` to represent padding more efficiently than using a
+    padding mask. In this case, a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_
+    will be returned, and an additional speedup proportional to the fraction of the input
+    that is padding can be expected.
+
+    Args:
+        embed_dim: Total dimension of the model.
+        num_heads: Number of parallel attention heads. Note that ``embed_dim`` will be split
+            across ``num_heads`` (i.e. each head will have dimension ``embed_dim // num_heads``).
+        dropout: Dropout probability on ``attn_output_weights``. Default: ``0.0`` (no dropout).
+        bias: If specified, adds bias to input / output projection layers. Default: ``True``.
+        add_bias_kv: If specified, adds bias to the key and value sequences at dim=0. Default: ``False``.
+        add_zero_attn: If specified, adds a new batch of zeros to the key and value sequences at dim=1.
+            Default: ``False``.
+        kdim: Total number of features for keys. Default: ``None`` (uses ``kdim=embed_dim``).
+        vdim: Total number of features for values. Default: ``None`` (uses ``vdim=embed_dim``).
+        batch_first: If ``True``, then the input and output tensors are provided
+            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
+        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
+
+    """
+
+    __constants__ = ["batch_first"]
+    bias_k: Optional[torch.Tensor]
+    bias_v: Optional[torch.Tensor]
+
+    def __init__(
+        self,
+        embed_dim,
+        num_heads,
+        dropout=0.0,
+        bias=True,
+        add_bias_kv=False,
+        add_zero_attn=False,
+        kdim=None,
+        vdim=None,
+        batch_first=False,
+        linear1_cls=Linear,
+        linear2_cls=Linear,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super(MultiheadAttention, self).__init__()
+        self.embed_dim = embed_dim
+        self.kdim = kdim if kdim is not None else embed_dim
+        self.vdim = vdim if vdim is not None else embed_dim
+        self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim
+
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.batch_first = batch_first
+        self.head_dim = embed_dim // num_heads
+        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+
+        if add_bias_kv:
+            self.bias_k = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs))
+            self.bias_v = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs))
+        else:
+            self.bias_k = self.bias_v = None
+
+        if linear1_cls == Linear:
+            if not self._qkv_same_embed_dim:
+                self.q_proj_weight = Parameter(torch.empty((embed_dim, embed_dim), **factory_kwargs))
+                self.k_proj_weight = Parameter(torch.empty((embed_dim, self.kdim), **factory_kwargs))
+                self.v_proj_weight = Parameter(torch.empty((embed_dim, self.vdim), **factory_kwargs))
+                self.register_parameter("in_proj_weight", None)
+            else:
+                self.in_proj_weight = Parameter(torch.empty((3 * embed_dim, embed_dim), **factory_kwargs))
+                self.register_parameter("q_proj_weight", None)
+                self.register_parameter("k_proj_weight", None)
+                self.register_parameter("v_proj_weight", None)
+
+            if bias:
+                self.in_proj_bias = Parameter(torch.empty(3 * embed_dim, **factory_kwargs))
+            else:
+                self.register_parameter("in_proj_bias", None)
+            self.out_proj = NonDynamicallyQuantizableLinear(embed_dim, embed_dim, bias=bias, **factory_kwargs)
+
+            self._reset_parameters()
+        else:
+            if not self._qkv_same_embed_dim:
+                raise NotImplementedError
+            else:
+                self.in_proj_linear = linear1_cls(embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs)
+                self.in_proj_weight = self.in_proj_linear.weight
+
+                self.register_parameter("q_proj_weight", None)
+                self.register_parameter("k_proj_weight", None)
+                self.register_parameter("v_proj_weight", None)
+
+                if bias:
+                    self.in_proj_bias = self.in_proj_linear.bias
+                else:
+                    self.register_parameter("in_proj_bias", None)
+
+            self.out_proj = linear2_cls(embed_dim, embed_dim, bias=bias, **factory_kwargs)
+
+            if self.bias_k is not None:
+                xavier_normal_(self.bias_k)
+            if self.bias_v is not None:
+                xavier_normal_(self.bias_v)
+
+        self.add_zero_attn = add_zero_attn
+
+    def _reset_parameters(self):
+        if self._qkv_same_embed_dim:
+            xavier_uniform_(self.in_proj_weight)
+        else:
+            xavier_uniform_(self.q_proj_weight)
+            xavier_uniform_(self.k_proj_weight)
+            xavier_uniform_(self.v_proj_weight)
+
+        if self.in_proj_bias is not None:
+            constant_(self.in_proj_bias, 0.0)
+            constant_(self.out_proj.bias, 0.0)
+
+        if self.bias_k is not None:
+            xavier_normal_(self.bias_k)
+        if self.bias_v is not None:
+            xavier_normal_(self.bias_v)
+
+    def __setstate__(self, state):
+        # Support loading old MultiheadAttention checkpoints generated by v1.1.0
+        if "_qkv_same_embed_dim" not in state:
+            state["_qkv_same_embed_dim"] = True
+
+        super(MultiheadAttention, self).__setstate__(state)
+
+    def forward(
+        self,
+        query: Tensor,
+        key: Tensor,
+        value: Tensor,
+        key_padding_mask: Optional[Tensor] = None,
+        need_weights: bool = True,
+        attn_mask: Optional[Tensor] = None,
+        average_attn_weights: bool = True,
+        cache=None,
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        r"""
+        Args:
+            query: Query embeddings of shape :math:`(L, E_q)` for unbatched input, :math:`(L, N, E_q)` when ``batch_first=False``
+                or :math:`(N, L, E_q)` when ``batch_first=True``, where :math:`L` is the target sequence length,
+                :math:`N` is the batch size, and :math:`E_q` is the query embedding dimension ``embed_dim``.
+                Queries are compared against key-value pairs to produce the output.
+                See "Attention Is All You Need" for more details.
+            key: Key embeddings of shape :math:`(S, E_k)` for unbatched input, :math:`(S, N, E_k)` when ``batch_first=False``
+                or :math:`(N, S, E_k)` when ``batch_first=True``, where :math:`S` is the source sequence length,
+                :math:`N` is the batch size, and :math:`E_k` is the key embedding dimension ``kdim``.
+                See "Attention Is All You Need" for more details.
+            value: Value embeddings of shape :math:`(S, E_v)` for unbatched input, :math:`(S, N, E_v)` when
+                ``batch_first=False`` or :math:`(N, S, E_v)` when ``batch_first=True``, where :math:`S` is the source
+                sequence length, :math:`N` is the batch size, and :math:`E_v` is the value embedding dimension ``vdim``.
+                See "Attention Is All You Need" for more details.
+            key_padding_mask: If specified, a mask of shape :math:`(N, S)` indicating which elements within ``key``
+                to ignore for the purpose of attention (i.e. treat as "padding"). For unbatched `query`, shape should be :math:`(S)`.
+                Binary and byte masks are supported.
+                For a binary mask, a ``True`` value indicates that the corresponding ``key`` value will be ignored for
+                the purpose of attention. For a float mask, it will be directly added to the corresponding ``key`` value.
+            need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``.
+                Default: ``True``.
+            attn_mask: If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape
+                :math:`(L, S)` or :math:`(N\cdot\text{num\_heads}, L, S)`, where :math:`N` is the batch size,
+                :math:`L` is the target sequence length, and :math:`S` is the source sequence length. A 2D mask will be
+                broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch.
+                Binary, byte, and float masks are supported. For a binary mask, a ``True`` value indicates that the
+                corresponding position is not allowed to attend. For a byte mask, a non-zero value indicates that the
+                corresponding position is not allowed to attend. For a float mask, the mask values will be added to
+                the attention weight.
+            average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across
+                heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an
+                effect when ``need_weights=True``. Default: ``True`` (i.e. average weights across heads)
+
+        Outputs:
+            - **attn_output** - Attention outputs of shape :math:`(L, E)` when input is unbatched,
+              :math:`(L, N, E)` when ``batch_first=False`` or :math:`(N, L, E)` when ``batch_first=True``,
+              where :math:`L` is the target sequence length, :math:`N` is the batch size, and :math:`E` is the
+              embedding dimension ``embed_dim``.
+            - **attn_output_weights** - Only returned when ``need_weights=True``. If ``average_attn_weights=True``,
+              returns attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or
+              :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and
+              :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
+              head of shape :math:`(\text{num\_heads}, L, S)` when input is unbatched or :math:`(N, \text{num\_heads}, L, S)`.
+
+            .. note::
+                `batch_first` argument is ignored for unbatched inputs.
+        """
+        is_batched = query.dim() == 3
+        if key_padding_mask is not None:
+            _kpm_dtype = key_padding_mask.dtype
+            if _kpm_dtype != torch.bool and not torch.is_floating_point(key_padding_mask):
+                raise AssertionError("only bool and floating types of key_padding_mask are supported")
+        why_not_fast_path = ""
+        if not is_batched:
+            why_not_fast_path = f"input not batched; expected query.dim() of 3 but got {query.dim()}"
+        elif query is not key or key is not value:
+            # When lifting this restriction, don't forget to either
+            # enforce that the dtypes all match or test cases where
+            # they don't!
+            why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)"
+        elif self.in_proj_bias is not None and query.dtype != self.in_proj_bias.dtype:
+            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match"
+        elif self.in_proj_weight is not None and query.dtype != self.in_proj_weight.dtype:
+            # this case will fail anyway, but at least they'll get a useful error message.
+            why_not_fast_path = (
+                f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match"
+            )
+        elif self.training:
+            why_not_fast_path = "training is enabled"
+        elif not self.batch_first:
+            why_not_fast_path = "batch_first was not True"
+        elif self.bias_k is not None:
+            why_not_fast_path = "self.bias_k was not None"
+        elif self.bias_v is not None:
+            why_not_fast_path = "self.bias_v was not None"
+        elif self.dropout:
+            why_not_fast_path = f"dropout was {self.dropout}, required zero"
+        elif self.add_zero_attn:
+            why_not_fast_path = "add_zero_attn was enabled"
+        elif not self._qkv_same_embed_dim:
+            why_not_fast_path = "_qkv_same_embed_dim was not True"
+        elif attn_mask is not None:
+            why_not_fast_path = "attn_mask was not None"
+        elif query.is_nested and key_padding_mask is not None:
+            why_not_fast_path = "key_padding_mask is not supported with NestedTensor input"
+        elif self.num_heads % 2 == 1:
+            why_not_fast_path = "num_heads is odd"
+        elif torch.is_autocast_enabled():
+            why_not_fast_path = "autocast is enabled"
+
+        if not why_not_fast_path:
+            tensor_args = (
+                query,
+                key,
+                value,
+                self.in_proj_weight,
+                self.in_proj_bias,
+                self.out_proj.weight,
+                self.out_proj.bias,
+            )
+            # We have to use list comprehensions below because TorchScript does not support
+            # generator expressions.
+            if torch.overrides.has_torch_function(tensor_args):
+                why_not_fast_path = "some Tensor argument has_torch_function"
+            elif not all([(x is None or x.is_cuda or "cpu" in str(x.device)) for x in tensor_args]):
+                why_not_fast_path = "some Tensor argument is neither CUDA nor CPU"
+            elif torch.is_grad_enabled() and any([x is not None and x.requires_grad for x in tensor_args]):
+                why_not_fast_path = (
+                    "grad is enabled and at least one of query or the " "input/output projection weights or biases requires_grad"
+                )
+            if not why_not_fast_path:
+                return torch._native_multi_head_attention(
+                    query,
+                    key,
+                    value,
+                    self.embed_dim,
+                    self.num_heads,
+                    self.in_proj_weight,
+                    self.in_proj_bias,
+                    self.out_proj.weight,
+                    self.out_proj.bias,
+                    key_padding_mask if key_padding_mask is not None else attn_mask,
+                    need_weights,
+                    average_attn_weights,
+                    1 if key_padding_mask is not None else 0 if attn_mask is not None else None,
+                )
+
+        any_nested = query.is_nested or key.is_nested or value.is_nested
+        assert not any_nested, (
+            "MultiheadAttention does not support NestedTensor outside of its fast path. "
+            + f"The fast path was not hit because {why_not_fast_path}"
+        )
+
+        if self.batch_first and is_batched:
+            # make sure that the transpose op does not affect the "is" property
+            if key is value:
+                if query is key:
+                    query = key = value = query.transpose(1, 0)
+                else:
+                    query, key = [x.transpose(1, 0) for x in (query, key)]
+                    value = key
+            else:
+                query, key, value = [x.transpose(1, 0) for x in (query, key, value)]
+
+        if not self._qkv_same_embed_dim:
+            attn_output, attn_output_weights = F.multi_head_attention_forward(
+                query,
+                key,
+                value,
+                self.embed_dim,
+                self.num_heads,
+                self.in_proj_weight,
+                self.in_proj_bias,
+                self.bias_k,
+                self.bias_v,
+                self.add_zero_attn,
+                self.dropout,
+                self.out_proj.weight,
+                self.out_proj.bias,
+                training=self.training,
+                key_padding_mask=key_padding_mask,
+                need_weights=need_weights,
+                attn_mask=attn_mask,
+                use_separate_proj_weight=True,
+                q_proj_weight=self.q_proj_weight,
+                k_proj_weight=self.k_proj_weight,
+                v_proj_weight=self.v_proj_weight,
+                average_attn_weights=average_attn_weights,
+                cache=cache,
+            )
+        else:
+            attn_output, attn_output_weights = F.multi_head_attention_forward(
+                query,
+                key,
+                value,
+                self.embed_dim,
+                self.num_heads,
+                self.in_proj_weight,
+                self.in_proj_bias,
+                self.bias_k,
+                self.bias_v,
+                self.add_zero_attn,
+                self.dropout,
+                self.out_proj.weight,
+                self.out_proj.bias,
+                training=self.training,
+                key_padding_mask=key_padding_mask,
+                need_weights=need_weights,
+                attn_mask=attn_mask,
+                average_attn_weights=average_attn_weights,
+                cache=cache,
+            )
+        if self.batch_first and is_batched:
+            return attn_output.transpose(1, 0), attn_output_weights
+        else:
+            return attn_output, attn_output_weights
diff --git a/utils/tts/gpt_sovits/AR/modules/embedding.py b/utils/tts/gpt_sovits/AR/modules/embedding.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a382f93d8e5c87d6d52354937494703dc81523b
--- /dev/null
+++ b/utils/tts/gpt_sovits/AR/modules/embedding.py
@@ -0,0 +1,81 @@
+# modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/embedding.py
+import math
+
+import torch
+from torch import nn
+
+
+class TokenEmbedding(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        vocab_size: int,
+        dropout: float = 0.0,
+    ):
+        super().__init__()
+
+        self.vocab_size = vocab_size
+        self.embedding_dim = embedding_dim
+
+        self.dropout = torch.nn.Dropout(p=dropout)
+        self.word_embeddings = nn.Embedding(self.vocab_size, self.embedding_dim)
+
+    @property
+    def weight(self) -> torch.Tensor:
+        return self.word_embeddings.weight
+
+    def embedding(self, index: int) -> torch.Tensor:
+        return self.word_embeddings.weight[index : index + 1]
+
+    def forward(self, x: torch.Tensor):
+        x = self.word_embeddings(x)
+        x = self.dropout(x)
+        return x
+
+
+class SinePositionalEmbedding(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        dropout: float = 0.0,
+        scale: bool = False,
+        alpha: bool = False,
+    ):
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.x_scale = math.sqrt(embedding_dim) if scale else 1.0
+        self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
+        self.dropout = torch.nn.Dropout(p=dropout)
+
+        self.reverse = False
+        self.pe = None
+        self.extend_pe(torch.tensor(0.0).expand(1, 4000))
+
+    def extend_pe(self, x):
+        """Reset the positional encodings."""
+        if self.pe is not None:
+            if self.pe.size(1) >= x.size(1):
+                if self.pe.dtype != x.dtype or self.pe.device != x.device:
+                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
+                return
+        pe = torch.zeros(x.size(1), self.embedding_dim)
+        if self.reverse:
+            position = torch.arange(
+                x.size(1) - 1, -1, -1.0, dtype=torch.float32
+            ).unsqueeze(1)
+        else:
+            position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, self.embedding_dim, 2, dtype=torch.float32)
+            * -(math.log(10000.0) / self.embedding_dim)
+        )
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.pe = pe.to(device=x.device, dtype=x.dtype).detach()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        self.extend_pe(x)
+        output = x.unsqueeze(-1) if x.ndim == 2 else x
+        output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)]
+        return self.dropout(output)
diff --git a/utils/tts/gpt_sovits/AR/modules/lr_schedulers.py b/utils/tts/gpt_sovits/AR/modules/lr_schedulers.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8867467381cd58e14ad09ac4434512bc46186fc
--- /dev/null
+++ b/utils/tts/gpt_sovits/AR/modules/lr_schedulers.py
@@ -0,0 +1,83 @@
+# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/modules/lr_schedulers.py
+# reference: https://github.com/lifeiteng/vall-e
+import math
+
+import torch
+from matplotlib import pyplot as plt
+from torch import nn
+from torch.optim import Adam
+
+
+class WarmupCosineLRSchedule(torch.optim.lr_scheduler._LRScheduler):
+    """
+    Implements Warmup learning rate schedule until 'warmup_steps', going from 'init_lr' to 'peak_lr' for multiple optimizers.
+    """
+
+    def __init__(
+        self,
+        optimizer,
+        init_lr,
+        peak_lr,
+        end_lr,
+        warmup_steps=10000,
+        total_steps=400000,
+        current_step=0,
+    ):
+        self.init_lr = init_lr
+        self.peak_lr = peak_lr
+        self.end_lr = end_lr
+        self.optimizer = optimizer
+        self._warmup_rate = (peak_lr - init_lr) / warmup_steps
+        self._decay_rate = (end_lr - peak_lr) / (total_steps - warmup_steps)
+        self._current_step = current_step
+        self.lr = init_lr
+        self.warmup_steps = warmup_steps
+        self.total_steps = total_steps
+        self._last_lr = [self.lr]
+
+    def set_lr(self, lr):
+        self._last_lr = [g["lr"] for g in self.optimizer.param_groups]
+        for g in self.optimizer.param_groups:
+            # g['lr'] = lr
+            g["lr"] = self.end_lr  ###锁定用线性
+
+    def step(self):
+        if self._current_step < self.warmup_steps:
+            lr = self.init_lr + self._warmup_rate * self._current_step
+
+        elif self._current_step > self.total_steps:
+            lr = self.end_lr
+
+        else:
+            decay_ratio = (self._current_step - self.warmup_steps) / (
+                self.total_steps - self.warmup_steps
+            )
+            if decay_ratio < 0.0 or decay_ratio > 1.0:
+                raise RuntimeError(
+                    "Decay ratio must be in [0.0, 1.0]. Fix LR scheduler settings."
+                )
+            coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
+            lr = self.end_lr + coeff * (self.peak_lr - self.end_lr)
+
+        self.lr = lr = self.end_lr = 0.002  ###锁定用线性###不听话，直接锁定！
+        self.set_lr(lr)
+        self.lr = lr
+        self._current_step += 1
+        return self.lr
+
+
+if __name__ == "__main__":
+    m = nn.Linear(10, 10)
+    opt = Adam(m.parameters(), lr=1e-4)
+    s = WarmupCosineLRSchedule(
+        opt, 1e-6, 2e-4, 1e-6, warmup_steps=2000, total_steps=20000, current_step=0
+    )
+    lrs = []
+    for i in range(25000):
+        s.step()
+        lrs.append(s.lr)
+        print(s.lr)
+
+    plt.plot(lrs)
+    plt.plot(range(0, 25000), lrs)
+    plt.show()
diff --git a/utils/tts/gpt_sovits/AR/modules/optim.py b/utils/tts/gpt_sovits/AR/modules/optim.py
new file mode 100644
index 0000000000000000000000000000000000000000..98785f05b2e3aa123ae22955d677b9f68efec256
--- /dev/null
+++ b/utils/tts/gpt_sovits/AR/modules/optim.py
@@ -0,0 +1,622 @@
+# Copyright      2022  Xiaomi Corp.        (authors: Daniel Povey)
+#
+# See ../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import contextlib
+import logging
+from collections import defaultdict
+from typing import List
+from typing import Tuple
+
+import torch
+from torch import Tensor
+from torch.optim import Optimizer
+
+
+class BatchedOptimizer(Optimizer):
+    """
+    This class adds to class Optimizer the capability to optimize parameters in batches:
+    it will stack the parameters and their grads for you so the optimizer can work
+    on tensors with an extra leading dimension.  This is intended for speed with GPUs,
+    as it reduces the number of kernels launched in the optimizer.
+
+    Args:
+      params:
+    """
+
+    def __init__(self, params, defaults):
+        super(BatchedOptimizer, self).__init__(params, defaults)
+
+    @contextlib.contextmanager
+    def batched_params(self, param_group, group_params_names):
+        """
+        This function returns (technically, yields) a list of
+          of tuples (p, state), where
+        p is a `fake` parameter that is stacked (over axis 0) from real parameters
+        that share the same shape, and its gradient is also stacked;
+        `state` is the state corresponding to this batch of parameters
+        (it will be physically located in the "state" for one of the real
+        parameters, the last one that has any particular shape and dtype).
+
+        This function is decorated as a context manager so that it can
+        write parameters back to their "real" locations.
+
+        The idea is, instead of doing:
+        <code>
+          for p in group["params"]:
+             state = self.state[p]
+             ...
+        </code>
+        you can do:
+        <code>
+          with self.batched_params(group["params"]) as batches:
+             for p, state, p_names in batches:
+                 ...
+        </code>
+
+        Args:
+          group: a parameter group, which is a list of parameters; should be
+                one of self.param_groups.
+          group_params_names: name for each parameter in group,
+                which is List[str].
+        """
+        batches = defaultdict(
+            list
+        )  # `batches` maps from tuple (dtype_as_str,*shape) to list of nn.Parameter
+        batches_names = defaultdict(
+            list
+        )  # `batches` maps from tuple (dtype_as_str,*shape) to list of str
+
+        assert len(param_group) == len(group_params_names)
+        for p, named_p in zip(param_group, group_params_names):
+            key = (str(p.dtype), *p.shape)
+            batches[key].append(p)
+            batches_names[key].append(named_p)
+
+        batches_names_keys = list(batches_names.keys())
+        sorted_idx = sorted(
+            range(len(batches_names)), key=lambda i: batches_names_keys[i])
+        batches_names = [
+            batches_names[batches_names_keys[idx]] for idx in sorted_idx
+        ]
+        batches = [batches[batches_names_keys[idx]] for idx in sorted_idx]
+
+        stacked_params_dict = dict()
+
+        # turn batches into a list, in deterministic order.
+        # tuples will contain tuples of (stacked_param, state, stacked_params_names),
+        # one for each batch in `batches`.
+        tuples = []
+
+        for batch, batch_names in zip(batches, batches_names):
+            p = batch[0]
+            # we arbitrarily store the state in the
+            # state corresponding to the 1st parameter in the
+            # group.  class Optimizer will take care of saving/loading state.
+            state = self.state[p]
+            p_stacked = torch.stack(batch)
+            grad = torch.stack([
+                torch.zeros_like(p) if p.grad is None else p.grad for p in batch
+            ])
+            p_stacked.grad = grad
+            stacked_params_dict[key] = p_stacked
+            tuples.append((p_stacked, state, batch_names))
+
+        yield tuples  # <-- calling code will do the actual optimization here!
+
+        for ((stacked_params, _state, _names), batch) in zip(tuples, batches):
+            for i, p in enumerate(batch):  # batch is list of Parameter
+                p.copy_(stacked_params[i])
+
+
+class ScaledAdam(BatchedOptimizer):
+    """
+     Implements 'Scaled Adam', a variant of Adam where we scale each parameter's update
+     proportional to the norm of that parameter; and also learn the scale of the parameter,
+     in log space, subject to upper and lower limits (as if we had factored each parameter as
+     param = underlying_param * log_scale.exp())
+
+
+     Args:
+          params:  The parameters or param_groups to optimize (like other Optimizer subclasses)
+              lr:  The learning rate.  We will typically use a learning rate schedule that starts
+                   at 0.03 and decreases over time, i.e. much higher than other common
+                   optimizers.
+     clipping_scale: (e.g. 2.0)
+                   A scale for gradient-clipping: if specified, the normalized gradients
+                   over the whole model will be clipped to have 2-norm equal to
+                   `clipping_scale` times the median 2-norm over the most recent period
+                   of `clipping_update_period` minibatches.  By "normalized gradients",
+                   we mean after multiplying by the rms parameter value for this tensor
+                   [for non-scalars]; this is appropriate because our update is scaled
+                   by this quantity.
+            betas: beta1,beta2 are momentum constants for regular momentum, and moving sum-sq grad.
+                   Must satisfy 0 < beta <= beta2 < 1.
+     scalar_lr_scale: A scaling factor on the learning rate, that we use to update the
+                   scale of each parameter tensor and scalar parameters of the mode..
+                   If each parameter were decomposed
+                   as p * p_scale.exp(), where (p**2).mean().sqrt() == 1.0, scalar_lr_scale
+                   would be a the scaling factor on the learning rate of p_scale.
+              eps:  A general-purpose epsilon to prevent division by zero
+    param_min_rms: Minimum root-mean-square value of parameter tensor, for purposes of
+                   learning the scale on the parameters (we'll constrain the rms of each non-scalar
+                   parameter tensor to be >= this value)
+    param_max_rms: Maximum root-mean-square value of parameter tensor, for purposes of
+                   learning the scale on the parameters (we'll constrain the rms of each non-scalar
+                   parameter tensor to be <= this value)
+       scalar_max: Maximum absolute value for scalar parameters (applicable if your
+                   model has any parameters with numel() == 1).
+    size_update_period: The periodicity, in steps, with which we update the size (scale)
+                   of the parameter tensor.  This is provided to save a little time
+                   in the update.
+     clipping_update_period: if clipping_scale is specified, this is the period
+    """
+
+    def __init__(
+            self,
+            params,
+            lr=3e-02,
+            clipping_scale=None,
+            betas=(0.9, 0.98),
+            scalar_lr_scale=0.1,
+            eps=1.0e-08,
+            param_min_rms=1.0e-05,
+            param_max_rms=3.0,
+            scalar_max=10.0,
+            size_update_period=4,
+            clipping_update_period=100,
+            parameters_names=None,
+            show_dominant_parameters=True, ):
+
+        assert parameters_names is not None, (
+            "Please prepare parameters_names,"
+            "which is a List[List[str]]. Each List[str] is for a group"
+            "and each str is for a parameter")
+        defaults = dict(
+            lr=lr,
+            clipping_scale=clipping_scale,
+            betas=betas,
+            scalar_lr_scale=scalar_lr_scale,
+            eps=eps,
+            param_min_rms=param_min_rms,
+            param_max_rms=param_max_rms,
+            scalar_max=scalar_max,
+            size_update_period=size_update_period,
+            clipping_update_period=clipping_update_period, )
+
+        super(ScaledAdam, self).__init__(params, defaults)
+        assert len(self.param_groups) == len(parameters_names)
+        self.parameters_names = parameters_names
+        self.show_dominant_parameters = show_dominant_parameters
+
+    def __setstate__(self, state):
+        super(ScaledAdam, self).__setstate__(state)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        batch = True
+
+        for group, group_params_names in zip(self.param_groups,
+                                             self.parameters_names):
+
+            with self.batched_params(group["params"],
+                                     group_params_names) as batches:
+
+                # batches is list of pairs (stacked_param, state).  stacked_param is like
+                # a regular parameter, and will have a .grad, but the 1st dim corresponds to
+                # a stacking dim, it is not a real dim.
+
+                if (len(batches[0][1]) ==
+                        0):  # if len(first state) == 0: not yet initialized
+                    clipping_scale = 1
+                else:
+                    clipping_scale = self._get_clipping_scale(group, batches)
+
+                for p, state, _ in batches:
+                    # Perform optimization step.
+                    # grad is not going to be None, we handled that when creating the batches.
+                    grad = p.grad
+                    if grad.is_sparse:
+                        raise RuntimeError(
+                            "ScaledAdam optimizer does not support sparse gradients"
+                        )
+                    # State initialization
+                    if len(state) == 0:
+                        self._init_state(group, p, state)
+
+                    self._step_one_batch(group, p, state, clipping_scale)
+
+        return loss
+
+    def _init_state(self, group: dict, p: Tensor, state: dict):
+        """
+        Initializes state dict for parameter 'p'.  Assumes that dim 0 of tensor p
+        is actually the batch dimension, corresponding to batched-together
+        parameters of a given shape.
+
+
+        Args:
+           group:   Dict to look up configuration values.
+               p: The parameter that we are initializing the state for
+           state: Dict from string to whatever state we are initializing
+        """
+        size_update_period = group["size_update_period"]
+
+        state["step"] = 0
+
+        kwargs = {"device": p.device, "dtype": p.dtype}
+
+        # 'delta' implements conventional momentum.  There are
+        # several different kinds of update going on, so rather than
+        # compute "exp_avg" like in Adam, we store and decay a
+        # parameter-change "delta", which combines all forms of
+        # update.  this is equivalent to how it's done in Adam,
+        # except for the first few steps.
+        state["delta"] = torch.zeros_like(
+            p, memory_format=torch.preserve_format)
+
+        batch_size = p.shape[0]
+        numel = p.numel() // batch_size
+        numel = p.numel()
+
+        if numel > 1:
+            # "param_rms" just periodically records the scalar root-mean-square value of
+            # the parameter tensor.
+            # it has a shape like (batch_size, 1, 1, 1, 1)
+            param_rms = (
+                (p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt())
+            state["param_rms"] = param_rms
+
+            state["scale_exp_avg_sq"] = torch.zeros_like(param_rms)
+            state["scale_grads"] = torch.zeros(size_update_period,
+                                               *param_rms.shape, **kwargs)
+
+        # exp_avg_sq is the weighted sum of scaled gradients. as in Adam.
+        state["exp_avg_sq"] = torch.zeros_like(
+            p, memory_format=torch.preserve_format)
+
+    def _get_clipping_scale(self,
+                            group: dict,
+                            tuples: List[Tuple[Tensor, dict, List[str]]]
+                            ) -> float:
+        """
+        Returns a scalar factor <= 1.0 that dictates gradient clipping, i.e. we will scale the gradients
+        by this amount before applying the rest of the update.
+
+        Args:
+           group: the parameter group, an item in self.param_groups
+           tuples: a list of tuples of (param, state, param_names)
+                where param is a batched set of parameters,
+                with a .grad (1st dim is batch dim)
+                and state is the state-dict where optimization parameters are kept.
+                param_names is a List[str] while each str is name for a parameter
+                in batched set of parameters "param".
+        """
+        assert len(tuples) >= 1
+        clipping_scale = group["clipping_scale"]
+        (first_p, first_state, _) = tuples[0]
+        step = first_state["step"]
+        if clipping_scale is None or step == 0:
+            # no clipping.  return early on step == 0 because the other
+            # parameters' state won't have been initialized yet.
+            return 1.0
+        clipping_update_period = group["clipping_update_period"]
+
+        tot_sumsq = torch.tensor(0.0, device=first_p.device)
+        for (p, state, param_names) in tuples:
+            grad = p.grad
+            if grad.is_sparse:
+                raise RuntimeError(
+                    "ScaledAdam optimizer does not support sparse gradients")
+            if p.numel() == p.shape[0]:  # a batch of scalars
+                tot_sumsq += (grad**2).sum()  # sum() to change shape [1] to []
+            else:
+                tot_sumsq += ((grad * state["param_rms"])**2).sum()
+
+        tot_norm = tot_sumsq.sqrt()
+        if "model_norms" not in first_state:
+            first_state["model_norms"] = torch.zeros(
+                clipping_update_period, device=p.device)
+        first_state["model_norms"][step % clipping_update_period] = tot_norm
+
+        if step % clipping_update_period == 0:
+            # Print some stats.
+            # We don't reach here if step == 0 because we would have returned
+            # above.
+            sorted_norms = first_state["model_norms"].sort()[0].to("cpu")
+            quartiles = []
+            for n in range(0, 5):
+                index = min(
+                    clipping_update_period - 1,
+                    (clipping_update_period // 4) * n, )
+                quartiles.append(sorted_norms[index].item())
+
+            median = quartiles[2]
+            threshold = clipping_scale * median
+            first_state["model_norm_threshold"] = threshold
+            percent_clipped = (first_state["num_clipped"] * 100.0 /
+                               clipping_update_period
+                               if "num_clipped" in first_state else 0.0)
+            first_state["num_clipped"] = 0
+            quartiles = " ".join(["%.3e" % x for x in quartiles])
+            logging.info(
+                f"Clipping_scale={clipping_scale}, grad-norm quartiles {quartiles}, "
+                f"threshold={threshold:.3e}, percent-clipped={percent_clipped:.1f}"
+            )
+
+        if step < clipping_update_period:
+            return 1.0  # We have not yet estimated a norm to clip to.
+        else:
+            try:
+                model_norm_threshold = first_state["model_norm_threshold"]
+            except KeyError:
+                logging.info(
+                    "Warning: model_norm_threshold not in state: possibly "
+                    "you changed config when restarting, adding clipping_scale option?"
+                )
+                return 1.0
+            ans = min(1.0, (model_norm_threshold / (tot_norm + 1.0e-20)).item())
+            if ans < 1.0:
+                first_state["num_clipped"] += 1
+            if ans < 0.1:
+                logging.warn(
+                    f"Scaling gradients by {ans}, model_norm_threshold={model_norm_threshold}"
+                )
+                if self.show_dominant_parameters:
+                    assert p.shape[0] == len(param_names)
+                    self._show_gradient_dominating_parameter(tuples, tot_sumsq)
+            return ans
+
+    def _show_gradient_dominating_parameter(
+            self, tuples: List[Tuple[Tensor, dict, List[str]]],
+            tot_sumsq: Tensor):
+        """
+        Show information of parameter wihch dominanting tot_sumsq.
+
+        Args:
+           tuples: a list of tuples of (param, state, param_names)
+                where param is a batched set of parameters,
+                with a .grad (1st dim is batch dim)
+                and state is the state-dict where optimization parameters are kept.
+                param_names is a List[str] while each str is name for a parameter
+                in batched set of parameters "param".
+            tot_sumsq: sumsq of all parameters. Though it's could be calculated
+                from tuples, we still pass it to save some time.
+        """
+        all_sumsq_orig = {}
+        for (p, state, batch_param_names) in tuples:
+            # p is a stacked batch parameters.
+            batch_grad = p.grad
+            if p.numel() == p.shape[0]:  # a batch of scalars
+                batch_sumsq_orig = batch_grad**2
+                # Dummpy values used by following `zip` statement.
+                batch_rms_orig = torch.ones(p.shape[0])
+            else:
+                batch_rms_orig = state["param_rms"]
+                batch_sumsq_orig = ((batch_grad * batch_rms_orig)**2).sum(
+                    dim=list(range(1, batch_grad.ndim)))
+
+            for name, sumsq_orig, rms, grad in zip(batch_param_names,
+                                                   batch_sumsq_orig,
+                                                   batch_rms_orig, batch_grad):
+
+                proportion_orig = sumsq_orig / tot_sumsq
+                all_sumsq_orig[name] = (proportion_orig, sumsq_orig, rms, grad)
+
+        assert torch.isclose(
+            sum([value[0] for value in all_sumsq_orig.values()]).cpu(),
+            torch.tensor(1.0), )
+        sorted_by_proportion = {
+            k: v
+            for k, v in sorted(
+                all_sumsq_orig.items(),
+                key=lambda item: item[1][0],
+                reverse=True, )
+        }
+        dominant_param_name = next(iter(sorted_by_proportion))
+        (dominant_proportion, dominant_sumsq, dominant_rms,
+         dominant_grad, ) = sorted_by_proportion[dominant_param_name]
+        logging.info(f"Parameter Dominanting tot_sumsq {dominant_param_name}"
+                     f" with proportion {dominant_proportion:.2f},"
+                     f" where dominant_sumsq=(grad_sumsq*orig_rms_sq)"
+                     f"={dominant_sumsq:.3e},"
+                     f" grad_sumsq = {(dominant_grad**2).sum():.3e},"
+                     f" orig_rms_sq={(dominant_rms**2).item():.3e}")
+
+    def _step_one_batch(self,
+                        group: dict,
+                        p: Tensor,
+                        state: dict,
+                        clipping_scale: float):
+        """
+        Do the step for one parameter, which is actually going to be a batch of
+        `real` parameters, with dim 0 as the batch dim.
+        Args:
+                  group:  dict to look up configuration values
+                    p: parameter to update (actually multiple parameters stacked together
+                       as a batch)
+                  state: state-dict for p, to look up the optimizer state
+        """
+        lr = group["lr"]
+        size_update_period = group["size_update_period"]
+        beta1 = group["betas"][0]
+
+        grad = p.grad
+        if clipping_scale != 1.0:
+            grad = grad * clipping_scale
+        step = state["step"]
+        delta = state["delta"]
+
+        delta.mul_(beta1)
+        batch_size = p.shape[0]
+        numel = p.numel() // batch_size
+        if numel > 1:
+            # Update the size/scale of p, and set param_rms
+            scale_grads = state["scale_grads"]
+            scale_grads[step % size_update_period] = (p * grad).sum(
+                dim=list(range(1, p.ndim)), keepdim=True)
+            if step % size_update_period == size_update_period - 1:
+                param_rms = state["param_rms"]  # shape: (batch_size, 1, 1, ..)
+                param_rms.copy_((p**2)
+                                .mean(dim=list(range(1, p.ndim)), keepdim=True)
+                                .sqrt())
+                if step > 0:
+                    # self._size_update() learns the overall scale on the
+                    # parameter, by shrinking or expanding it.
+                    self._size_update(group, scale_grads, p, state)
+
+        if numel == 1:
+            # For parameters with 1 element we just use regular Adam.
+            # Updates delta.
+            self._step_scalar(group, p, state)
+        else:
+            self._step(group, p, state)
+
+        state["step"] = step + 1
+
+    def _size_update(self,
+                     group: dict,
+                     scale_grads: Tensor,
+                     p: Tensor,
+                     state: dict) -> None:
+        """
+               Called only where p.numel() > 1, this updates the scale of the parameter.
+               If we imagine: p =  underlying_param * scale.exp(), and we are doing
+               gradient descent on underlying param and on scale, this function does the update
+               on `scale`.
+
+               Args:
+              group: dict to look up configuration values
+        scale_grads: a tensor of shape (size_update_period, batch_size, 1, 1,...) containing
+                      grads w.r.t. the scales.
+                  p:  The parameter to update
+               state: The state-dict of p
+        """
+
+        param_rms = state["param_rms"]
+        beta1, beta2 = group["betas"]
+        size_lr = group["lr"] * group["scalar_lr_scale"]
+        param_min_rms = group["param_min_rms"]
+        param_max_rms = group["param_max_rms"]
+        eps = group["eps"]
+        step = state["step"]
+        batch_size = p.shape[0]
+
+        size_update_period = scale_grads.shape[0]
+        # correct beta2 for the size update period: we will have
+        # faster decay at this level.
+        beta2_corr = beta2**size_update_period
+
+        scale_exp_avg_sq = state[
+            "scale_exp_avg_sq"]  # shape: (batch_size, 1, 1, ..)
+        scale_exp_avg_sq.mul_(beta2_corr).add_(
+            (scale_grads**2).mean(dim=0),  # mean over dim `size_update_period`
+            alpha=1 - beta2_corr, )  # shape is (batch_size, 1, 1, ...)
+
+        # The 1st time we reach here is when size_step == 1.
+        size_step = (step + 1) // size_update_period
+        bias_correction2 = 1 - beta2_corr**size_step
+        # we don't bother with bias_correction1; this will help prevent divergence
+        # at the start of training.
+
+        denom = scale_exp_avg_sq.sqrt() + eps
+
+        scale_step = (-size_lr * (bias_correction2**0.5) *
+                      scale_grads.sum(dim=0) / denom)
+
+        is_too_small = param_rms < param_min_rms
+        is_too_large = param_rms > param_max_rms
+
+        # when the param gets too small, just don't shrink it any further.
+        scale_step.masked_fill_(is_too_small, 0.0)
+        # when it gets too large, stop it from getting any larger.
+        scale_step.masked_fill_(is_too_large, -size_lr * size_update_period)
+        delta = state["delta"]
+        # the factor of (1-beta1) relates to momentum.
+        delta.add_(p * scale_step, alpha=(1 - beta1))
+
+    def _step(self, group: dict, p: Tensor, state: dict):
+        """
+        This function does the core update of self.step(), in the case where the members of
+        the batch have more than 1 element.
+
+        Args:
+            group: A dict which will be used to look up configuration values
+                p: The parameter to be updated
+             grad: The grad of p
+            state: The state-dict corresponding to parameter p
+
+        This function modifies p.
+        """
+        grad = p.grad
+        lr = group["lr"]
+        beta1, beta2 = group["betas"]
+        eps = group["eps"]
+        param_min_rms = group["param_min_rms"]
+        step = state["step"]
+
+        exp_avg_sq = state["exp_avg_sq"]
+        exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=(1 - beta2))
+
+        this_step = state["step"] - (state["zero_step"]
+                                     if "zero_step" in state else 0)
+        bias_correction2 = 1 - beta2**(this_step + 1)
+        if bias_correction2 < 0.99:
+            # note: not in-place.
+            exp_avg_sq = exp_avg_sq * (1.0 / bias_correction2)
+
+        denom = exp_avg_sq.sqrt()
+        denom += eps
+        grad = grad / denom
+
+        alpha = -lr * (1 - beta1) * state["param_rms"].clamp(min=param_min_rms)
+
+        delta = state["delta"]
+        delta.add_(grad * alpha)
+        p.add_(delta)
+
+    def _step_scalar(self, group: dict, p: Tensor, state: dict):
+        """
+        A simplified form of the core update for scalar tensors, where we cannot get a good
+        estimate of the parameter rms.
+        """
+        beta1, beta2 = group["betas"]
+        scalar_max = group["scalar_max"]
+        eps = group["eps"]
+        lr = group["lr"] * group["scalar_lr_scale"]
+        grad = p.grad
+
+        exp_avg_sq = state["exp_avg_sq"]  # shape: (batch_size,)
+        exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+
+        # bias_correction2 is like in Adam.  Don't bother with bias_correction1;
+        # slower update at the start will help stability anyway.
+        bias_correction2 = 1 - beta2**(state["step"] + 1)
+        denom = (exp_avg_sq / bias_correction2).sqrt() + eps
+
+        delta = state["delta"]
+        delta.add_(grad / denom, alpha=-lr * (1 - beta1))
+        p.clamp_(min=-scalar_max, max=scalar_max)
+        p.add_(delta)
diff --git a/utils/tts/gpt_sovits/AR/modules/patched_mha_with_cache.py b/utils/tts/gpt_sovits/AR/modules/patched_mha_with_cache.py
new file mode 100644
index 0000000000000000000000000000000000000000..7be241dadd378fc9312916f60433ba4b7aa7c764
--- /dev/null
+++ b/utils/tts/gpt_sovits/AR/modules/patched_mha_with_cache.py
@@ -0,0 +1,465 @@
+from torch.nn.functional import *
+from torch.nn.functional import (
+    _mha_shape_check,
+    _canonical_mask,
+    _none_or_dtype,
+    _in_projection_packed,
+)
+from torch.nn import functional as F
+import torch
+# Tensor = torch.Tensor
+# from typing import Callable, List, Optional, Tuple, Union
+
+
+def multi_head_attention_forward_patched(
+    query: Tensor,
+    key: Tensor,
+    value: Tensor,
+    embed_dim_to_check: int,
+    num_heads: int,
+    in_proj_weight: Optional[Tensor],
+    in_proj_bias: Optional[Tensor],
+    bias_k: Optional[Tensor],
+    bias_v: Optional[Tensor],
+    add_zero_attn: bool,
+    dropout_p: float,
+    out_proj_weight: Tensor,
+    out_proj_bias: Optional[Tensor],
+    training: bool = True,
+    key_padding_mask: Optional[Tensor] = None,
+    need_weights: bool = True,
+    attn_mask: Optional[Tensor] = None,
+    use_separate_proj_weight: bool = False,
+    q_proj_weight: Optional[Tensor] = None,
+    k_proj_weight: Optional[Tensor] = None,
+    v_proj_weight: Optional[Tensor] = None,
+    static_k: Optional[Tensor] = None,
+    static_v: Optional[Tensor] = None,
+    average_attn_weights: bool = True,
+    is_causal: bool = False,
+    cache=None,
+) -> Tuple[Tensor, Optional[Tensor]]:
+    r"""
+    Args:
+        query, key, value: map a query and a set of key-value pairs to an output.
+            See "Attention Is All You Need" for more details.
+        embed_dim_to_check: total dimension of the model.
+        num_heads: parallel attention heads.
+        in_proj_weight, in_proj_bias: input projection weight and bias.
+        bias_k, bias_v: bias of the key and value sequences to be added at dim=0.
+        add_zero_attn: add a new batch of zeros to the key and
+                       value sequences at dim=1.
+        dropout_p: probability of an element to be zeroed.
+        out_proj_weight, out_proj_bias: the output projection weight and bias.
+        training: apply dropout if is ``True``.
+        key_padding_mask: if provided, specified padding elements in the key will
+            be ignored by the attention. This is an binary mask. When the value is True,
+            the corresponding value on the attention layer will be filled with -inf.
+        need_weights: output attn_output_weights.
+            Default: `True`
+            Note: `needs_weight` defaults to `True`, but should be set to `False`
+            For best performance when attention weights are not nedeeded.
+            *Setting needs_weights to `True`
+            leads to a significant performance degradation.*
+        attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
+            the batches while a 3D mask allows to specify a different mask for the entries of each batch.
+        is_causal: If specified, applies a causal mask as attention mask, and ignores
+            attn_mask for computing scaled dot product attention.
+            Default: ``False``.
+            .. warning::
+                is_causal is provides a hint that the attn_mask is the
+                causal mask.Providing incorrect hints can result in
+                incorrect execution, including forward and backward
+                compatibility.
+        use_separate_proj_weight: the function accept the proj. weights for query, key,
+            and value in different forms. If false, in_proj_weight will be used, which is
+            a combination of q_proj_weight, k_proj_weight, v_proj_weight.
+        q_proj_weight, k_proj_weight, v_proj_weight, in_proj_bias: input projection weight and bias.
+        static_k, static_v: static key and value used for attention operators.
+        average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across heads.
+            Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an effect
+            when ``need_weights=True.``. Default: True
+
+
+    Shape:
+        Inputs:
+        - query: :math:`(L, E)` or :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
+          the embedding dimension.
+        - key: :math:`(S, E)` or :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
+          the embedding dimension.
+        - value: :math:`(S, E)` or :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
+          the embedding dimension.
+        - key_padding_mask: :math:`(S)` or :math:`(N, S)` where N is the batch size, S is the source sequence length.
+          If a FloatTensor is provided, it will be directly added to the value.
+          If a BoolTensor is provided, the positions with the
+          value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
+        - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
+          3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
+          S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked
+          positions. If a BoolTensor is provided, positions with ``True``
+          are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
+          is provided, it will be added to the attention weight.
+        - static_k: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,
+          N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.
+        - static_v: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,
+          N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.
+
+        Outputs:
+        - attn_output: :math:`(L, E)` or :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
+          E is the embedding dimension.
+        - attn_output_weights: Only returned when ``need_weights=True``. If ``average_attn_weights=True``, returns
+          attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or
+          :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and
+          :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
+          head of shape :math:`(num_heads, L, S)` when input is unbatched or :math:`(N, num_heads, L, S)`.
+    """
+    tens_ops = (
+        query,
+        key,
+        value,
+        in_proj_weight,
+        in_proj_bias,
+        bias_k,
+        bias_v,
+        out_proj_weight,
+        out_proj_bias,
+    )
+    if has_torch_function(tens_ops):
+        return handle_torch_function(
+            multi_head_attention_forward,
+            tens_ops,
+            query,
+            key,
+            value,
+            embed_dim_to_check,
+            num_heads,
+            in_proj_weight,
+            in_proj_bias,
+            bias_k,
+            bias_v,
+            add_zero_attn,
+            dropout_p,
+            out_proj_weight,
+            out_proj_bias,
+            training=training,
+            key_padding_mask=key_padding_mask,
+            need_weights=need_weights,
+            attn_mask=attn_mask,
+            is_causal=is_causal,
+            use_separate_proj_weight=use_separate_proj_weight,
+            q_proj_weight=q_proj_weight,
+            k_proj_weight=k_proj_weight,
+            v_proj_weight=v_proj_weight,
+            static_k=static_k,
+            static_v=static_v,
+            average_attn_weights=average_attn_weights,
+            cache=cache,
+        )
+
+    is_batched = _mha_shape_check(
+        query, key, value, key_padding_mask, attn_mask, num_heads
+    )
+
+    # For unbatched input, we unsqueeze at the expected batch-dim to pretend that the input
+    # is batched, run the computation and before returning squeeze the
+    # batch dimension so that the output doesn't carry this temporary batch dimension.
+    if not is_batched:
+        # unsqueeze if the input is unbatched
+        query = query.unsqueeze(1)
+        key = key.unsqueeze(1)
+        value = value.unsqueeze(1)
+        if key_padding_mask is not None:
+            key_padding_mask = key_padding_mask.unsqueeze(0)
+
+    # set up shape vars
+    tgt_len, bsz, embed_dim = query.shape
+    src_len, _, _ = key.shape
+
+    key_padding_mask = _canonical_mask(
+        mask=key_padding_mask,
+        mask_name="key_padding_mask",
+        other_type=_none_or_dtype(attn_mask),
+        other_name="attn_mask",
+        target_type=query.dtype,
+    )
+
+    if is_causal and attn_mask is None:
+        raise RuntimeError(
+            "Need attn_mask if specifying the is_causal hint. "
+            "You may use the Transformer module method "
+            "`generate_square_subsequent_mask` to create this mask."
+        )
+
+    if is_causal and key_padding_mask is None and not need_weights:
+        # when we have a kpm or need weights, we need attn_mask
+        # Otherwise, we use the is_causal hint go as is_causal
+        # indicator to SDPA.
+        attn_mask = None
+    else:
+        attn_mask = _canonical_mask(
+            mask=attn_mask,
+            mask_name="attn_mask",
+            other_type=None,
+            other_name="",
+            target_type=query.dtype,
+            check_other=False,
+        )
+
+        if key_padding_mask is not None:
+            # We have the attn_mask, and use that to merge kpm into it.
+            # Turn off use of is_causal hint, as the merged mask is no
+            # longer causal.
+            is_causal = False
+
+    assert (
+        embed_dim == embed_dim_to_check
+    ), f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}"
+    if isinstance(embed_dim, torch.Tensor):
+        # embed_dim can be a tensor when JIT tracing
+        head_dim = embed_dim.div(num_heads, rounding_mode="trunc")
+    else:
+        head_dim = embed_dim // num_heads
+    assert (
+        head_dim * num_heads == embed_dim
+    ), f"embed_dim {embed_dim} not divisible by num_heads {num_heads}"
+    if use_separate_proj_weight:
+        # allow MHA to have different embedding dimensions when separate projection weights are used
+        assert (
+            key.shape[:2] == value.shape[:2]
+        ), f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}"
+    else:
+        assert (
+            key.shape == value.shape
+        ), f"key shape {key.shape} does not match value shape {value.shape}"
+
+    #
+    # compute in-projection
+    #
+    if not use_separate_proj_weight:
+        assert (
+            in_proj_weight is not None
+        ), "use_separate_proj_weight is False but in_proj_weight is None"
+        q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias)
+    else:
+        assert (
+            q_proj_weight is not None
+        ), "use_separate_proj_weight is True but q_proj_weight is None"
+        assert (
+            k_proj_weight is not None
+        ), "use_separate_proj_weight is True but k_proj_weight is None"
+        assert (
+            v_proj_weight is not None
+        ), "use_separate_proj_weight is True but v_proj_weight is None"
+        if in_proj_bias is None:
+            b_q = b_k = b_v = None
+        else:
+            b_q, b_k, b_v = in_proj_bias.chunk(3)
+        q, k, v = _in_projection(
+            query,
+            key,
+            value,
+            q_proj_weight,
+            k_proj_weight,
+            v_proj_weight,
+            b_q,
+            b_k,
+            b_v,
+        )
+    if cache != None:
+        if cache["first_infer"] == 1:
+            cache["k"][cache["stage"]] = k
+            # print(0,cache["k"].shape)
+            cache["v"][cache["stage"]] = v
+        else:  ###12个layer每个都要留自己的cache_kv
+            # print(1,cache["k"].shape)
+            cache["k"][cache["stage"]] = torch.cat(
+                [cache["k"][cache["stage"]], k], 0
+            )  ##本来时序是1，但是proj的时候可能transpose了所以时序到0维了
+            cache["v"][cache["stage"]] = torch.cat([cache["v"][cache["stage"]], v], 0)
+            # print(2, cache["k"].shape)
+            src_len = cache["k"][cache["stage"]].shape[0]
+            k = cache["k"][cache["stage"]]
+            v = cache["v"][cache["stage"]]
+            # if attn_mask is not None:
+            #     attn_mask=attn_mask[-1:,]
+            # print(attn_mask.shape,attn_mask)
+        cache["stage"] = (cache["stage"] + 1) % cache["all_stage"]
+    # print(2333,cache)
+    # prep attention mask
+
+    attn_mask = _canonical_mask(
+        mask=attn_mask,
+        mask_name="attn_mask",
+        other_type=None,
+        other_name="",
+        target_type=q.dtype,
+        check_other=False,
+    )
+
+    if attn_mask is not None:
+        # ensure attn_mask's dim is 3
+        if attn_mask.dim() == 2:
+            correct_2d_size = (tgt_len, src_len)
+            if attn_mask.shape != correct_2d_size:
+                raise RuntimeError(
+                    f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}."
+                )
+            attn_mask = attn_mask.unsqueeze(0)
+        elif attn_mask.dim() == 3:
+            correct_3d_size = (bsz * num_heads, tgt_len, src_len)
+            if attn_mask.shape != correct_3d_size:
+                raise RuntimeError(
+                    f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}."
+                )
+        else:
+            raise RuntimeError(
+                f"attn_mask's dimension {attn_mask.dim()} is not supported"
+            )
+
+    # add bias along batch dimension (currently second)
+    if bias_k is not None and bias_v is not None:
+        assert static_k is None, "bias cannot be added to static key."
+        assert static_v is None, "bias cannot be added to static value."
+        k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
+        v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
+        if attn_mask is not None:
+            attn_mask = pad(attn_mask, (0, 1))
+        if key_padding_mask is not None:
+            key_padding_mask = pad(key_padding_mask, (0, 1))
+    else:
+        assert bias_k is None
+        assert bias_v is None
+
+    #
+    # reshape q, k, v for multihead attention and make em batch first
+    #
+    q = q.view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
+    if static_k is None:
+        k = k.view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
+    else:
+        # TODO finish disentangling control flow so we don't do in-projections when statics are passed
+        assert (
+            static_k.size(0) == bsz * num_heads
+        ), f"expecting static_k.size(0) of {bsz * num_heads}, but got {static_k.size(0)}"
+        assert (
+            static_k.size(2) == head_dim
+        ), f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}"
+        k = static_k
+    if static_v is None:
+        v = v.view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
+    else:
+        # TODO finish disentangling control flow so we don't do in-projections when statics are passed
+        assert (
+            static_v.size(0) == bsz * num_heads
+        ), f"expecting static_v.size(0) of {bsz * num_heads}, but got {static_v.size(0)}"
+        assert (
+            static_v.size(2) == head_dim
+        ), f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}"
+        v = static_v
+
+    # add zero attention along batch dimension (now first)
+    if add_zero_attn:
+        zero_attn_shape = (bsz * num_heads, 1, head_dim)
+        k = torch.cat(
+            [k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1
+        )
+        v = torch.cat(
+            [v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1
+        )
+        if attn_mask is not None:
+            attn_mask = pad(attn_mask, (0, 1))
+        if key_padding_mask is not None:
+            key_padding_mask = pad(key_padding_mask, (0, 1))
+
+    # update source sequence length after adjustments
+    src_len = k.size(1)
+
+    # merge key padding and attention masks
+    if key_padding_mask is not None:
+        assert key_padding_mask.shape == (
+            bsz,
+            src_len,
+        ), f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}"
+        key_padding_mask = (
+            key_padding_mask.view(bsz, 1, 1, src_len)
+            .expand(-1, num_heads, -1, -1)
+            .reshape(bsz * num_heads, 1, src_len)
+        )
+        if attn_mask is None:
+            attn_mask = key_padding_mask
+        else:
+            attn_mask = attn_mask + key_padding_mask
+
+    # adjust dropout probability
+    if not training:
+        dropout_p = 0.0
+
+    #
+    # (deep breath) calculate attention and out projection
+    #
+
+    if need_weights:
+        B, Nt, E = q.shape
+        q_scaled = q / math.sqrt(E)
+
+        assert not (
+            is_causal and attn_mask is None
+        ), "FIXME: is_causal not implemented for need_weights"
+
+        if attn_mask is not None:
+            attn_output_weights = torch.baddbmm(
+                attn_mask, q_scaled, k.transpose(-2, -1)
+            )
+        else:
+            attn_output_weights = torch.bmm(q_scaled, k.transpose(-2, -1))
+        attn_output_weights = softmax(attn_output_weights, dim=-1)
+        if dropout_p > 0.0:
+            attn_output_weights = dropout(attn_output_weights, p=dropout_p)
+
+        attn_output = torch.bmm(attn_output_weights, v)
+
+        attn_output = (
+            attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim)
+        )
+        attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
+        attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
+
+        # optionally average attention weights over heads
+        attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
+        if average_attn_weights:
+            attn_output_weights = attn_output_weights.mean(dim=1)
+
+        if not is_batched:
+            # squeeze the output if input was unbatched
+            attn_output = attn_output.squeeze(1)
+            attn_output_weights = attn_output_weights.squeeze(0)
+        return attn_output, attn_output_weights
+    else:
+        # attn_mask can be either (L,S) or (N*num_heads, L, S)
+        # if attn_mask's shape is (1, L, S) we need to unsqueeze to (1, 1, L, S)
+        # in order to match the input for SDPA of (N, num_heads, L, S)
+        if attn_mask is not None:
+            if attn_mask.size(0) == 1 and attn_mask.dim() == 3:
+                attn_mask = attn_mask.unsqueeze(0)
+            else:
+                attn_mask = attn_mask.view(bsz, num_heads, -1, src_len)
+
+        q = q.view(bsz, num_heads, tgt_len, head_dim)
+        k = k.view(bsz, num_heads, src_len, head_dim)
+        v = v.view(bsz, num_heads, src_len, head_dim)
+
+        # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
+        attn_output = scaled_dot_product_attention(
+            q, k, v, attn_mask, dropout_p, is_causal
+        )
+
+        attn_output = (
+            attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim)
+        )
+
+        attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
+        attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
+        if not is_batched:
+            # squeeze the output if input was unbatched
+            attn_output = attn_output.squeeze(1)
+        return attn_output, None
diff --git a/utils/tts/gpt_sovits/AR/modules/scaling.py b/utils/tts/gpt_sovits/AR/modules/scaling.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0de89269ae606d26a83faa9ff30bb0c82198006
--- /dev/null
+++ b/utils/tts/gpt_sovits/AR/modules/scaling.py
@@ -0,0 +1,318 @@
+import random
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+
+class DoubleSwishFunction(torch.autograd.Function):
+    """
+      double_swish(x) = x * torch.sigmoid(x-1)
+    This is a definition, originally motivated by its close numerical
+    similarity to swish(swish(x)), where swish(x) =  x * sigmoid(x).
+
+    Memory-efficient derivative computation:
+     double_swish(x) = x * s, where s(x) = torch.sigmoid(x-1)
+     double_swish'(x) = d/dx double_swish(x) =  x * s'(x) + x' * s(x) = x * s'(x) + s(x).
+     Now, s'(x) = s(x) * (1-s(x)).
+     double_swish'(x) =  x * s'(x) + s(x).
+                      =  x * s(x) * (1-s(x)) + s(x).
+                     = double_swish(x) * (1-s(x)) + s(x)
+     ... so we just need to remember s(x) but not x itself.
+    """
+
+    @staticmethod
+    def forward(ctx, x: Tensor) -> Tensor:
+        requires_grad = x.requires_grad
+        x_dtype = x.dtype
+        if x.dtype == torch.float16:
+            x = x.to(torch.float32)
+
+        s = torch.sigmoid(x - 1.0)
+        y = x * s
+
+        if requires_grad:
+            deriv = y * (1 - s) + s
+            # notes on derivative of x * sigmoid(x - 1):
+            # https://www.wolframalpha.com/input?i=d%2Fdx+%28x+*+sigmoid%28x-1%29%29
+            # min \simeq -0.043638.  Take floor as -0.043637 so it's a lower bund
+            # max \simeq 1.1990.   Take ceil to be 1.2 so it's an upper bound.
+            # the combination of "+ torch.rand_like(deriv)" and casting to torch.uint8 (which
+            # floors), should be expectation-preserving.
+            floor = -0.043637
+            ceil = 1.2
+            d_scaled = (deriv - floor) * (255.0 / (ceil - floor)) + torch.rand_like(
+                deriv
+            )
+            if __name__ == "__main__":
+                # for self-testing only.
+                assert d_scaled.min() >= 0.0
+                assert d_scaled.max() < 256.0
+            d_int = d_scaled.to(torch.uint8)
+            ctx.save_for_backward(d_int)
+        if x.dtype == torch.float16 or torch.is_autocast_enabled():
+            y = y.to(torch.float16)
+        return y
+
+    @staticmethod
+    def backward(ctx, y_grad: Tensor) -> Tensor:
+        (d,) = ctx.saved_tensors
+        # the same constants as used in forward pass.
+        floor = -0.043637
+        ceil = 1.2
+        d = d * ((ceil - floor) / 255.0) + floor
+        return y_grad * d
+
+
+class DoubleSwish(torch.nn.Module):
+    def forward(self, x: Tensor) -> Tensor:
+        """Return double-swish activation function which is an approximation to Swish(Swish(x)),
+        that we approximate closely with x * sigmoid(x-1).
+        """
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            return x * torch.sigmoid(x - 1.0)
+        return DoubleSwishFunction.apply(x)
+
+
+class ActivationBalancerFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        x: Tensor,
+        scale_factor: Tensor,
+        sign_factor: Optional[Tensor],
+        channel_dim: int,
+    ) -> Tensor:
+        if channel_dim < 0:
+            channel_dim += x.ndim
+        ctx.channel_dim = channel_dim
+        xgt0 = x > 0
+        if sign_factor is None:
+            ctx.save_for_backward(xgt0, scale_factor)
+        else:
+            ctx.save_for_backward(xgt0, scale_factor, sign_factor)
+        return x
+
+    @staticmethod
+    def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None]:
+        if len(ctx.saved_tensors) == 3:
+            xgt0, scale_factor, sign_factor = ctx.saved_tensors
+            for _ in range(ctx.channel_dim, x_grad.ndim - 1):
+                scale_factor = scale_factor.unsqueeze(-1)
+                sign_factor = sign_factor.unsqueeze(-1)
+            factor = sign_factor + scale_factor * (xgt0.to(x_grad.dtype) - 0.5)
+        else:
+            xgt0, scale_factor = ctx.saved_tensors
+            for _ in range(ctx.channel_dim, x_grad.ndim - 1):
+                scale_factor = scale_factor.unsqueeze(-1)
+            factor = scale_factor * (xgt0.to(x_grad.dtype) - 0.5)
+        neg_delta_grad = x_grad.abs() * factor
+        return (
+            x_grad - neg_delta_grad,
+            None,
+            None,
+            None,
+        )
+
+
+def _compute_scale_factor(
+    x: Tensor,
+    channel_dim: int,
+    min_abs: float,
+    max_abs: float,
+    gain_factor: float,
+    max_factor: float,
+) -> Tensor:
+    if channel_dim < 0:
+        channel_dim += x.ndim
+    sum_dims = [d for d in range(x.ndim) if d != channel_dim]
+    x_abs_mean = torch.mean(x.abs(), dim=sum_dims).to(torch.float32)
+
+    if min_abs == 0.0:
+        below_threshold = 0.0
+    else:
+        # below_threshold is 0 if x_abs_mean > min_abs, can be at most max_factor if
+        # x_abs)_mean , min_abs.
+        below_threshold = ((min_abs - x_abs_mean) * (gain_factor / min_abs)).clamp(
+            min=0, max=max_factor
+        )
+
+    above_threshold = ((x_abs_mean - max_abs) * (gain_factor / max_abs)).clamp(
+        min=0, max=max_factor
+    )
+
+    return below_threshold - above_threshold
+
+
+def _compute_sign_factor(
+    x: Tensor,
+    channel_dim: int,
+    min_positive: float,
+    max_positive: float,
+    gain_factor: float,
+    max_factor: float,
+) -> Tensor:
+    if channel_dim < 0:
+        channel_dim += x.ndim
+    sum_dims = [d for d in range(x.ndim) if d != channel_dim]
+    proportion_positive = torch.mean((x > 0).to(torch.float32), dim=sum_dims)
+    if min_positive == 0.0:
+        factor1 = 0.0
+    else:
+        # 0 if proportion_positive >= min_positive, else can be
+        # as large as max_factor.
+        factor1 = (
+            (min_positive - proportion_positive) * (gain_factor / min_positive)
+        ).clamp_(min=0, max=max_factor)
+
+    if max_positive == 1.0:
+        factor2 = 0.0
+    else:
+        # 0 if self.proportion_positive <= max_positive, else can be
+        # as large as -max_factor.
+        factor2 = (
+            (proportion_positive - max_positive) * (gain_factor / (1.0 - max_positive))
+        ).clamp_(min=0, max=max_factor)
+    sign_factor = factor1 - factor2
+    # require min_positive != 0 or max_positive != 1:
+    assert not isinstance(sign_factor, float)
+    return sign_factor
+
+
+class ActivationBalancer(torch.nn.Module):
+    """
+    Modifies the backpropped derivatives of a function to try to encourage, for
+    each channel, that it is positive at least a proportion `threshold` of the
+    time.  It does this by multiplying negative derivative values by up to
+    (1+max_factor), and positive derivative values by up to (1-max_factor),
+    interpolated from 1 at the threshold to those extremal values when none
+    of the inputs are positive.
+
+    Args:
+           num_channels: the number of channels
+           channel_dim: the dimension/axis corresponding to the channel, e.g.
+               -1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative.
+           min_positive: the minimum, per channel, of the proportion of the time
+               that (x > 0), below which we start to modify the derivatives.
+           max_positive: the maximum, per channel, of the proportion of the time
+               that (x > 0), above which we start to modify the derivatives.
+           max_factor: the maximum factor by which we modify the derivatives for
+              either the sign constraint or the magnitude constraint;
+              e.g. with max_factor=0.02, the the derivatives would be multiplied by
+              values in the range [0.98..1.02].
+           sign_gain_factor: determines the 'gain' with which we increase the
+              change in gradient once the constraints on min_positive and max_positive
+              are violated.
+           scale_gain_factor: determines the 'gain' with which we increase the
+              change in gradient once the constraints on min_abs and max_abs
+              are violated.
+           min_abs:  the minimum average-absolute-value difference from the mean
+               value per channel, which we allow, before we start to modify
+               the derivatives to prevent this.
+           max_abs:  the maximum average-absolute-value difference from the mean
+               value per channel, which we allow, before we start to modify
+               the derivatives to prevent this.
+          min_prob: determines the minimum probability with which we modify the
+             gradients for the {min,max}_positive and {min,max}_abs constraints,
+             on each forward().  This is done randomly to prevent all layers
+             from doing it at the same time.  Early in training we may use
+             higher probabilities than this; it will decay to this value.
+    """
+
+    def __init__(
+        self,
+        num_channels: int,
+        channel_dim: int,
+        min_positive: float = 0.05,
+        max_positive: float = 0.95,
+        max_factor: float = 0.04,
+        sign_gain_factor: float = 0.01,
+        scale_gain_factor: float = 0.02,
+        min_abs: float = 0.2,
+        max_abs: float = 100.0,
+        min_prob: float = 0.1,
+    ):
+        super(ActivationBalancer, self).__init__()
+        self.num_channels = num_channels
+        self.channel_dim = channel_dim
+        self.min_positive = min_positive
+        self.max_positive = max_positive
+        self.max_factor = max_factor
+        self.min_abs = min_abs
+        self.max_abs = max_abs
+        self.min_prob = min_prob
+        self.sign_gain_factor = sign_gain_factor
+        self.scale_gain_factor = scale_gain_factor
+
+        # count measures how many times the forward() function has been called.
+        # We occasionally sync this to a tensor called `count`, that exists to
+        # make sure it is synced to disk when we load and save the model.
+        self.cpu_count = 0
+        self.register_buffer("count", torch.tensor(0, dtype=torch.int64))
+
+    def forward(self, x: Tensor) -> Tensor:
+        if torch.jit.is_scripting() or not x.requires_grad or torch.jit.is_tracing():
+            return _no_op(x)
+
+        count = self.cpu_count
+        self.cpu_count += 1
+
+        if random.random() < 0.01:
+            # Occasionally sync self.cpu_count with self.count.
+            # count affects the decay of 'prob'.  don't do this on every iter,
+            # because syncing with the GPU is slow.
+            self.cpu_count = max(self.cpu_count, self.count.item())
+            self.count.fill_(self.cpu_count)
+
+        # the prob of doing some work exponentially decreases from 0.5 till it hits
+        # a floor at min_prob (==0.1, by default)
+        prob = max(self.min_prob, 0.5 ** (1 + (count / 4000.0)))
+
+        if random.random() < prob:
+            sign_gain_factor = 0.5
+            if self.min_positive != 0.0 or self.max_positive != 1.0:
+                sign_factor = _compute_sign_factor(
+                    x,
+                    self.channel_dim,
+                    self.min_positive,
+                    self.max_positive,
+                    gain_factor=self.sign_gain_factor / prob,
+                    max_factor=self.max_factor,
+                )
+            else:
+                sign_factor = None
+
+            scale_factor = _compute_scale_factor(
+                x.detach(),
+                self.channel_dim,
+                min_abs=self.min_abs,
+                max_abs=self.max_abs,
+                gain_factor=self.scale_gain_factor / prob,
+                max_factor=self.max_factor,
+            )
+            return ActivationBalancerFunction.apply(
+                x,
+                scale_factor,
+                sign_factor,
+                self.channel_dim,
+            )
+        else:
+            return _no_op(x)
+
+
+def BalancedDoubleSwish(
+    d_model, channel_dim=-1, max_abs=10.0, min_prob=0.25
+) -> nn.Sequential:
+    """
+    ActivationBalancer -> DoubleSwish
+    """
+    balancer = ActivationBalancer(
+        d_model, channel_dim=channel_dim, max_abs=max_abs, min_prob=min_prob
+    )
+    return nn.Sequential(
+        balancer,
+        DoubleSwish(),
+    )
diff --git a/utils/tts/gpt_sovits/AR/modules/transformer.py b/utils/tts/gpt_sovits/AR/modules/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..317342af8b798fd7d5a60a1e52fbddc8a99fb0fa
--- /dev/null
+++ b/utils/tts/gpt_sovits/AR/modules/transformer.py
@@ -0,0 +1,373 @@
+# modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/transformer.py
+import copy
+import numbers
+from functools import partial
+from typing import Any, Callable, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+
+from utils.tts.gpt_sovits.AR.modules.activation import MultiheadAttention
+from utils.tts.gpt_sovits.AR.modules.scaling import BalancedDoubleSwish
+
+_shape_t = Union[int, List[int], torch.Size]
+
+
+class LayerNorm(nn.Module):
+    __constants__ = ["normalized_shape", "eps", "elementwise_affine"]
+    normalized_shape: Tuple[int, ...]
+    eps: float
+    elementwise_affine: bool
+
+    def __init__(
+        self,
+        normalized_shape: _shape_t,
+        eps: float = 1e-5,
+        elementwise_affine: bool = True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super(LayerNorm, self).__init__()
+        if isinstance(normalized_shape, numbers.Integral):
+            # mypy error: incompatible types in assignment
+            normalized_shape = (normalized_shape,)  # type: ignore[assignment]
+        self.normalized_shape = tuple(normalized_shape)  # type: ignore[arg-type]
+        self.eps = eps
+        self.elementwise_affine = elementwise_affine
+        if self.elementwise_affine:
+            self.weight = nn.Parameter(
+                torch.empty(self.normalized_shape, **factory_kwargs)
+            )
+            self.bias = nn.Parameter(
+                torch.empty(self.normalized_shape, **factory_kwargs)
+            )
+        else:
+            self.register_parameter("weight", None)
+            self.register_parameter("bias", None)
+
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        if self.elementwise_affine:
+            nn.init.ones_(self.weight)
+            nn.init.zeros_(self.bias)
+
+    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
+        if isinstance(input, tuple):
+            input, embedding = input
+            return (
+                F.layer_norm(
+                    input,
+                    self.normalized_shape,
+                    self.weight,
+                    self.bias,
+                    self.eps,
+                ),
+                embedding,
+            )
+
+        assert embedding is None
+        return F.layer_norm(
+            input, self.normalized_shape, self.weight, self.bias, self.eps
+        )
+
+    def extra_repr(self) -> str:
+        return (
+            "{normalized_shape}, eps={eps}, "
+            "elementwise_affine={elementwise_affine}".format(**self.__dict__)
+        )
+
+
+class IdentityNorm(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        eps: float = 1e-5,
+        device=None,
+        dtype=None,
+    ) -> None:
+        super(IdentityNorm, self).__init__()
+
+    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
+        if isinstance(input, tuple):
+            return input
+
+        assert embedding is None
+        return input
+
+
+class TransformerEncoder(nn.Module):
+    r"""TransformerEncoder is a stack of N encoder layers. Users can build the
+    BERT(https://arxiv.org/abs/1810.04805) model with corresponding parameters.
+
+    Args:
+        encoder_layer: an instance of the TransformerEncoderLayer() class (required).
+        num_layers: the number of sub-encoder-layers in the encoder (required).
+        norm: the layer normalization component (optional).
+        enable_nested_tensor: if True, input will automatically convert to nested tensor
+            (and convert back on output). This will improve the overall performance of
+            TransformerEncoder when padding rate is high. Default: ``True`` (enabled).
+
+    Examples::
+        >>> encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8)
+        >>> transformer_encoder = TransformerEncoder(encoder_layer, num_layers=6)
+        >>> src = torch.rand(10, 32, 512)
+        >>> out = transformer_encoder(src)
+    """
+    __constants__ = ["norm"]
+
+    def __init__(self, encoder_layer, num_layers, norm=None):
+        super(TransformerEncoder, self).__init__()
+        self.layers = _get_clones(encoder_layer, num_layers)
+        self.num_layers = num_layers
+        self.norm = norm
+
+    def forward(
+        self,
+        src: Tensor,
+        mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        return_layer_states: bool = False,
+        cache=None,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layers in turn.
+
+        Args:
+            src: the sequence to the encoder (required).
+            mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+            return_layer_states: return layers' state (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        if return_layer_states:
+            layer_states = []  # layers' output
+            output = src
+            for mod in self.layers:
+                output = mod(
+                    output,
+                    src_mask=mask,
+                    src_key_padding_mask=src_key_padding_mask,
+                    cache=cache,
+                )
+                layer_states.append(output[0])
+
+            if self.norm is not None:
+                output = self.norm(output)
+
+            return layer_states, output
+
+        output = src
+        for mod in self.layers:
+            output = mod(
+                output,
+                src_mask=mask,
+                src_key_padding_mask=src_key_padding_mask,
+                cache=cache,
+            )
+
+        if self.norm is not None:
+            output = self.norm(output)
+
+        return output
+
+
+class TransformerEncoderLayer(nn.Module):
+    __constants__ = ["batch_first", "norm_first"]
+
+    def __init__(
+        self,
+        d_model: int,
+        nhead: int,
+        dim_feedforward: int = 2048,
+        dropout: float = 0.1,
+        activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
+        batch_first: bool = False,
+        norm_first: bool = False,
+        device=None,
+        dtype=None,
+        linear1_self_attention_cls: nn.Module = nn.Linear,
+        linear2_self_attention_cls: nn.Module = nn.Linear,
+        linear1_feedforward_cls: nn.Module = nn.Linear,
+        linear2_feedforward_cls: nn.Module = nn.Linear,
+        layer_norm_cls: nn.Module = LayerNorm,
+        layer_norm_eps: float = 1e-5,
+        adaptive_layer_norm=False,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super(TransformerEncoderLayer, self).__init__()
+        # print(233333333333,d_model,nhead)
+        # import os
+        # os._exit(2333333)
+        self.self_attn = MultiheadAttention(
+            d_model,  # 512 16
+            nhead,
+            dropout=dropout,
+            batch_first=batch_first,
+            linear1_cls=linear1_self_attention_cls,
+            linear2_cls=linear2_self_attention_cls,
+            **factory_kwargs,
+        )
+
+        # Implementation of Feedforward model
+        self.linear1 = linear1_feedforward_cls(
+            d_model, dim_feedforward, **factory_kwargs
+        )
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = linear2_feedforward_cls(
+            dim_feedforward, d_model, **factory_kwargs
+        )
+
+        self.norm_first = norm_first
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+
+        # Legacy string support for activation function.
+        if isinstance(activation, str):
+            activation = _get_activation_fn(activation)
+        elif isinstance(activation, partial):
+            activation = activation(d_model)
+        elif activation == BalancedDoubleSwish:
+            activation = BalancedDoubleSwish(d_model)
+
+        # # We can't test self.activation in forward() in TorchScript,
+        # # so stash some information about it instead.
+        # if activation is F.relu or isinstance(activation, torch.nn.ReLU):
+        #     self.activation_relu_or_gelu = 1
+        # elif activation is F.gelu or isinstance(activation, torch.nn.GELU):
+        #     self.activation_relu_or_gelu = 2
+        # else:
+        #     self.activation_relu_or_gelu = 0
+        self.activation = activation
+
+        norm1 = layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)
+        if layer_norm_cls == IdentityNorm:
+            norm2 = BalancedBasicNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
+        else:
+            norm2 = layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)
+
+        if adaptive_layer_norm:
+            self.norm1 = AdaptiveLayerNorm(d_model, norm1)
+            self.norm2 = AdaptiveLayerNorm(d_model, norm2)
+        else:
+            self.norm1 = norm1
+            self.norm2 = norm2
+
+    def __setstate__(self, state):
+        super(TransformerEncoderLayer, self).__setstate__(state)
+        if not hasattr(self, "activation"):
+            self.activation = F.relu
+
+    def forward(
+        self,
+        src: Tensor,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        cache=None,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layer.
+
+        Args:
+            src: the sequence to the encoder layer (required).
+            src_mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        x, stage_embedding = src, None
+        is_src_tuple = False
+        if isinstance(src, tuple):
+            x, stage_embedding = src
+            is_src_tuple = True
+
+        if src_key_padding_mask is not None:
+            _skpm_dtype = src_key_padding_mask.dtype
+            if _skpm_dtype != torch.bool and not torch.is_floating_point(
+                src_key_padding_mask
+            ):
+                raise AssertionError(
+                    "only bool and floating types of key_padding_mask are supported"
+                )
+
+        if self.norm_first:
+            x = x + self._sa_block(
+                self.norm1(x, stage_embedding),
+                src_mask,
+                src_key_padding_mask,
+                cache=cache,
+            )
+            x = x + self._ff_block(self.norm2(x, stage_embedding))
+        else:
+            x = self.norm1(
+                x + self._sa_block(x, src_mask, src_key_padding_mask, cache=cache),
+                stage_embedding,
+            )
+            x = self.norm2(x + self._ff_block(x), stage_embedding)
+
+        if is_src_tuple:
+            return (x, stage_embedding)
+        return x
+
+    # self-attention block
+    def _sa_block(
+        self,
+        x: Tensor,
+        attn_mask: Optional[Tensor],
+        key_padding_mask: Optional[Tensor],
+        cache=None,
+    ) -> Tensor:
+        # print(x.shape,attn_mask.shape,key_padding_mask)
+        # torch.Size([1, 188, 512]) torch.Size([188, 188]) None
+        # import os
+        # os._exit(23333)
+        x = self.self_attn(
+            x,
+            x,
+            x,
+            attn_mask=attn_mask,
+            key_padding_mask=key_padding_mask,
+            need_weights=False,
+            cache=cache,
+        )[0]
+        return self.dropout1(x)
+
+    # feed forward block
+    def _ff_block(self, x: Tensor) -> Tensor:
+        x = self.linear2(self.dropout(self.activation(self.linear1(x))))
+        return self.dropout2(x)
+
+
+class AdaptiveLayerNorm(nn.Module):
+    r"""Adaptive Layer Normalization"""
+
+    def __init__(self, d_model, norm) -> None:
+        super(AdaptiveLayerNorm, self).__init__()
+        self.project_layer = nn.Linear(d_model, 2 * d_model)
+        self.norm = norm
+        self.d_model = d_model
+        self.eps = self.norm.eps
+
+    def forward(self, input: Tensor, embedding: Tensor = None) -> Tensor:
+        if isinstance(input, tuple):
+            input, embedding = input
+            weight, bias = torch.split(
+                self.project_layer(embedding),
+                split_size_or_sections=self.d_model,
+                dim=-1,
+            )
+            return (weight * self.norm(input) + bias, embedding)
+
+        weight, bias = torch.split(
+            self.project_layer(embedding),
+            split_size_or_sections=self.d_model,
+            dim=-1,
+        )
+        return weight * self.norm(input) + bias
+
+
+def _get_clones(module, N):
+    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
diff --git a/utils/tts/gpt_sovits/AR/utils/__init__.py b/utils/tts/gpt_sovits/AR/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2eaf61adcfee96d6e7ec8fd70a7603e18afb567
--- /dev/null
+++ b/utils/tts/gpt_sovits/AR/utils/__init__.py
@@ -0,0 +1,37 @@
+import re
+
+
+def str2bool(str):
+    return True if str.lower() == 'true' else False
+
+
+def get_newest_ckpt(string_list):
+    # 定义一个正则表达式模式，用于匹配字符串中的数字
+    pattern = r'epoch=(\d+)-step=(\d+)\.ckpt'
+
+    # 使用正则表达式提取每个字符串中的数字信息，并创建一个包含元组的列表
+    extracted_info = []
+    for string in string_list:
+        match = re.match(pattern, string)
+        if match:
+            epoch = int(match.group(1))
+            step = int(match.group(2))
+            extracted_info.append((epoch, step, string))
+    # 按照 epoch 后面的数字和 step 后面的数字进行排序
+    sorted_info = sorted(
+        extracted_info, key=lambda x: (x[0], x[1]), reverse=True)
+    # 获取最新的 ckpt 文件名
+    newest_ckpt = sorted_info[0][2]
+    return newest_ckpt
+
+
+# 文本存在且不为空时 return True
+def check_txt_file(file_path):
+    try:
+        with open(file_path, 'r') as file:
+            text = file.readline().strip()
+        assert text.strip() != ''
+        return text
+    except Exception:
+        return False
+    return False
diff --git a/utils/tts/gpt_sovits/AR/utils/initialize.py b/utils/tts/gpt_sovits/AR/utils/initialize.py
new file mode 100644
index 0000000000000000000000000000000000000000..17ff9f92e51c8941973139d6e34d4a1c7cd8daaa
--- /dev/null
+++ b/utils/tts/gpt_sovits/AR/utils/initialize.py
@@ -0,0 +1,38 @@
+#!/usr/bin/env python3
+"""Initialize modules for espnet2 neural networks."""
+import torch
+from typeguard import check_argument_types
+
+
+def initialize(model: torch.nn.Module, init: str):
+    """Initialize weights of a neural network module.
+
+    Parameters are initialized using the given method or distribution.
+
+    Custom initialization routines can be implemented into submodules
+    as function `espnet_initialization_fn` within the custom module.
+
+    Args:
+        model: Target.
+        init: Method of initialization.
+    """
+    assert check_argument_types()
+    print("init with", init)
+
+    # weight init
+    for p in model.parameters():
+        if p.dim() > 1:
+            if init == "xavier_uniform":
+                torch.nn.init.xavier_uniform_(p.data)
+            elif init == "xavier_normal":
+                torch.nn.init.xavier_normal_(p.data)
+            elif init == "kaiming_uniform":
+                torch.nn.init.kaiming_uniform_(p.data, nonlinearity="relu")
+            elif init == "kaiming_normal":
+                torch.nn.init.kaiming_normal_(p.data, nonlinearity="relu")
+            else:
+                raise ValueError("Unknown initialization: " + init)
+    # bias init
+    for name, p in model.named_parameters():
+        if ".bias" in name and p.dim() == 1:
+            p.data.zero_()
diff --git a/utils/tts/gpt_sovits/AR/utils/io.py b/utils/tts/gpt_sovits/AR/utils/io.py
new file mode 100644
index 0000000000000000000000000000000000000000..52f1f3c991506a9ea5d3fdbc71ded61e914694f1
--- /dev/null
+++ b/utils/tts/gpt_sovits/AR/utils/io.py
@@ -0,0 +1,34 @@
+import sys
+
+import torch
+import yaml
+
+
+def load_yaml_config(path):
+    with open(path) as f:
+        config = yaml.full_load(f)
+    return config
+
+
+def save_config_to_yaml(config, path):
+    assert path.endswith(".yaml")
+    with open(path, "w") as f:
+        f.write(yaml.dump(config))
+        f.close()
+
+
+def write_args(args, path):
+    args_dict = dict(
+        (name, getattr(args, name)) for name in dir(args) if not name.startswith("_")
+    )
+    with open(path, "a") as args_file:
+        args_file.write("==> torch version: {}\n".format(torch.__version__))
+        args_file.write(
+            "==> cudnn version: {}\n".format(torch.backends.cudnn.version())
+        )
+        args_file.write("==> Cmd:\n")
+        args_file.write(str(sys.argv))
+        args_file.write("\n==> args:\n")
+        for k, v in sorted(args_dict.items()):
+            args_file.write("  %s: %s\n" % (str(k), str(v)))
+        args_file.close()
diff --git a/utils/tts/gpt_sovits/__pycache__/inference_gpt_sovits.cpython-310.pyc b/utils/tts/gpt_sovits/__pycache__/inference_gpt_sovits.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5eef45a6c058b317d26b68620f1d2b7a4291522e
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diff --git a/utils/tts/gpt_sovits/__pycache__/utils.cpython-310.pyc b/utils/tts/gpt_sovits/__pycache__/utils.cpython-310.pyc
new file mode 100644
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diff --git a/utils/tts/gpt_sovits/inference_gpt_sovits.py b/utils/tts/gpt_sovits/inference_gpt_sovits.py
new file mode 100644
index 0000000000000000000000000000000000000000..0bb0334f59273712952fd15c237e369734fda171
--- /dev/null
+++ b/utils/tts/gpt_sovits/inference_gpt_sovits.py
@@ -0,0 +1,690 @@
+"""
+TTS 
+https://github.com/RVC-Boss/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
+"""
+
+import os
+import re
+import shutil
+import time
+from dataclasses import dataclass
+from io import BytesIO
+from pathlib import Path
+
+import LangSegment
+import librosa
+import numpy as np
+import soundfile as sf
+import streamlit as st
+import torch
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+from transformers.models.bert.modeling_bert import BertForMaskedLM
+from transformers.models.bert.tokenization_bert_fast import BertTokenizerFast
+
+from utils import HParams
+from utils.tts.gpt_sovits.AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from utils.tts.gpt_sovits.module import cnhubert
+from utils.tts.gpt_sovits.module.cnhubert import CNHubert
+from utils.tts.gpt_sovits.module.mel_processing import spectrogram_torch
+from utils.tts.gpt_sovits.module.models import SynthesizerTrn
+from utils.tts.gpt_sovits.text import cleaned_text_to_sequence
+from utils.tts.gpt_sovits.text.cleaner import clean_text
+from utils.tts.gpt_sovits.utils import load_audio
+from utils.web_configs import WEB_CONFIGS
+
+symbol_splits = {
+    "，",
+    "。",
+    "？",
+    "！",
+    ",",
+    ".",
+    "?",
+    "!",
+    "~",
+    ":",
+    "：",
+    "—",
+    "…",
+}
+
+DEVICE = "cuda"
+HZ = 50
+
+
+def get_bert_feature(text, bert_tokenizer, bert_model, word2ph):
+    with torch.no_grad():
+        inputs = bert_tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(DEVICE)
+        res = bert_model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
+    assert len(word2ph) == len(text)
+    phone_level_feature = []
+    for i in range(len(word2ph)):
+        repeat_feature = res[i].repeat(word2ph[i], 1)
+        phone_level_feature.append(repeat_feature)
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+    return phone_level_feature.T
+
+
+def change_sovits_weights(sovits_path, is_half):
+
+    dict_s2 = torch.load(sovits_path, map_location="cpu")
+    hps = dict_s2["config"]
+    hps.model.semantic_frame_rate = "25hz"
+    vq_model = SynthesizerTrn(
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model,
+    )
+    if "pretrained" not in sovits_path:
+        del vq_model.enc_q
+    if is_half:
+        vq_model = vq_model.half()
+    vq_model = vq_model.to(DEVICE)
+    vq_model.eval()
+    print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
+
+    return vq_model, hps
+
+
+def change_gpt_weights(gpt_path, is_half):
+    dict_s1 = torch.load(gpt_path, map_location="cpu")
+    config = dict_s1["config"]
+    max_sec = config["data"]["max_sec"]
+    t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
+    t2s_model.load_state_dict(dict_s1["weight"])
+    if is_half:
+        t2s_model = t2s_model.half()
+    t2s_model = t2s_model.to(DEVICE)
+    t2s_model.eval()
+    total = sum([param.nelement() for param in t2s_model.parameters()])
+    print("Number of parameter: %.2fM" % (total / 1e6))
+
+    return max_sec, t2s_model
+
+
+def get_spepc(hps, filename):
+    audio = load_audio(filename, int(hps.data.sampling_rate))
+    audio = torch.FloatTensor(audio)
+    audio_norm = audio
+    audio_norm = audio_norm.unsqueeze(0)
+    spec = spectrogram_torch(
+        audio_norm,
+        hps.data.filter_length,
+        hps.data.sampling_rate,
+        hps.data.hop_length,
+        hps.data.win_length,
+        center=False,
+    )
+    return spec
+
+
+def clean_text_inf(text, language):
+    phones, word2ph, norm_text = clean_text(text, language)
+    phones = cleaned_text_to_sequence(phones)
+    return phones, word2ph, norm_text
+
+
+def get_bert_inf(phones, word2ph, bert_tokenizer, bert_model, norm_text, language, is_half=True):
+    language = language.replace("all_", "")
+    if language == "zh":
+        bert = get_bert_feature(norm_text, bert_tokenizer, bert_model, word2ph).to(DEVICE)  # .to(dtype)
+    else:
+        bert = torch.zeros((1024, len(phones)), dtype=torch.float16 if is_half else torch.float32).to(DEVICE)
+
+    return bert
+
+
+def get_first(text):
+    pattern = "[" + "".join(re.escape(sep) for sep in symbol_splits) + "]"
+    text = re.split(pattern, text)[0].strip()
+    return text
+
+
+def get_phones_and_bert(text, bert_tokenizer, bert_model, language, is_half=True):
+    if language in {"en", "all_zh", "all_ja"}:
+        language = language.replace("all_", "")
+        if language == "en":
+            LangSegment.setfilters(["en"])
+            formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
+        else:
+            # 因无法区别中日文汉字,以用户输入为准
+            formattext = text
+        while "  " in formattext:
+            formattext = formattext.replace("  ", " ")
+        phones, word2ph, norm_text = clean_text_inf(formattext, language)
+        if language == "zh":
+            bert = get_bert_feature(norm_text, bert_tokenizer, bert_model, word2ph).to(DEVICE)
+        else:
+            bert = torch.zeros(
+                (1024, len(phones)),
+                dtype=torch.float16 if is_half else torch.float32,
+            ).to(DEVICE)
+    elif language in {"zh", "ja", "auto"}:
+        textlist = []
+        langlist = []
+        LangSegment.setfilters(["zh", "ja", "en", "ko"])
+        if language == "auto":
+            for tmp in LangSegment.getTexts(text):
+                if tmp["lang"] == "ko":
+                    langlist.append("zh")
+                    textlist.append(tmp["text"])
+                else:
+                    langlist.append(tmp["lang"])
+                    textlist.append(tmp["text"])
+        else:
+            for tmp in LangSegment.getTexts(text):
+                if tmp["lang"] == "en":
+                    langlist.append(tmp["lang"])
+                else:
+                    # 因无法区别中日文汉字,以用户输入为准
+                    langlist.append(language)
+                textlist.append(tmp["text"])
+        print(textlist)
+        print(langlist)
+        phones_list = []
+        bert_list = []
+        norm_text_list = []
+        for i in range(len(textlist)):
+            lang = langlist[i]
+            phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
+            bert = get_bert_inf(phones, word2ph, bert_tokenizer, bert_model, norm_text, lang, is_half)
+            phones_list.append(phones)
+            norm_text_list.append(norm_text)
+            bert_list.append(bert)
+        bert = torch.cat(bert_list, dim=1)
+        phones = sum(phones_list, [])
+        norm_text = "".join(norm_text_list)
+
+    return phones, bert.to(torch.float16 if is_half else torch.float32), norm_text
+
+
+def merge_short_text_in_array(texts, threshold):
+    if (len(texts)) < 2:
+        return texts
+    result = []
+    text = ""
+    for ele in texts:
+        text += ele
+        if len(text) >= threshold:
+            result.append(text)
+            text = ""
+    if len(text) > 0:
+        if len(result) == 0:
+            result.append(text)
+        else:
+            result[len(result) - 1] += text
+    return result
+
+
+def get_tts_wav(
+    text,
+    text_language,
+    bert_tokenizer,
+    bert_model,
+    ssl_model,
+    vq_model,
+    hps,
+    max_sec,
+    t2s_model: Text2SemanticLightningModule,
+    ref_wav_path,
+    prompt,
+    refer,
+    bert1,
+    phones1,
+    zero_wav,
+    prompt_text,
+    prompt_language,
+    how_to_cut="不切",
+    top_k=20,
+    top_p=0.6,
+    temperature=0.6,
+    ref_free=False,
+    is_half=True,
+    process_bar=None,
+):
+
+    dict_language = {
+        "中文": "all_zh",  # 全部按中文识别
+        "英文": "en",  # 全部按英文识别#######不变
+        "日文": "all_ja",  # 全部按日文识别
+        "中英混合": "zh",  # 按中英混合识别####不变
+        "日英混合": "ja",  # 按日英混合识别####不变
+        "多语种混合": "auto",  # 多语种启动切分识别语种
+    }
+
+    prompt_language = dict_language[prompt_language]
+    text_language = dict_language[text_language]
+
+    text = text.strip("\n")
+    if text[0] not in symbol_splits and len(get_first(text)) < 4:
+        text = "。" + text
+    print("=" * 20, "\n实际输入的目标文本:", text)
+
+    text = cut_sentences(text, how_to_cut)
+    print("=" * 20, "\n实际输入的目标文本(切句后):", text)
+
+    texts = text.split("\n")
+    texts = merge_short_text_in_array(texts, 5)  # 小于 5 个字符的句子和上一句合并
+
+    audio_opt = []
+    # if not ref_free:
+    #     phones1, bert1, _ = get_phones_and_bert(prompt_text, bert_tokenizer, bert_model, prompt_language, is_half)
+
+    for text_idx, text in enumerate(texts):
+
+        if process_bar is not None:
+            percent_complete = (text_idx + 1) / len(texts)
+            process_bar.progress(percent_complete, text=f"正在生成语音 {round(percent_complete * 100, 2)} % ...")
+
+        # 解决输入目标文本的空行导致报错的问题
+        if len(text.strip()) == 0:
+            continue
+        if text[-1] not in symbol_splits:
+            text += "。" if text_language != "en" else "."
+        print("=" * 20, "\n实际输入的目标文本(每句):", text)
+        phones2, bert2, norm_text2 = get_phones_and_bert(text, bert_tokenizer, bert_model, text_language, is_half)
+        print("=" * 20, "\n前端处理后的文本(每句):", norm_text2)
+
+        if not ref_free:
+            bert = torch.cat([bert1, bert2], 1)
+            all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(DEVICE).unsqueeze(0)
+        else:
+            pass
+            # bert = bert2
+            # all_phoneme_ids = torch.LongTensor(phones2).to(DEVICE).unsqueeze(0)
+
+        bert = bert.to(DEVICE).unsqueeze(0)
+        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(DEVICE)
+
+        with torch.no_grad():
+            pred_semantic, idx = t2s_model.model.infer_panel(
+                all_phoneme_ids,
+                all_phoneme_len,
+                None if ref_free else prompt,
+                bert,
+                top_k=top_k,
+                top_p=top_p,
+                temperature=temperature,
+                early_stop_num=HZ * max_sec,
+            )
+        pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)  # .unsqueeze(0) # mq要多unsqueeze一次
+
+        # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
+        audio = (
+            vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(DEVICE).unsqueeze(0), refer).detach().cpu().numpy()[0, 0]
+        )  ###试试重建不带上prompt部分
+        max_audio = np.abs(audio).max()  # 简单防止 16bit 爆音
+        if max_audio > 1:
+            audio /= max_audio
+        audio_opt.append(audio)
+        audio_opt.append(zero_wav)
+
+    return hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
+
+
+def split_txt(todo_text):
+    """根据 symbol_splits 标点切分句子
+
+    Args:
+        todo_text (str): 原文本
+
+    Returns:
+        list: 切后的文本 list
+    """
+
+    todo_text = todo_text.replace("……", "。").replace("——", "，")
+
+    if todo_text[-1] not in symbol_splits:
+        todo_text += "。"  # 尾部加入 。
+
+    i_split_head = i_split_tail = 0
+    len_text = len(todo_text)
+    todo_texts = []
+    while 1:
+        if i_split_head >= len_text:
+            break  # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
+        if todo_text[i_split_head] in symbol_splits:
+            i_split_head += 1
+            todo_texts.append(todo_text[i_split_tail:i_split_head])
+            i_split_tail = i_split_head
+        else:
+            i_split_head += 1
+    return todo_texts
+
+
+def cut_sentences(input_text, how_to_cut):
+
+    inp = input_text.strip("\n")
+
+    if how_to_cut == "凑四句一切":
+        inps = split_txt(inp)  # 根据标点符号直接切
+        split_idx = list(range(0, len(inps), 4))
+        split_idx[-1] = None
+        if len(split_idx) > 1:
+            opts = []
+            for idx in range(len(split_idx) - 1):
+                opts.append("".join(inps[split_idx[idx] : split_idx[idx + 1]]))
+        else:
+            opts = [inp]
+        cut_txt = "\n".join(opts)
+
+    elif how_to_cut == "凑50字一切":
+        inps = split_txt(inp)
+        if len(inps) < 2:
+            return inp
+        opts = []
+        summ = 0
+        tmp_str = ""
+        for i in range(len(inps)):
+            summ += len(inps[i])
+            tmp_str += inps[i]
+            if summ > 50:
+                summ = 0
+                opts.append(tmp_str)
+                tmp_str = ""
+        if tmp_str != "":
+            opts.append(tmp_str)
+        # print(opts)
+        if len(opts) > 1 and len(opts[-1]) < 50:  ##如果最后一个太短了，和前一个合一起
+            opts[-2] = opts[-2] + opts[-1]
+            opts = opts[:-1]
+        cut_txt = "\n".join(opts)
+
+    elif how_to_cut == "按中文句号。切":
+        cut_txt = "\n".join(["%s" % item for item in inp.strip("。").split("。")])
+
+    elif how_to_cut == "按英文句号.切":
+        cut_txt = "\n".join(["%s" % item for item in inp.strip(".").split(".")])
+
+    elif how_to_cut == "按标点符号切":
+        punds = r"[,.;?!、，。？！;：…]"
+        items = re.split(f"({punds})", inp)
+        mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
+        # 在句子不存在符号或句尾无符号的时候保证文本完整
+        if len(items) % 2 == 1:
+            mergeitems.append(items[-1])
+        cut_txt = "\n".join(mergeitems)
+
+    else:
+        cut_txt = inp
+
+    cut_txt = cut_txt.replace("\n\n", "\n")
+    return cut_txt
+
+
+def get_gpt_and_sovits_model_path(voice_character_name: str, tts_model_root: Path):
+    gpt_path_list = [i for i in tts_model_root.glob(f"{voice_character_name}*.ckpt")]
+    sovits_path_list = [i for i in tts_model_root.glob(f"{voice_character_name}*.pth")]
+
+    if len(gpt_path_list) > 0 and len(sovits_path_list) > 0:
+        return str(gpt_path_list[0]), str(sovits_path_list[0])
+    else:
+        return None, None
+
+
+@dataclass
+class HandlerTTS:
+    bert_tokenizer: BertTokenizerFast
+    bert_model: BertForMaskedLM
+    ssl_model: CNHubert
+    max_sec: KeyboardInterrupt
+    t2s_model: Text2SemanticLightningModule
+    vq_model: SynthesizerTrn
+    hps: HParams
+    inp_ref: str
+    prompt_text: str
+    prompt: torch.Tensor
+    refer: torch.Tensor
+    bert1: torch.Tensor
+    phones1: list
+    zero_wav: np.ndarray
+
+
+@st.cache_resource
+def get_tts_model(voice_character_name="艾丝妲", is_half=True):
+
+    os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
+    from huggingface_hub import hf_hub_download, snapshot_download
+
+    # https://huggingface.co/baicai1145/GPT-SoVITS-STAR/tree/main
+    tts_star_model_root = Path(WEB_CONFIGS.TTS_MODEL_DIR).joinpath("star")
+
+    gpt_path, sovits_path = get_gpt_and_sovits_model_path(voice_character_name, tts_star_model_root)
+
+    if gpt_path is None:
+        if tts_star_model_root.exists():
+            # 有可能中断了下载，先删除文件夹
+            shutil.rmtree(tts_star_model_root)
+
+        # 直接下载单个文件
+        tts_model_dir = hf_hub_download(
+            repo_id="baicai1145/GPT-SoVITS-STAR",
+            filename=f"{voice_character_name}.zip",
+            local_dir=str(tts_star_model_root),
+        )
+
+        # 解压
+        os.system(f"cd {str(tts_star_model_root)} && unzip {voice_character_name}.zip")
+
+    gpt_path, sovits_path = get_gpt_and_sovits_model_path(voice_character_name, tts_star_model_root)
+    print(f"gpt_path dir = {gpt_path}")
+    print(f"sovits_path dir = {sovits_path}")
+
+    inf_name = "平静说话-你们经过的收容舱段收藏着诸多「奇物」和「遗器」，是最核心的研究场所。.wav"
+    prompt_text = inf_name.split("-")[-1].replace(".wav", "")
+    ref_wav_path = Path(tts_star_model_root).joinpath("参考音频", inf_name)
+
+    # https://huggingface.co/lj1995/GPT-SoVITS/tree/main
+    tts_model_dir = snapshot_download(repo_id="lj1995/GPT-SoVITS", local_dir=Path(WEB_CONFIGS.TTS_MODEL_DIR).joinpath("pretrain"))
+    cnhubert_base_path = os.path.join(tts_model_dir, "chinese-hubert-base")
+    bert_path = os.path.join(tts_model_dir, "chinese-roberta-wwm-ext-large")
+
+    print(f"cnhubert_base_path dir = {cnhubert_base_path}")
+    print(f"bert_path dir = {bert_path}")
+
+    print("Loading tts bert model...")
+    bert_tokenizer = AutoTokenizer.from_pretrained(bert_path)
+    bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
+    if is_half:
+        bert_model = bert_model.half()
+    bert_model = bert_model.to(DEVICE)
+    print("load tts bert model done!")
+
+    print("Loading tts ssl model...")
+    ssl_model = cnhubert.get_model(cnhubert_base_path)
+    if is_half:
+        ssl_model = ssl_model.half()
+    ssl_model = ssl_model.to(DEVICE)
+    print("load tts ssl model done !")
+
+    max_sec, t2s_model = change_gpt_weights(gpt_path, is_half)
+    vq_model, hps = change_sovits_weights(sovits_path, is_half)
+
+    zero_wav = np.zeros(
+        int(hps.data.sampling_rate * 0.3),
+        dtype=np.float16 if is_half else np.float32,
+    )
+    print("=" * 20, "\n加载参考音频 。。。")
+    t1 = time.time()
+    with torch.no_grad():
+        wav16k, sr = librosa.load(ref_wav_path, sr=16000)
+        if wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000:
+            raise OSError("参考音频在3~10秒范围外，请更换！")
+        wav16k = torch.from_numpy(wav16k)
+        zero_wav_torch = torch.from_numpy(zero_wav)
+
+        wav16k = wav16k.half()
+        zero_wav_torch = zero_wav_torch.half()
+
+        wav16k = wav16k.to(DEVICE)
+        zero_wav_torch = zero_wav_torch.to(DEVICE)
+
+        wav16k = torch.cat([wav16k, zero_wav_torch])
+        ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2)  # .float()
+        codes = vq_model.extract_latent(ssl_content)
+
+        prompt_semantic = codes[0, 0]
+        prompt = prompt_semantic.unsqueeze(0).to(DEVICE)
+    print("加载 参考音频 用时: ", time.time() - t1)
+
+    t3 = time.time()
+    refer = get_spepc(hps, ref_wav_path)
+    if is_half:
+        refer = refer.half()
+    refer = refer.to(DEVICE)
+    print("get_spepc 用时: ", time.time() - t3)
+
+    ref_free = False
+    dict_language = {
+        "中文": "all_zh",  # 全部按中文识别
+        "英文": "en",  # 全部按英文识别#######不变
+        "日文": "all_ja",  # 全部按日文识别
+        "中英混合": "zh",  # 按中英混合识别####不变
+        "日英混合": "ja",  # 按日英混合识别####不变
+        "多语种混合": "auto",  # 多语种启动切分识别语种
+    }
+
+    prompt_text = prompt_text.strip("\n")
+    if prompt_text[-1] not in symbol_splits:
+        prompt_text += "。"
+    print("=" * 20, "\n音频参考文本:", prompt_text)
+
+    if not ref_free:
+        phones1, bert1, _ = get_phones_and_bert(prompt_text, bert_tokenizer, bert_model, dict_language["中英混合"], is_half)
+
+    tts_handler = HandlerTTS(
+        bert_tokenizer=bert_tokenizer,
+        bert_model=bert_model,
+        ssl_model=ssl_model,
+        max_sec=max_sec,
+        t2s_model=t2s_model,
+        vq_model=vq_model,
+        hps=hps,
+        inp_ref=str(ref_wav_path),
+        prompt_text=prompt_text,
+        prompt=prompt,
+        refer=refer,
+        bert1=bert1,
+        phones1=phones1,
+        zero_wav=zero_wav,
+    )
+
+    return tts_handler
+
+
+def gen_tts_wav(
+    text,
+    text_language,
+    bert_tokenizer,
+    bert_model,
+    ssl_model,
+    vq_model,
+    hps,
+    max_sec,
+    t2s_model,
+    inp_ref,
+    prompt_text,
+    prompt,
+    refer,
+    bert1,
+    phones1,
+    zero_wav,
+    wav_path_output,
+    how_to_cut="凑四句一切",  # ["不切", "凑四句一切", "凑50字一切", "按中文句号。切", "按英文句号.切", "按标点符号切"]
+):
+
+    process_bar = st.progress(0, text="正在生成语音...")
+
+    # 推理
+    sampling_rate, audio_data = get_tts_wav(
+        text,
+        text_language,
+        bert_tokenizer,
+        bert_model,
+        ssl_model,
+        vq_model,
+        hps,
+        max_sec,
+        t2s_model,
+        inp_ref,
+        prompt,
+        refer,
+        bert1,
+        phones1,
+        zero_wav,
+        prompt_text,
+        prompt_language="中英混合",
+        how_to_cut=how_to_cut,
+        top_k=5,  # 0 ~ 100
+        top_p=1,  # 0. ~ 1.
+        temperature=1,  # 0. ~ 1.
+        ref_free=False,
+        is_half=True,
+        process_bar=process_bar,
+    )
+
+    process_bar.progress(1, text=f"正在生成语音 100.00 % ...")
+    process_bar.empty()
+
+    # 保存
+    wav = BytesIO()
+    sf.write(wav, audio_data, sampling_rate, format="wav")
+    wav.seek(0)
+
+    with open(wav_path_output, "wb") as f:
+        f.write(wav.getvalue())
+    print("output:", wav_path_output)
+
+
+def demo():
+
+    # https://huggingface.co/baicai1145/GPT-SoVITS-STAR/tree/main
+    gpt_path = "./work_dirs/gpt_sovits/weights/GPT_weights/艾丝妲-e10.ckpt"
+    sovits_path = "./work_dirs/gpt_sovits/weights/SoVITS_weights/艾丝妲_e25_s925.pth"
+
+    # https://huggingface.co/lj1995/GPT-SoVITS/tree/main
+    cnhubert_base_path = "./work_dirs/gpt_sovits/weights/pretrained_models/chinese-hubert-base"
+    bert_path = "./work_dirs/utils/tts/gpt_sovits/weights/pretrained_models/chinese-roberta-wwm-ext-large"
+
+    inp_ref = r"./work_dirs/ref_wav/【开心】处理完之前的事情，这几天甚至都有空闲来车上转转了。.wav"
+
+    bert_tokenizer, bert_model, ssl_model, max_sec, t2s_model, vq_model, hps = get_tts_model(
+        bert_path, cnhubert_base_path, gpt_path, sovits_path, is_half=True
+    )
+
+    text = """哈喽哈喽，家人们好啊！今天呀，咱们这儿可是有大大的福利等着大家哦你们猜猜看是什么呢？没错啦，就是这款超级棒的本草精华洗发露啦！哎呀，我知道你们一定都想知道它的神奇之处吧？那就让小甜心来给你们一一揭秘吧💖
+
+首先呢，这款洗发露的配方真的是超级温和的哦，就算是敏感肌的小仙女们也能安心使用呢！而且它还能深层清洁我们的头皮，把那些烦人的油脂和污垢通通赶走，让我们的头发更加清爽健康呢！💦💦
+
+再来就是它的滋养效果啦，富含多种草本精华，轻轻一抹就能给我们的头皮提供满满的养分，让秀发更加乌黑亮丽，顺滑如丝哦！💖💖💖
+
+还有啊，这款洗发露的泡沫真的是超级丰富呢！轻轻一挤就能挤出好多好多细腻绵密的泡沫来，洗起来既舒服又干净，感觉就像是在给我们的头发做SPA一样呢！💖💖💖
+
+最后啊，这款洗发露还特别容易冲洗哦！用完之后轻轻一冲就能把泡沫全部冲洗干净，不会残留任何黏腻感，让你随时随地保持清爽状态哦！💦💦💦
+
+而且呀，这款洗发露不仅适用于各种发质，无论是油性、干性还是混合性，都能轻松应对呢！所以家人们，无论你是哪种发质，只要用了这款洗发露，保证让你的头发焕发出前所未有的光彩哦！💖💖💖
+
+好啦，家人们，这么一款集温和、深层清洁、滋养、丰富泡沫、易冲洗于一身的神级洗发露，你们是不是已经心动了呢？快来把它带回家吧，让你的秀发从此告别烦恼，迎接美丽新世界吧！💖💖💖"""
+    text_language = "中英混合"
+
+    gen_tts_wav(
+        text,
+        text_language,
+        bert_tokenizer,
+        bert_model,
+        ssl_model,
+        vq_model,
+        hps,
+        max_sec,
+        t2s_model,
+        inp_ref,
+        wav_path_output=r"./work_dirs/tts_wavs/gpt-sovits-test.wav",
+    )
+
+
+if __name__ == "__main__":
+    demo()
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diff --git a/utils/tts/gpt_sovits/module/attentions.py b/utils/tts/gpt_sovits/module/attentions.py
new file mode 100644
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--- /dev/null
+++ b/utils/tts/gpt_sovits/module/attentions.py
@@ -0,0 +1,658 @@
+import math
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from utils.tts.gpt_sovits.module import commons
+from utils.tts.gpt_sovits.module.modules import LayerNorm
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=4,
+        isflow=False,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    window_size=window_size,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+        if isflow:
+            cond_layer = torch.nn.Conv1d(kwargs["gin_channels"], 2 * hidden_channels * n_layers, 1)
+            self.cond_pre = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, 1)
+            self.cond_layer = weight_norm_modules(cond_layer, name="weight")
+            self.gin_channels = kwargs["gin_channels"]
+
+    def forward(self, x, x_mask, g=None):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        if g is not None:
+            g = self.cond_layer(g)
+
+        for i in range(self.n_layers):
+            if g is not None:
+                x = self.cond_pre(x)
+                cond_offset = i * 2 * self.hidden_channels
+                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
+                x = commons.fused_add_tanh_sigmoid_multiply(x, g_l, torch.IntTensor([self.hidden_channels]))
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        proximal_bias=False,
+        proximal_init=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+
+        self.drop = nn.Dropout(p_dropout)
+        self.self_attn_layers = nn.ModuleList()
+        self.norm_layers_0 = nn.ModuleList()
+        self.encdec_attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.self_attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    proximal_bias=proximal_bias,
+                    proximal_init=proximal_init,
+                )
+            )
+            self.norm_layers_0.append(LayerNorm(hidden_channels))
+            self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                    causal=True,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, h, h_mask):
+        """
+        x: decoder input
+        h: encoder output
+        """
+        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
+        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            y = self.self_attn_layers[i](x, x, self_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_0[i](x + y)
+
+            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        block_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels**-0.5
+            self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+            self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert t_s == t_t, "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(query / math.sqrt(self.k_channels), key_relative_embeddings)
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert t_s == t_t, "Local attention is only available for self-attention."
+                block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
+            output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
+        output = output.transpose(2, 3).contiguous().view(b, d, t_t)  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        max_relative_position = 2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+            )
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]))
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1 :]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]))
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout=0.0,
+        activation=None,
+        causal=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+
+import torch.nn as nn
+from torch.nn.utils import remove_weight_norm, weight_norm
+
+
+class Depthwise_Separable_Conv1D(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        bias=True,
+        padding_mode="zeros",  # TODO: refine this type
+        device=None,
+        dtype=None,
+    ):
+        super().__init__()
+        self.depth_conv = nn.Conv1d(
+            in_channels=in_channels,
+            out_channels=in_channels,
+            kernel_size=kernel_size,
+            groups=in_channels,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias,
+            padding_mode=padding_mode,
+            device=device,
+            dtype=dtype,
+        )
+        self.point_conv = nn.Conv1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            bias=bias,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input):
+        return self.point_conv(self.depth_conv(input))
+
+    def weight_norm(self):
+        self.depth_conv = weight_norm(self.depth_conv, name="weight")
+        self.point_conv = weight_norm(self.point_conv, name="weight")
+
+    def remove_weight_norm(self):
+        self.depth_conv = remove_weight_norm(self.depth_conv, name="weight")
+        self.point_conv = remove_weight_norm(self.point_conv, name="weight")
+
+
+class Depthwise_Separable_TransposeConv1D(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",  # TODO: refine this type
+        device=None,
+        dtype=None,
+    ):
+        super().__init__()
+        self.depth_conv = nn.ConvTranspose1d(
+            in_channels=in_channels,
+            out_channels=in_channels,
+            kernel_size=kernel_size,
+            groups=in_channels,
+            stride=stride,
+            output_padding=output_padding,
+            padding=padding,
+            dilation=dilation,
+            bias=bias,
+            padding_mode=padding_mode,
+            device=device,
+            dtype=dtype,
+        )
+        self.point_conv = nn.Conv1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            bias=bias,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input):
+        return self.point_conv(self.depth_conv(input))
+
+    def weight_norm(self):
+        self.depth_conv = weight_norm(self.depth_conv, name="weight")
+        self.point_conv = weight_norm(self.point_conv, name="weight")
+
+    def remove_weight_norm(self):
+        remove_weight_norm(self.depth_conv, name="weight")
+        remove_weight_norm(self.point_conv, name="weight")
+
+
+def weight_norm_modules(module, name="weight", dim=0):
+    if isinstance(module, Depthwise_Separable_Conv1D) or isinstance(module, Depthwise_Separable_TransposeConv1D):
+        module.weight_norm()
+        return module
+    else:
+        return weight_norm(module, name, dim)
+
+
+def remove_weight_norm_modules(module, name="weight"):
+    if isinstance(module, Depthwise_Separable_Conv1D) or isinstance(module, Depthwise_Separable_TransposeConv1D):
+        module.remove_weight_norm()
+    else:
+        remove_weight_norm(module, name)
+
+
+class FFT(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers=1,
+        kernel_size=1,
+        p_dropout=0.0,
+        proximal_bias=False,
+        proximal_init=True,
+        isflow=False,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        if isflow:
+            cond_layer = torch.nn.Conv1d(kwargs["gin_channels"], 2 * hidden_channels * n_layers, 1)
+            self.cond_pre = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, 1)
+            self.cond_layer = weight_norm_modules(cond_layer, name="weight")
+            self.gin_channels = kwargs["gin_channels"]
+        self.drop = nn.Dropout(p_dropout)
+        self.self_attn_layers = nn.ModuleList()
+        self.norm_layers_0 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.self_attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    proximal_bias=proximal_bias,
+                    proximal_init=proximal_init,
+                )
+            )
+            self.norm_layers_0.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                    causal=True,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, g=None):
+        """
+        x: decoder input
+        h: encoder output
+        """
+        if g is not None:
+            g = self.cond_layer(g)
+
+        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if g is not None:
+                x = self.cond_pre(x)
+                cond_offset = i * 2 * self.hidden_channels
+                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
+                x = commons.fused_add_tanh_sigmoid_multiply(x, g_l, torch.IntTensor([self.hidden_channels]))
+            y = self.self_attn_layers[i](x, x, self_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_0[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class TransformerCouplingLayer(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        n_layers,
+        n_heads,
+        p_dropout=0,
+        filter_channels=0,
+        mean_only=False,
+        wn_sharing_parameter=None,
+        gin_channels=0,
+    ):
+        assert channels % 2 == 0, "channels should be divisible by 2"
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.half_channels = channels // 2
+        self.mean_only = mean_only
+
+        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+        self.enc = (
+            Encoder(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=gin_channels,
+            )
+            if wn_sharing_parameter is None
+            else wn_sharing_parameter
+        )
+        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+        self.post.weight.data.zero_()
+        self.post.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0) * x_mask
+        h = self.enc(h, x_mask, g=g)
+        stats = self.post(h) * x_mask
+        if not self.mean_only:
+            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
+        else:
+            m = stats
+            logs = torch.zeros_like(m)
+
+        if not reverse:
+            x1 = m + x1 * torch.exp(logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            logdet = torch.sum(logs, [1, 2])
+            return x, logdet
+        else:
+            x1 = (x1 - m) * torch.exp(-logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            return x
diff --git a/utils/tts/gpt_sovits/module/cnhubert.py b/utils/tts/gpt_sovits/module/cnhubert.py
new file mode 100644
index 0000000000000000000000000000000000000000..544e4894d260e38fc50e62ddd55c3f9cbf554a4d
--- /dev/null
+++ b/utils/tts/gpt_sovits/module/cnhubert.py
@@ -0,0 +1,24 @@
+import logging
+
+import torch.nn as nn
+from transformers import HubertModel, Wav2Vec2FeatureExtractor
+
+logging.getLogger("numba").setLevel(logging.WARNING)
+
+
+class CNHubert(nn.Module):
+    def __init__(self, cnhubert_base_path):
+        super().__init__()
+        self.model = HubertModel.from_pretrained(cnhubert_base_path)
+        self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(cnhubert_base_path)
+
+    def forward(self, x):
+        input_values = self.feature_extractor(x, return_tensors="pt", sampling_rate=16000).input_values.to(x.device)
+        feats = self.model(input_values)["last_hidden_state"]
+        return feats
+
+
+def get_model(cnhubert_base_path):
+    model = CNHubert(cnhubert_base_path)
+    model.eval()
+    return model
diff --git a/utils/tts/gpt_sovits/module/commons.py b/utils/tts/gpt_sovits/module/commons.py
new file mode 100644
index 0000000000000000000000000000000000000000..e96cf923515c7f5648af9dfedaa803df3f4f28be
--- /dev/null
+++ b/utils/tts/gpt_sovits/module/commons.py
@@ -0,0 +1,189 @@
+import math
+import torch
+from torch.nn import functional as F
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+def convert_pad_shape(pad_shape):
+    l = pad_shape[::-1]
+    pad_shape = [item for sublist in l for item in sublist]
+    return pad_shape
+
+
+def intersperse(lst, item):
+    result = [item] * (len(lst) * 2 + 1)
+    result[1::2] = lst
+    return result
+
+
+def kl_divergence(m_p, logs_p, m_q, logs_q):
+    """KL(P||Q)"""
+    kl = (logs_q - logs_p) - 0.5
+    kl += (
+        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
+    )
+    return kl
+
+
+def rand_gumbel(shape):
+    """Sample from the Gumbel distribution, protect from overflows."""
+    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
+    return -torch.log(-torch.log(uniform_samples))
+
+
+def rand_gumbel_like(x):
+    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
+    return g
+
+
+def slice_segments(x, ids_str, segment_size=4):
+    ret = torch.zeros_like(x[:, :, :segment_size])
+    for i in range(x.size(0)):
+        idx_str = ids_str[i]
+        idx_end = idx_str + segment_size
+        ret[i] = x[i, :, idx_str:idx_end]
+    return ret
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+    b, d, t = x.size()
+    if x_lengths is None:
+        x_lengths = t
+    ids_str_max = x_lengths - segment_size + 1
+    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+    ret = slice_segments(x, ids_str, segment_size)
+    return ret, ids_str
+
+
+def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
+    position = torch.arange(length, dtype=torch.float)
+    num_timescales = channels // 2
+    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
+        num_timescales - 1
+    )
+    inv_timescales = min_timescale * torch.exp(
+        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
+    )
+    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+    signal = F.pad(signal, [0, 0, 0, channels % 2])
+    signal = signal.view(1, channels, length)
+    return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+    return mask
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+def convert_pad_shape(pad_shape):
+    l = pad_shape[::-1]
+    pad_shape = [item for sublist in l for item in sublist]
+    return pad_shape
+
+
+def shift_1d(x):
+    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
+    return x
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration, mask):
+    """
+    duration: [b, 1, t_x]
+    mask: [b, 1, t_y, t_x]
+    """
+    device = duration.device
+
+    b, _, t_y, t_x = mask.shape
+    cum_duration = torch.cumsum(duration, -1)
+
+    cum_duration_flat = cum_duration.view(b * t_x)
+    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+    path = path.view(b, t_x, t_y)
+    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+    path = path.unsqueeze(1).transpose(2, 3) * mask
+    return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    parameters = list(filter(lambda p: p.grad is not None, parameters))
+    norm_type = float(norm_type)
+    if clip_value is not None:
+        clip_value = float(clip_value)
+
+    total_norm = 0
+    for p in parameters:
+        param_norm = p.grad.data.norm(norm_type)
+        total_norm += param_norm.item() ** norm_type
+        if clip_value is not None:
+            p.grad.data.clamp_(min=-clip_value, max=clip_value)
+    total_norm = total_norm ** (1.0 / norm_type)
+    return total_norm
+
+
+def squeeze(x, x_mask=None, n_sqz=2):
+    b, c, t = x.size()
+
+    t = (t // n_sqz) * n_sqz
+    x = x[:, :, :t]
+    x_sqz = x.view(b, c, t // n_sqz, n_sqz)
+    x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz)
+
+    if x_mask is not None:
+        x_mask = x_mask[:, :, n_sqz - 1 :: n_sqz]
+    else:
+        x_mask = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype)
+    return x_sqz * x_mask, x_mask
+
+
+def unsqueeze(x, x_mask=None, n_sqz=2):
+    b, c, t = x.size()
+
+    x_unsqz = x.view(b, n_sqz, c // n_sqz, t)
+    x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz)
+
+    if x_mask is not None:
+        x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz)
+    else:
+        x_mask = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype)
+    return x_unsqz * x_mask, x_mask
diff --git a/utils/tts/gpt_sovits/module/core_vq.py b/utils/tts/gpt_sovits/module/core_vq.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5e22d667631d22a561189651e18e821800b57a5
--- /dev/null
+++ b/utils/tts/gpt_sovits/module/core_vq.py
@@ -0,0 +1,383 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+# This implementation is inspired from
+# https://github.com/lucidrains/vector-quantize-pytorch
+# which is released under MIT License. Hereafter, the original license:
+# MIT License
+#
+# Copyright (c) 2020 Phil Wang
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+"""Core vector quantization implementation."""
+import typing as tp
+
+from einops import rearrange, repeat
+import torch
+from torch import nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+
+def default(val: tp.Any, d: tp.Any) -> tp.Any:
+    return val if val is not None else d
+
+
+def ema_inplace(moving_avg, new, decay: float):
+    moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
+
+
+def laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5):
+    return (x + epsilon) / (x.sum() + n_categories * epsilon)
+
+
+def uniform_init(*shape: int):
+    t = torch.empty(shape)
+    nn.init.kaiming_uniform_(t)
+    return t
+
+
+def sample_vectors(samples, num: int):
+    num_samples, device = samples.shape[0], samples.device
+
+    if num_samples >= num:
+        indices = torch.randperm(num_samples, device=device)[:num]
+    else:
+        indices = torch.randint(0, num_samples, (num,), device=device)
+
+    return samples[indices]
+
+
+def kmeans(samples, num_clusters: int, num_iters: int = 10):
+    dim, dtype = samples.shape[-1], samples.dtype
+    max_kmeans_samples = 500
+    samples = samples[:max_kmeans_samples, :]
+    means = sample_vectors(samples, num_clusters)
+
+    print("kmeans start ... ")
+    for _ in tqdm(range(num_iters)):
+        diffs = rearrange(samples, "n d -> n () d") - rearrange(means, "c d -> () c d")
+        dists = -(diffs**2).sum(dim=-1)
+
+        buckets = dists.max(dim=-1).indices
+        bins = torch.bincount(buckets, minlength=num_clusters)
+        zero_mask = bins == 0
+        bins_min_clamped = bins.masked_fill(zero_mask, 1)
+
+        new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype)
+        new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples)
+        new_means = new_means / bins_min_clamped[..., None]
+
+        means = torch.where(zero_mask[..., None], means, new_means)
+
+    return means, bins
+
+
+class EuclideanCodebook(nn.Module):
+    """Codebook with Euclidean distance.
+    Args:
+        dim (int): Dimension.
+        codebook_size (int): Codebook size.
+        kmeans_init (bool): Whether to use k-means to initialize the codebooks.
+            If set to true, run the k-means algorithm on the first training batch and use
+            the learned centroids as initialization.
+        kmeans_iters (int): Number of iterations used for k-means algorithm at initialization.
+        decay (float): Decay for exponential moving average over the codebooks.
+        epsilon (float): Epsilon value for numerical stability.
+        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
+            that have an exponential moving average cluster size less than the specified threshold with
+            randomly selected vector from the current batch.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        codebook_size: int,
+        kmeans_init: int = False,
+        kmeans_iters: int = 10,
+        decay: float = 0.99,
+        epsilon: float = 1e-5,
+        threshold_ema_dead_code: int = 2,
+    ):
+        super().__init__()
+        self.decay = decay
+        init_fn: tp.Union[tp.Callable[..., torch.Tensor], tp.Any] = (
+            uniform_init if not kmeans_init else torch.zeros
+        )
+        embed = init_fn(codebook_size, dim)
+
+        self.codebook_size = codebook_size
+
+        self.kmeans_iters = kmeans_iters
+        self.epsilon = epsilon
+        self.threshold_ema_dead_code = threshold_ema_dead_code
+
+        self.register_buffer("inited", torch.Tensor([not kmeans_init]))
+        self.register_buffer("cluster_size", torch.zeros(codebook_size))
+        self.register_buffer("embed", embed)
+        self.register_buffer("embed_avg", embed.clone())
+
+    @torch.jit.ignore
+    def init_embed_(self, data):
+        if self.inited:
+            return
+
+        embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters)
+        self.embed.data.copy_(embed)
+        self.embed_avg.data.copy_(embed.clone())
+        self.cluster_size.data.copy_(cluster_size)
+        self.inited.data.copy_(torch.Tensor([True]))
+        # Make sure all buffers across workers are in sync after initialization
+        # broadcast_tensors(self.buffers())
+
+    def replace_(self, samples, mask):
+        modified_codebook = torch.where(
+            mask[..., None], sample_vectors(samples, self.codebook_size), self.embed
+        )
+        self.embed.data.copy_(modified_codebook)
+
+    def expire_codes_(self, batch_samples):
+        if self.threshold_ema_dead_code == 0:
+            return
+
+        expired_codes = self.cluster_size < self.threshold_ema_dead_code
+        if not torch.any(expired_codes):
+            return
+
+        batch_samples = rearrange(batch_samples, "... d -> (...) d")
+        self.replace_(batch_samples, mask=expired_codes)
+        # broadcast_tensors(self.buffers())
+
+    def preprocess(self, x):
+        x = rearrange(x, "... d -> (...) d")
+        return x
+
+    def quantize(self, x):
+        embed = self.embed.t()
+        dist = -(
+            x.pow(2).sum(1, keepdim=True)
+            - 2 * x @ embed
+            + embed.pow(2).sum(0, keepdim=True)
+        )
+        embed_ind = dist.max(dim=-1).indices
+        return embed_ind
+
+    def postprocess_emb(self, embed_ind, shape):
+        return embed_ind.view(*shape[:-1])
+
+    def dequantize(self, embed_ind):
+        quantize = F.embedding(embed_ind, self.embed)
+        return quantize
+
+    def encode(self, x):
+        shape = x.shape
+        # pre-process
+        x = self.preprocess(x)
+        # quantize
+        embed_ind = self.quantize(x)
+        # post-process
+        embed_ind = self.postprocess_emb(embed_ind, shape)
+        return embed_ind
+
+    def decode(self, embed_ind):
+        quantize = self.dequantize(embed_ind)
+        return quantize
+
+    def forward(self, x):
+        shape, dtype = x.shape, x.dtype
+        x = self.preprocess(x)
+
+        self.init_embed_(x)
+
+        embed_ind = self.quantize(x)
+        embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
+        embed_ind = self.postprocess_emb(embed_ind, shape)
+        quantize = self.dequantize(embed_ind)
+
+        if self.training:
+            # We do the expiry of code at that point as buffers are in sync
+            # and all the workers will take the same decision.
+            self.expire_codes_(x)
+            ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)
+            embed_sum = x.t() @ embed_onehot
+            ema_inplace(self.embed_avg, embed_sum.t(), self.decay)
+            cluster_size = (
+                laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon)
+                * self.cluster_size.sum()
+            )
+            embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
+            self.embed.data.copy_(embed_normalized)
+
+        return quantize, embed_ind
+
+
+class VectorQuantization(nn.Module):
+    """Vector quantization implementation.
+    Currently supports only euclidean distance.
+    Args:
+        dim (int): Dimension
+        codebook_size (int): Codebook size
+        codebook_dim (int): Codebook dimension. If not defined, uses the specified dimension in dim.
+        decay (float): Decay for exponential moving average over the codebooks.
+        epsilon (float): Epsilon value for numerical stability.
+        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
+        kmeans_iters (int): Number of iterations used for kmeans initialization.
+        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
+            that have an exponential moving average cluster size less than the specified threshold with
+            randomly selected vector from the current batch.
+        commitment_weight (float): Weight for commitment loss.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        codebook_size: int,
+        codebook_dim: tp.Optional[int] = None,
+        decay: float = 0.99,
+        epsilon: float = 1e-5,
+        kmeans_init: bool = True,
+        kmeans_iters: int = 50,
+        threshold_ema_dead_code: int = 2,
+        commitment_weight: float = 1.0,
+    ):
+        super().__init__()
+        _codebook_dim: int = default(codebook_dim, dim)
+
+        requires_projection = _codebook_dim != dim
+        self.project_in = (
+            nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity()
+        )
+        self.project_out = (
+            nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity()
+        )
+
+        self.epsilon = epsilon
+        self.commitment_weight = commitment_weight
+
+        self._codebook = EuclideanCodebook(
+            dim=_codebook_dim,
+            codebook_size=codebook_size,
+            kmeans_init=kmeans_init,
+            kmeans_iters=kmeans_iters,
+            decay=decay,
+            epsilon=epsilon,
+            threshold_ema_dead_code=threshold_ema_dead_code,
+        )
+        self.codebook_size = codebook_size
+
+    @property
+    def codebook(self):
+        return self._codebook.embed
+
+    def encode(self, x):
+        x = rearrange(x, "b d n -> b n d")
+        x = self.project_in(x)
+        embed_in = self._codebook.encode(x)
+        return embed_in
+
+    def decode(self, embed_ind):
+        quantize = self._codebook.decode(embed_ind)
+        quantize = self.project_out(quantize)
+        quantize = rearrange(quantize, "b n d -> b d n")
+        return quantize
+
+    def forward(self, x):
+        device = x.device
+        x = rearrange(x, "b d n -> b n d")
+        x = self.project_in(x)
+
+        quantize, embed_ind = self._codebook(x)
+
+        if self.training:
+            quantize = x + (quantize - x).detach()
+
+        loss = torch.tensor([0.0], device=device, requires_grad=self.training)
+
+        if self.training:
+            if self.commitment_weight > 0:
+                commit_loss = F.mse_loss(quantize.detach(), x)
+                loss = loss + commit_loss * self.commitment_weight
+
+        quantize = self.project_out(quantize)
+        quantize = rearrange(quantize, "b n d -> b d n")
+        return quantize, embed_ind, loss
+
+
+class ResidualVectorQuantization(nn.Module):
+    """Residual vector quantization implementation.
+    Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf
+    """
+
+    def __init__(self, *, num_quantizers, **kwargs):
+        super().__init__()
+        self.layers = nn.ModuleList(
+            [VectorQuantization(**kwargs) for _ in range(num_quantizers)]
+        )
+
+    def forward(
+        self, x, n_q: tp.Optional[int] = None, layers: tp.Optional[list] = None
+    ):
+        quantized_out = 0.0
+        residual = x
+
+        all_losses = []
+        all_indices = []
+        out_quantized = []
+
+        n_q = n_q or len(self.layers)
+
+        for i, layer in enumerate(self.layers[:n_q]):
+            quantized, indices, loss = layer(residual)
+            residual = residual - quantized
+            quantized_out = quantized_out + quantized
+
+            all_indices.append(indices)
+            all_losses.append(loss)
+            if layers and i in layers:
+                out_quantized.append(quantized)
+
+        out_losses, out_indices = map(torch.stack, (all_losses, all_indices))
+        return quantized_out, out_indices, out_losses, out_quantized
+
+    def encode(
+        self, x: torch.Tensor, n_q: tp.Optional[int] = None, st: tp.Optional[int] = None
+    ) -> torch.Tensor:
+        residual = x
+        all_indices = []
+        n_q = n_q or len(self.layers)
+        st = st or 0
+        for layer in self.layers[st:n_q]:
+            indices = layer.encode(residual)
+            quantized = layer.decode(indices)
+            residual = residual - quantized
+            all_indices.append(indices)
+        out_indices = torch.stack(all_indices)
+        return out_indices
+
+    def decode(self, q_indices: torch.Tensor, st: int = 0) -> torch.Tensor:
+        quantized_out = torch.tensor(0.0, device=q_indices.device)
+        for i, indices in enumerate(q_indices):
+            layer = self.layers[st + i]
+            quantized = layer.decode(indices)
+            quantized_out = quantized_out + quantized
+        return quantized_out
diff --git a/utils/tts/gpt_sovits/module/mel_processing.py b/utils/tts/gpt_sovits/module/mel_processing.py
new file mode 100644
index 0000000000000000000000000000000000000000..0507c8160375252a6628a3584a50a037e76569ef
--- /dev/null
+++ b/utils/tts/gpt_sovits/module/mel_processing.py
@@ -0,0 +1,143 @@
+import torch
+import torch.nn.functional as F
+import torch.utils.data
+from librosa.filters import mel as librosa_mel_fn
+
+MAX_WAV_VALUE = 32768.0
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    """
+    PARAMS
+    ------
+    C: compression factor
+    """
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression_torch(x, C=1):
+    """
+    PARAMS
+    ------
+    C: compression factor used to compress
+    """
+    return torch.exp(x) / C
+
+
+def spectral_normalize_torch(magnitudes):
+    output = dynamic_range_compression_torch(magnitudes)
+    return output
+
+
+def spectral_de_normalize_torch(magnitudes):
+    output = dynamic_range_decompression_torch(magnitudes)
+    return output
+
+
+mel_basis = {}
+hann_window = {}
+
+
+def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
+    if torch.min(y) < -1.0:
+        print("min value is ", torch.min(y))
+    if torch.max(y) > 1.0:
+        print("max value is ", torch.max(y))
+
+    global hann_window
+    dtype_device = str(y.dtype) + "_" + str(y.device)
+    wnsize_dtype_device = str(win_size) + "_" + dtype_device
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
+            dtype=y.dtype, device=y.device
+        )
+
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1),
+        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+        mode="reflect",
+    )
+    y = y.squeeze(1)
+    spec = torch.stft(
+        y,
+        n_fft,
+        hop_length=hop_size,
+        win_length=win_size,
+        window=hann_window[wnsize_dtype_device],
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=False,
+    )
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    return spec
+
+
+def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
+    global mel_basis
+    dtype_device = str(spec.dtype) + "_" + str(spec.device)
+    fmax_dtype_device = str(fmax) + "_" + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(
+            sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
+        )
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
+            dtype=spec.dtype, device=spec.device
+        )
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = spectral_normalize_torch(spec)
+    return spec
+
+
+def mel_spectrogram_torch(
+    y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
+):
+    if torch.min(y) < -1.0:
+        print("min value is ", torch.min(y))
+    if torch.max(y) > 1.0:
+        print("max value is ", torch.max(y))
+
+    global mel_basis, hann_window
+    dtype_device = str(y.dtype) + "_" + str(y.device)
+    fmax_dtype_device = str(fmax) + "_" + dtype_device
+    wnsize_dtype_device = str(win_size) + "_" + dtype_device
+    if fmax_dtype_device not in mel_basis:
+        mel = librosa_mel_fn(
+            sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
+        )
+        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
+            dtype=y.dtype, device=y.device
+        )
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
+            dtype=y.dtype, device=y.device
+        )
+
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1),
+        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+        mode="reflect",
+    )
+    y = y.squeeze(1)
+
+    spec = torch.stft(
+        y,
+        n_fft,
+        hop_length=hop_size,
+        win_length=win_size,
+        window=hann_window[wnsize_dtype_device],
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=False,
+    )
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+
+    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = spectral_normalize_torch(spec)
+
+    return spec
diff --git a/utils/tts/gpt_sovits/module/models.py b/utils/tts/gpt_sovits/module/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..e56a67dbd8b8914b751ad04ced8e39ac407a1e21
--- /dev/null
+++ b/utils/tts/gpt_sovits/module/models.py
@@ -0,0 +1,916 @@
+import contextlib
+import math
+
+import torch
+from torch import nn
+from torch.cuda.amp import autocast
+from torch.nn import Conv1d, Conv2d, ConvTranspose1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
+
+from utils.tts.gpt_sovits.module import attentions, commons, modules
+from utils.tts.gpt_sovits.module.commons import get_padding, init_weights
+from utils.tts.gpt_sovits.module.mrte_model import MRTE
+from utils.tts.gpt_sovits.module.quantize import ResidualVectorQuantizer
+from utils.tts.gpt_sovits.text import symbols
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        if not reverse:
+            flows = self.flows
+            assert w is not None
+
+            logdet_tot_q = 0
+            h_w = self.post_pre(w)
+            h_w = self.post_convs(h_w, x_mask)
+            h_w = self.post_proj(h_w) * x_mask
+            e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
+            z_q = e_q
+            for flow in self.post_flows:
+                z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
+                logdet_tot_q += logdet_q
+            z_u, z1 = torch.split(z_q, [1, 1], 1)
+            u = torch.sigmoid(z_u) * x_mask
+            z0 = (w - u) * x_mask
+            logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
+            logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2]) - logdet_tot_q
+
+            logdet_tot = 0
+            z0, logdet = self.log_flow(z0, x_mask)
+            logdet_tot += logdet
+            z = torch.cat([z0, z1], 1)
+            for flow in flows:
+                z, logdet = flow(z, x_mask, g=x, reverse=reverse)
+                logdet_tot = logdet_tot + logdet
+            nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2]) - logdet_tot
+            return nll + logq  # [b]
+        else:
+            flows = list(reversed(self.flows))
+            flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+            z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
+            for flow in flows:
+                z = flow(z, x_mask, g=x, reverse=reverse)
+            z0, z1 = torch.split(z, [1, 1], 1)
+            logw = z0
+            return logw
+
+
+class DurationPredictor(nn.Module):
+    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_1 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+    def forward(self, x, x_mask, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        latent_channels=192,
+    ):
+        super().__init__()
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.latent_channels = latent_channels
+
+        self.ssl_proj = nn.Conv1d(768, hidden_channels, 1)
+
+        self.encoder_ssl = attentions.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers // 2,
+            kernel_size,
+            p_dropout,
+        )
+
+        self.encoder_text = attentions.Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout)
+        self.text_embedding = nn.Embedding(len(symbols), hidden_channels)
+
+        self.mrte = MRTE()
+
+        self.encoder2 = attentions.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers // 2,
+            kernel_size,
+            p_dropout,
+        )
+
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, y, y_lengths, text, text_lengths, ge, test=None):
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype)
+
+        y = self.ssl_proj(y * y_mask) * y_mask
+
+        y = self.encoder_ssl(y * y_mask, y_mask)
+
+        text_mask = torch.unsqueeze(commons.sequence_mask(text_lengths, text.size(1)), 1).to(y.dtype)
+        if test == 1:
+            text[:, :] = 0
+        text = self.text_embedding(text).transpose(1, 2)
+        text = self.encoder_text(text * text_mask, text_mask)
+        y = self.mrte(y, y_mask, text, text_mask, ge)
+
+        y = self.encoder2(y * y_mask, y_mask)
+
+        stats = self.proj(y) * y_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return y, m, logs, y_mask
+
+    def extract_latent(self, x):
+        x = self.ssl_proj(x)
+        quantized, codes, commit_loss, quantized_list = self.quantizer(x)
+        return codes.transpose(0, 1)
+
+    def decode_latent(self, codes, y_mask, refer, refer_mask, ge):
+        quantized = self.quantizer.decode(codes)
+
+        y = self.vq_proj(quantized) * y_mask
+        y = self.encoder_ssl(y * y_mask, y_mask)
+
+        y = self.mrte(y, y_mask, refer, refer_mask, ge)
+
+        y = self.encoder2(y * y_mask, y_mask)
+
+        stats = self.proj(y) * y_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return y, m, logs, y_mask, quantized
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        if g != None:
+            g = g.detach()
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class WNEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+        self.norm = modules.LayerNorm(out_channels)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        out = self.proj(x) * x_mask
+        out = self.norm(out)
+        return out
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class ReferenceEncoder(nn.Module):
+    """
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    """
+
+    def __init__(self, spec_channels, gin_channels=0):
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [
+            weight_norm(
+                nn.Conv2d(
+                    in_channels=filters[i],
+                    out_channels=filters[i + 1],
+                    kernel_size=(3, 3),
+                    stride=(2, 2),
+                    padding=(1, 1),
+                )
+            )
+            for i in range(K)
+        ]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)])
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(
+            input_size=ref_enc_filters[-1] * out_channels,
+            hidden_size=256 // 2,
+            batch_first=True,
+        )
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(self, inputs):
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0)).unsqueeze(-1)
+
+    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class Quantizer_module(torch.nn.Module):
+    def __init__(self, n_e, e_dim):
+        super(Quantizer_module, self).__init__()
+        self.embedding = nn.Embedding(n_e, e_dim)
+        self.embedding.weight.data.uniform_(-1.0 / n_e, 1.0 / n_e)
+
+    def forward(self, x):
+        d = (
+            torch.sum(x**2, 1, keepdim=True)
+            + torch.sum(self.embedding.weight**2, 1)
+            - 2 * torch.matmul(x, self.embedding.weight.T)
+        )
+        min_indicies = torch.argmin(d, 1)
+        z_q = self.embedding(min_indicies)
+        return z_q, min_indicies
+
+
+class Quantizer(torch.nn.Module):
+    def __init__(self, embed_dim=512, n_code_groups=4, n_codes=160):
+        super(Quantizer, self).__init__()
+        assert embed_dim % n_code_groups == 0
+        self.quantizer_modules = nn.ModuleList(
+            [Quantizer_module(n_codes, embed_dim // n_code_groups) for _ in range(n_code_groups)]
+        )
+        self.n_code_groups = n_code_groups
+        self.embed_dim = embed_dim
+
+    def forward(self, xin):
+        # B, C, T
+        B, C, T = xin.shape
+        xin = xin.transpose(1, 2)
+        x = xin.reshape(-1, self.embed_dim)
+        x = torch.split(x, self.embed_dim // self.n_code_groups, dim=-1)
+        min_indicies = []
+        z_q = []
+        for _x, m in zip(x, self.quantizer_modules):
+            _z_q, _min_indicies = m(_x)
+            z_q.append(_z_q)
+            min_indicies.append(_min_indicies)  # B * T,
+        z_q = torch.cat(z_q, -1).reshape(xin.shape)
+        loss = 0.25 * torch.mean((z_q.detach() - xin) ** 2) + torch.mean((z_q - xin.detach()) ** 2)
+        z_q = xin + (z_q - xin).detach()
+        z_q = z_q.transpose(1, 2)
+        codes = torch.stack(min_indicies, -1).reshape(B, T, self.n_code_groups)
+        return z_q, loss, codes.transpose(1, 2)
+
+    def embed(self, x):
+        # idx: N, 4, T
+        x = x.transpose(1, 2)
+        x = torch.split(x, 1, 2)
+        ret = []
+        for q, embed in zip(x, self.quantizer_modules):
+            q = embed.embedding(q.squeeze(-1))
+            ret.append(q)
+        ret = torch.cat(ret, -1)
+        return ret.transpose(1, 2)  # N, C, T
+
+
+class CodePredictor(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        n_q=8,
+        dims=1024,
+        ssl_dim=768,
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+
+        self.vq_proj = nn.Conv1d(ssl_dim, hidden_channels, 1)
+        self.ref_enc = modules.MelStyleEncoder(ssl_dim, style_vector_dim=hidden_channels)
+
+        self.encoder = attentions.Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout)
+
+        self.out_proj = nn.Conv1d(hidden_channels, (n_q - 1) * dims, 1)
+        self.n_q = n_q
+        self.dims = dims
+
+    def forward(self, x, x_mask, refer, codes, infer=False):
+        x = x.detach()
+        x = self.vq_proj(x * x_mask) * x_mask
+        g = self.ref_enc(refer, x_mask)
+        x = x + g
+        x = self.encoder(x * x_mask, x_mask)
+        x = self.out_proj(x * x_mask) * x_mask
+        logits = x.reshape(x.shape[0], self.n_q - 1, self.dims, x.shape[-1]).transpose(2, 3)
+        target = codes[1:].transpose(0, 1)
+        if not infer:
+            logits = logits.reshape(-1, self.dims)
+            target = target.reshape(-1)
+            loss = torch.nn.functional.cross_entropy(logits, target)
+            return loss
+        else:
+            _, top10_preds = torch.topk(logits, 10, dim=-1)
+            correct_top10 = torch.any(top10_preds == target.unsqueeze(-1), dim=-1)
+            top3_acc = 100 * torch.mean(correct_top10.float()).detach().cpu().item()
+
+            print("Top-10 Accuracy:", top3_acc, "%")
+
+            pred_codes = torch.argmax(logits, dim=-1)
+            acc = 100 * torch.mean((pred_codes == target).float()).detach().cpu().item()
+            print("Top-1 Accuracy:", acc, "%")
+
+            return pred_codes.transpose(0, 1)
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+        self,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        n_speakers=0,
+        gin_channels=0,
+        use_sdp=True,
+        semantic_frame_rate=None,
+        freeze_quantizer=None,
+        **kwargs
+    ):
+        super().__init__()
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+
+        self.use_sdp = use_sdp
+        self.enc_p = TextEncoder(
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
+
+        self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)
+
+        ssl_dim = 768
+        assert semantic_frame_rate in ["25hz", "50hz"]
+        self.semantic_frame_rate = semantic_frame_rate
+        if semantic_frame_rate == "25hz":
+            self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 2, stride=2)
+        else:
+            self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1)
+
+        self.quantizer = ResidualVectorQuantizer(dimension=ssl_dim, n_q=1, bins=1024)
+        self.freeze_quantizer = freeze_quantizer
+        # if freeze_quantizer:
+        #     self.ssl_proj.requires_grad_(False)
+        #     self.quantizer.requires_grad_(False)
+        # self.quantizer.eval()
+        # self.enc_p.text_embedding.requires_grad_(False)
+        # self.enc_p.encoder_text.requires_grad_(False)
+        # self.enc_p.mrte.requires_grad_(False)
+
+    def forward(self, ssl, y, y_lengths, text, text_lengths):
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype)
+        ge = self.ref_enc(y * y_mask, y_mask)
+
+        with autocast(enabled=False):
+            maybe_no_grad = torch.no_grad() if self.freeze_quantizer else contextlib.nullcontext
+            with maybe_no_grad:
+                if self.freeze_quantizer:
+                    self.ssl_proj.eval()
+                    self.quantizer.eval()
+            ssl = self.ssl_proj(ssl)
+            quantized, codes, commit_loss, quantized_list = self.quantizer(ssl, layers=[0])
+
+        if self.semantic_frame_rate == "25hz":
+            quantized = F.interpolate(quantized, size=int(quantized.shape[-1] * 2), mode="nearest")
+
+        x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
+        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=ge)
+        z_p = self.flow(z, y_mask, g=ge)
+
+        z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
+        o = self.dec(z_slice, g=ge)
+        return (
+            o,
+            commit_loss,
+            ids_slice,
+            y_mask,
+            y_mask,
+            (z, z_p, m_p, logs_p, m_q, logs_q),
+            quantized,
+        )
+
+    def infer(self, ssl, y, y_lengths, text, text_lengths, test=None, noise_scale=0.5):
+        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(y.dtype)
+        ge = self.ref_enc(y * y_mask, y_mask)
+
+        ssl = self.ssl_proj(ssl)
+        quantized, codes, commit_loss, _ = self.quantizer(ssl, layers=[0])
+        if self.semantic_frame_rate == "25hz":
+            quantized = F.interpolate(quantized, size=int(quantized.shape[-1] * 2), mode="nearest")
+
+        x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge, test=test)
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+
+        z = self.flow(z_p, y_mask, g=ge, reverse=True)
+
+        o = self.dec((z * y_mask)[:, :, :], g=ge)
+        return o, y_mask, (z, z_p, m_p, logs_p)
+
+    @torch.no_grad()
+    def decode(self, codes, text, refer, noise_scale=0.5):
+        ge = None
+        if refer is not None:
+            refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
+            refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, refer.size(2)), 1).to(refer.dtype)
+            ge = self.ref_enc(refer * refer_mask, refer_mask)
+
+        y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device)
+        text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
+
+        quantized = self.quantizer.decode(codes)
+        if self.semantic_frame_rate == "25hz":
+            quantized = F.interpolate(quantized, size=int(quantized.shape[-1] * 2), mode="nearest")
+
+        x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+
+        z = self.flow(z_p, y_mask, g=ge, reverse=True)
+
+        o = self.dec((z * y_mask)[:, :, :], g=ge)
+        return o
+
+    def extract_latent(self, x):
+        ssl = self.ssl_proj(x)
+        quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
+        return codes.transpose(0, 1)
diff --git a/utils/tts/gpt_sovits/module/modules.py b/utils/tts/gpt_sovits/module/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..58648ced365e1ddcb7bb53a54431f0ebc28d2245
--- /dev/null
+++ b/utils/tts/gpt_sovits/module/modules.py
@@ -0,0 +1,923 @@
+import math
+
+import numpy as np
+import torch
+import torch.distributions as D
+from torch import nn
+from torch.nn import Conv1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, weight_norm
+
+from utils.tts.gpt_sovits.module import commons
+from utils.tts.gpt_sovits.module.commons import get_padding, init_weights
+from utils.tts.gpt_sovits.module.transforms import \
+    piecewise_rational_quadratic_transform
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+class ConvReluNorm(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        hidden_channels,
+        out_channels,
+        kernel_size,
+        n_layers,
+        p_dropout,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.hidden_channels = hidden_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+        assert n_layers > 1, "Number of layers should be larger than 0."
+
+        self.conv_layers = nn.ModuleList()
+        self.norm_layers = nn.ModuleList()
+        self.conv_layers.append(
+            nn.Conv1d(
+                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
+            )
+        )
+        self.norm_layers.append(LayerNorm(hidden_channels))
+        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
+        for _ in range(n_layers - 1):
+            self.conv_layers.append(
+                nn.Conv1d(
+                    hidden_channels,
+                    hidden_channels,
+                    kernel_size,
+                    padding=kernel_size // 2,
+                )
+            )
+            self.norm_layers.append(LayerNorm(hidden_channels))
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask):
+        x_org = x
+        for i in range(self.n_layers):
+            x = self.conv_layers[i](x * x_mask)
+            x = self.norm_layers[i](x)
+            x = self.relu_drop(x)
+        x = x_org + self.proj(x)
+        return x * x_mask
+
+
+class DDSConv(nn.Module):
+    """
+    Dialted and Depth-Separable Convolution
+    """
+
+    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
+        super().__init__()
+        self.channels = channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+
+        self.drop = nn.Dropout(p_dropout)
+        self.convs_sep = nn.ModuleList()
+        self.convs_1x1 = nn.ModuleList()
+        self.norms_1 = nn.ModuleList()
+        self.norms_2 = nn.ModuleList()
+        for i in range(n_layers):
+            dilation = kernel_size**i
+            padding = (kernel_size * dilation - dilation) // 2
+            self.convs_sep.append(
+                nn.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    groups=channels,
+                    dilation=dilation,
+                    padding=padding,
+                )
+            )
+            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+            self.norms_1.append(LayerNorm(channels))
+            self.norms_2.append(LayerNorm(channels))
+
+    def forward(self, x, x_mask, g=None):
+        if g is not None:
+            x = x + g
+        for i in range(self.n_layers):
+            y = self.convs_sep[i](x * x_mask)
+            y = self.norms_1[i](y)
+            y = F.gelu(y)
+            y = self.convs_1x1[i](y)
+            y = self.norms_2[i](y)
+            y = F.gelu(y)
+            y = self.drop(y)
+            x = x + y
+        return x * x_mask
+
+
+class WN(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+        p_dropout=0,
+    ):
+        super(WN, self).__init__()
+        assert kernel_size % 2 == 1
+        self.hidden_channels = hidden_channels
+        self.kernel_size = (kernel_size,)
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+        self.p_dropout = p_dropout
+
+        self.in_layers = torch.nn.ModuleList()
+        self.res_skip_layers = torch.nn.ModuleList()
+        self.drop = nn.Dropout(p_dropout)
+
+        if gin_channels != 0:
+            cond_layer = torch.nn.Conv1d(
+                gin_channels, 2 * hidden_channels * n_layers, 1
+            )
+            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
+
+        for i in range(n_layers):
+            dilation = dilation_rate**i
+            padding = int((kernel_size * dilation - dilation) / 2)
+            in_layer = torch.nn.Conv1d(
+                hidden_channels,
+                2 * hidden_channels,
+                kernel_size,
+                dilation=dilation,
+                padding=padding,
+            )
+            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
+            self.in_layers.append(in_layer)
+
+            # last one is not necessary
+            if i < n_layers - 1:
+                res_skip_channels = 2 * hidden_channels
+            else:
+                res_skip_channels = hidden_channels
+
+            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
+            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
+            self.res_skip_layers.append(res_skip_layer)
+
+    def forward(self, x, x_mask, g=None, **kwargs):
+        output = torch.zeros_like(x)
+        n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+        if g is not None:
+            g = self.cond_layer(g)
+
+        for i in range(self.n_layers):
+            x_in = self.in_layers[i](x)
+            if g is not None:
+                cond_offset = i * 2 * self.hidden_channels
+                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
+            else:
+                g_l = torch.zeros_like(x_in)
+
+            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
+            acts = self.drop(acts)
+
+            res_skip_acts = self.res_skip_layers[i](acts)
+            if i < self.n_layers - 1:
+                res_acts = res_skip_acts[:, : self.hidden_channels, :]
+                x = (x + res_acts) * x_mask
+                output = output + res_skip_acts[:, self.hidden_channels :, :]
+            else:
+                output = output + res_skip_acts
+        return output * x_mask
+
+    def remove_weight_norm(self):
+        if self.gin_channels != 0:
+            torch.nn.utils.remove_weight_norm(self.cond_layer)
+        for l in self.in_layers:
+            torch.nn.utils.remove_weight_norm(l)
+        for l in self.res_skip_layers:
+            torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[2],
+                        padding=get_padding(kernel_size, dilation[2]),
+                    )
+                ),
+            ]
+        )
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+            ]
+        )
+        self.convs2.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+            ]
+        )
+        self.convs.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+            logdet = torch.sum(-y, [1, 2])
+            return y, logdet
+        else:
+            x = torch.exp(x) * x_mask
+            return x
+
+
+class Flip(nn.Module):
+    def forward(self, x, *args, reverse=False, **kwargs):
+        x = torch.flip(x, [1])
+        if not reverse:
+            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+            return x, logdet
+        else:
+            return x
+
+
+class ElementwiseAffine(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.channels = channels
+        self.m = nn.Parameter(torch.zeros(channels, 1))
+        self.logs = nn.Parameter(torch.zeros(channels, 1))
+
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = self.m + torch.exp(self.logs) * x
+            y = y * x_mask
+            logdet = torch.sum(self.logs * x_mask, [1, 2])
+            return y, logdet
+        else:
+            x = (x - self.m) * torch.exp(-self.logs) * x_mask
+            return x
+
+
+class ResidualCouplingLayer(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        p_dropout=0,
+        gin_channels=0,
+        mean_only=False,
+    ):
+        assert channels % 2 == 0, "channels should be divisible by 2"
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.half_channels = channels // 2
+        self.mean_only = mean_only
+
+        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+        self.enc = WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            p_dropout=p_dropout,
+            gin_channels=gin_channels,
+        )
+        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+        self.post.weight.data.zero_()
+        self.post.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0) * x_mask
+        h = self.enc(h, x_mask, g=g)
+        stats = self.post(h) * x_mask
+        if not self.mean_only:
+            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
+        else:
+            m = stats
+            logs = torch.zeros_like(m)
+
+        if not reverse:
+            x1 = m + x1 * torch.exp(logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            logdet = torch.sum(logs, [1, 2])
+            return x, logdet
+        else:
+            x1 = (x1 - m) * torch.exp(-logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            return x
+
+
+class ConvFlow(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        n_layers,
+        num_bins=10,
+        tail_bound=5.0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.num_bins = num_bins
+        self.tail_bound = tail_bound
+        self.half_channels = in_channels // 2
+
+        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
+        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
+        self.proj = nn.Conv1d(
+            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
+        )
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0)
+        h = self.convs(h, x_mask, g=g)
+        h = self.proj(h) * x_mask
+
+        b, c, t = x0.shape
+        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
+
+        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
+        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
+            self.filter_channels
+        )
+        unnormalized_derivatives = h[..., 2 * self.num_bins :]
+
+        x1, logabsdet = piecewise_rational_quadratic_transform(
+            x1,
+            unnormalized_widths,
+            unnormalized_heights,
+            unnormalized_derivatives,
+            inverse=reverse,
+            tails="linear",
+            tail_bound=self.tail_bound,
+        )
+
+        x = torch.cat([x0, x1], 1) * x_mask
+        logdet = torch.sum(logabsdet * x_mask, [1, 2])
+        if not reverse:
+            return x, logdet
+        else:
+            return x
+
+
+class LinearNorm(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        bias=True,
+        spectral_norm=False,
+    ):
+        super(LinearNorm, self).__init__()
+        self.fc = nn.Linear(in_channels, out_channels, bias)
+
+        if spectral_norm:
+            self.fc = nn.utils.spectral_norm(self.fc)
+
+    def forward(self, input):
+        out = self.fc(input)
+        return out
+
+
+class Mish(nn.Module):
+    def __init__(self):
+        super(Mish, self).__init__()
+
+    def forward(self, x):
+        return x * torch.tanh(F.softplus(x))
+
+
+class Conv1dGLU(nn.Module):
+    """
+    Conv1d + GLU(Gated Linear Unit) with residual connection.
+    For GLU refer to https://arxiv.org/abs/1612.08083 paper.
+    """
+
+    def __init__(self, in_channels, out_channels, kernel_size, dropout):
+        super(Conv1dGLU, self).__init__()
+        self.out_channels = out_channels
+        self.conv1 = ConvNorm(in_channels, 2 * out_channels, kernel_size=kernel_size)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        residual = x
+        x = self.conv1(x)
+        x1, x2 = torch.split(x, split_size_or_sections=self.out_channels, dim=1)
+        x = x1 * torch.sigmoid(x2)
+        x = residual + self.dropout(x)
+        return x
+
+
+class ConvNorm(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size=1,
+        stride=1,
+        padding=None,
+        dilation=1,
+        bias=True,
+        spectral_norm=False,
+    ):
+        super(ConvNorm, self).__init__()
+
+        if padding is None:
+            assert kernel_size % 2 == 1
+            padding = int(dilation * (kernel_size - 1) / 2)
+
+        self.conv = torch.nn.Conv1d(
+            in_channels,
+            out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias,
+        )
+
+        if spectral_norm:
+            self.conv = nn.utils.spectral_norm(self.conv)
+
+    def forward(self, input):
+        out = self.conv(input)
+        return out
+
+
+class MultiHeadAttention(nn.Module):
+    """Multi-Head Attention module"""
+
+    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.0, spectral_norm=False):
+        super().__init__()
+
+        self.n_head = n_head
+        self.d_k = d_k
+        self.d_v = d_v
+
+        self.w_qs = nn.Linear(d_model, n_head * d_k)
+        self.w_ks = nn.Linear(d_model, n_head * d_k)
+        self.w_vs = nn.Linear(d_model, n_head * d_v)
+
+        self.attention = ScaledDotProductAttention(
+            temperature=np.power(d_model, 0.5), dropout=dropout
+        )
+
+        self.fc = nn.Linear(n_head * d_v, d_model)
+        self.dropout = nn.Dropout(dropout)
+
+        if spectral_norm:
+            self.w_qs = nn.utils.spectral_norm(self.w_qs)
+            self.w_ks = nn.utils.spectral_norm(self.w_ks)
+            self.w_vs = nn.utils.spectral_norm(self.w_vs)
+            self.fc = nn.utils.spectral_norm(self.fc)
+
+    def forward(self, x, mask=None):
+        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
+        sz_b, len_x, _ = x.size()
+
+        residual = x
+
+        q = self.w_qs(x).view(sz_b, len_x, n_head, d_k)
+        k = self.w_ks(x).view(sz_b, len_x, n_head, d_k)
+        v = self.w_vs(x).view(sz_b, len_x, n_head, d_v)
+        q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_k)  # (n*b) x lq x dk
+        k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_k)  # (n*b) x lk x dk
+        v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_v)  # (n*b) x lv x dv
+
+        if mask is not None:
+            slf_mask = mask.repeat(n_head, 1, 1)  # (n*b) x .. x ..
+        else:
+            slf_mask = None
+        output, attn = self.attention(q, k, v, mask=slf_mask)
+
+        output = output.view(n_head, sz_b, len_x, d_v)
+        output = (
+            output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_x, -1)
+        )  # b x lq x (n*dv)
+
+        output = self.fc(output)
+
+        output = self.dropout(output) + residual
+        return output, attn
+
+
+class ScaledDotProductAttention(nn.Module):
+    """Scaled Dot-Product Attention"""
+
+    def __init__(self, temperature, dropout):
+        super().__init__()
+        self.temperature = temperature
+        self.softmax = nn.Softmax(dim=2)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, q, k, v, mask=None):
+        attn = torch.bmm(q, k.transpose(1, 2))
+        attn = attn / self.temperature
+
+        if mask is not None:
+            attn = attn.masked_fill(mask, -np.inf)
+
+        attn = self.softmax(attn)
+        p_attn = self.dropout(attn)
+
+        output = torch.bmm(p_attn, v)
+        return output, attn
+
+
+class MelStyleEncoder(nn.Module):
+    """MelStyleEncoder"""
+
+    def __init__(
+        self,
+        n_mel_channels=80,
+        style_hidden=128,
+        style_vector_dim=256,
+        style_kernel_size=5,
+        style_head=2,
+        dropout=0.1,
+    ):
+        super(MelStyleEncoder, self).__init__()
+        self.in_dim = n_mel_channels
+        self.hidden_dim = style_hidden
+        self.out_dim = style_vector_dim
+        self.kernel_size = style_kernel_size
+        self.n_head = style_head
+        self.dropout = dropout
+
+        self.spectral = nn.Sequential(
+            LinearNorm(self.in_dim, self.hidden_dim),
+            Mish(),
+            nn.Dropout(self.dropout),
+            LinearNorm(self.hidden_dim, self.hidden_dim),
+            Mish(),
+            nn.Dropout(self.dropout),
+        )
+
+        self.temporal = nn.Sequential(
+            Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout),
+            Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout),
+        )
+
+        self.slf_attn = MultiHeadAttention(
+            self.n_head,
+            self.hidden_dim,
+            self.hidden_dim // self.n_head,
+            self.hidden_dim // self.n_head,
+            self.dropout,
+        )
+
+        self.fc = LinearNorm(self.hidden_dim, self.out_dim)
+
+    def temporal_avg_pool(self, x, mask=None):
+        if mask is None:
+            out = torch.mean(x, dim=1)
+        else:
+            len_ = (~mask).sum(dim=1).unsqueeze(1)
+            x = x.masked_fill(mask.unsqueeze(-1), 0)
+            x = x.sum(dim=1)
+            out = torch.div(x, len_)
+        return out
+
+    def forward(self, x, mask=None):
+        x = x.transpose(1, 2)
+        if mask is not None:
+            mask = (mask.int() == 0).squeeze(1)
+        max_len = x.shape[1]
+        slf_attn_mask = (
+            mask.unsqueeze(1).expand(-1, max_len, -1) if mask is not None else None
+        )
+
+        # spectral
+        x = self.spectral(x)
+        # temporal
+        x = x.transpose(1, 2)
+        x = self.temporal(x)
+        x = x.transpose(1, 2)
+        # self-attention
+        if mask is not None:
+            x = x.masked_fill(mask.unsqueeze(-1), 0)
+        x, _ = self.slf_attn(x, mask=slf_attn_mask)
+        # fc
+        x = self.fc(x)
+        # temoral average pooling
+        w = self.temporal_avg_pool(x, mask=mask)
+
+        return w.unsqueeze(-1)
+
+
+class MelStyleEncoderVAE(nn.Module):
+    def __init__(self, spec_channels, z_latent_dim, emb_dim):
+        super().__init__()
+        self.ref_encoder = MelStyleEncoder(spec_channels, style_vector_dim=emb_dim)
+        self.fc1 = nn.Linear(emb_dim, z_latent_dim)
+        self.fc2 = nn.Linear(emb_dim, z_latent_dim)
+        self.fc3 = nn.Linear(z_latent_dim, emb_dim)
+        self.z_latent_dim = z_latent_dim
+
+    def reparameterize(self, mu, logvar):
+        if self.training:
+            std = torch.exp(0.5 * logvar)
+            eps = torch.randn_like(std)
+            return eps.mul(std).add_(mu)
+        else:
+            return mu
+
+    def forward(self, inputs, mask=None):
+        enc_out = self.ref_encoder(inputs.squeeze(-1), mask).squeeze(-1)
+        mu = self.fc1(enc_out)
+        logvar = self.fc2(enc_out)
+        posterior = D.Normal(mu, torch.exp(logvar))
+        kl_divergence = D.kl_divergence(
+            posterior, D.Normal(torch.zeros_like(mu), torch.ones_like(logvar))
+        )
+        loss_kl = kl_divergence.mean()
+
+        z = posterior.rsample()
+        style_embed = self.fc3(z)
+
+        return style_embed.unsqueeze(-1), loss_kl
+
+    def infer(self, inputs=None, random_sample=False, manual_latent=None):
+        if manual_latent is None:
+            if random_sample:
+                dev = next(self.parameters()).device
+                posterior = D.Normal(
+                    torch.zeros(1, self.z_latent_dim, device=dev),
+                    torch.ones(1, self.z_latent_dim, device=dev),
+                )
+                z = posterior.rsample()
+            else:
+                enc_out = self.ref_encoder(inputs.transpose(1, 2))
+                mu = self.fc1(enc_out)
+                z = mu
+        else:
+            z = manual_latent
+        style_embed = self.fc3(z)
+        return style_embed.unsqueeze(-1), z
+
+
+class ActNorm(nn.Module):
+    def __init__(self, channels, ddi=False, **kwargs):
+        super().__init__()
+        self.channels = channels
+        self.initialized = not ddi
+
+        self.logs = nn.Parameter(torch.zeros(1, channels, 1))
+        self.bias = nn.Parameter(torch.zeros(1, channels, 1))
+
+    def forward(self, x, x_mask=None, g=None, reverse=False, **kwargs):
+        if x_mask is None:
+            x_mask = torch.ones(x.size(0), 1, x.size(2)).to(
+                device=x.device, dtype=x.dtype
+            )
+        x_len = torch.sum(x_mask, [1, 2])
+        if not self.initialized:
+            self.initialize(x, x_mask)
+            self.initialized = True
+
+        if reverse:
+            z = (x - self.bias) * torch.exp(-self.logs) * x_mask
+            logdet = None
+            return z
+        else:
+            z = (self.bias + torch.exp(self.logs) * x) * x_mask
+            logdet = torch.sum(self.logs) * x_len  # [b]
+            return z, logdet
+
+    def store_inverse(self):
+        pass
+
+    def set_ddi(self, ddi):
+        self.initialized = not ddi
+
+    def initialize(self, x, x_mask):
+        with torch.no_grad():
+            denom = torch.sum(x_mask, [0, 2])
+            m = torch.sum(x * x_mask, [0, 2]) / denom
+            m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom
+            v = m_sq - (m**2)
+            logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6))
+
+            bias_init = (
+                (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype)
+            )
+            logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype)
+
+            self.bias.data.copy_(bias_init)
+            self.logs.data.copy_(logs_init)
+
+
+class InvConvNear(nn.Module):
+    def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
+        super().__init__()
+        assert n_split % 2 == 0
+        self.channels = channels
+        self.n_split = n_split
+        self.no_jacobian = no_jacobian
+
+        w_init = torch.linalg.qr(
+            torch.FloatTensor(self.n_split, self.n_split).normal_()
+        )[0]
+        if torch.det(w_init) < 0:
+            w_init[:, 0] = -1 * w_init[:, 0]
+        self.weight = nn.Parameter(w_init)
+
+    def forward(self, x, x_mask=None, g=None, reverse=False, **kwargs):
+        b, c, t = x.size()
+        assert c % self.n_split == 0
+        if x_mask is None:
+            x_mask = 1
+            x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t
+        else:
+            x_len = torch.sum(x_mask, [1, 2])
+
+        x = x.view(b, 2, c // self.n_split, self.n_split // 2, t)
+        x = (
+            x.permute(0, 1, 3, 2, 4)
+            .contiguous()
+            .view(b, self.n_split, c // self.n_split, t)
+        )
+
+        if reverse:
+            if hasattr(self, "weight_inv"):
+                weight = self.weight_inv
+            else:
+                weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)
+            logdet = None
+        else:
+            weight = self.weight
+            if self.no_jacobian:
+                logdet = 0
+            else:
+                logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len  # [b]
+
+        weight = weight.view(self.n_split, self.n_split, 1, 1)
+        z = F.conv2d(x, weight)
+
+        z = z.view(b, 2, self.n_split // 2, c // self.n_split, t)
+        z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask
+        if reverse:
+            return z
+        else:
+            return z, logdet
+
+    def store_inverse(self):
+        self.weight_inv = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)
diff --git a/utils/tts/gpt_sovits/module/mrte_model.py b/utils/tts/gpt_sovits/module/mrte_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e2b56e78863de936f088562de856288d4449020
--- /dev/null
+++ b/utils/tts/gpt_sovits/module/mrte_model.py
@@ -0,0 +1,174 @@
+# This is Multi-reference timbre encoder
+
+import torch
+from torch import nn
+from torch.nn.utils import remove_weight_norm, weight_norm
+
+from utils.tts.gpt_sovits.module.attentions import MultiHeadAttention
+
+
+class MRTE(nn.Module):
+    def __init__(
+        self,
+        content_enc_channels=192,
+        hidden_size=512,
+        out_channels=192,
+        kernel_size=5,
+        n_heads=4,
+        ge_layer=2,
+    ):
+        super(MRTE, self).__init__()
+        self.cross_attention = MultiHeadAttention(hidden_size, hidden_size, n_heads)
+        self.c_pre = nn.Conv1d(content_enc_channels, hidden_size, 1)
+        self.text_pre = nn.Conv1d(content_enc_channels, hidden_size, 1)
+        self.c_post = nn.Conv1d(hidden_size, out_channels, 1)
+
+    def forward(self, ssl_enc, ssl_mask, text, text_mask, ge, test=None):
+        if ge == None:
+            ge = 0
+        attn_mask = text_mask.unsqueeze(2) * ssl_mask.unsqueeze(-1)
+
+        ssl_enc = self.c_pre(ssl_enc * ssl_mask)
+        text_enc = self.text_pre(text * text_mask)
+        if test != None:
+            if test == 0:
+                x = self.cross_attention(ssl_enc * ssl_mask, text_enc * text_mask, attn_mask) + ssl_enc + ge
+            elif test == 1:
+                x = ssl_enc + ge
+            elif test == 2:
+                x = self.cross_attention(ssl_enc * 0 * ssl_mask, text_enc * text_mask, attn_mask) + ge
+            else:
+                raise ValueError("test should be 0,1,2")
+        else:
+            x = self.cross_attention(ssl_enc * ssl_mask, text_enc * text_mask, attn_mask) + ssl_enc + ge
+        x = self.c_post(x * ssl_mask)
+        return x
+
+
+class SpeakerEncoder(torch.nn.Module):
+    def __init__(
+        self,
+        mel_n_channels=80,
+        model_num_layers=2,
+        model_hidden_size=256,
+        model_embedding_size=256,
+    ):
+        super(SpeakerEncoder, self).__init__()
+        self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
+        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
+        self.relu = nn.ReLU()
+
+    def forward(self, mels):
+        self.lstm.flatten_parameters()
+        _, (hidden, _) = self.lstm(mels.transpose(-1, -2))
+        embeds_raw = self.relu(self.linear(hidden[-1]))
+        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
+
+
+class MELEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers)
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+
+    def forward(self, x):
+        # print(x.shape,x_lengths.shape)
+        x = self.pre(x)
+        x = self.enc(x)
+        x = self.proj(x)
+        return x
+
+
+class WN(torch.nn.Module):
+    def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers):
+        super(WN, self).__init__()
+        assert kernel_size % 2 == 1
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+
+        self.in_layers = torch.nn.ModuleList()
+        self.res_skip_layers = torch.nn.ModuleList()
+
+        for i in range(n_layers):
+            dilation = dilation_rate**i
+            padding = int((kernel_size * dilation - dilation) / 2)
+            in_layer = nn.Conv1d(
+                hidden_channels,
+                2 * hidden_channels,
+                kernel_size,
+                dilation=dilation,
+                padding=padding,
+            )
+            in_layer = weight_norm(in_layer)
+            self.in_layers.append(in_layer)
+
+            # last one is not necessary
+            if i < n_layers - 1:
+                res_skip_channels = 2 * hidden_channels
+            else:
+                res_skip_channels = hidden_channels
+
+            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
+            res_skip_layer = weight_norm(res_skip_layer, name="weight")
+            self.res_skip_layers.append(res_skip_layer)
+
+    def forward(self, x):
+        output = torch.zeros_like(x)
+        n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+        for i in range(self.n_layers):
+            x_in = self.in_layers[i](x)
+
+            acts = fused_add_tanh_sigmoid_multiply(x_in, n_channels_tensor)
+
+            res_skip_acts = self.res_skip_layers[i](acts)
+            if i < self.n_layers - 1:
+                res_acts = res_skip_acts[:, : self.hidden_channels, :]
+                x = x + res_acts
+                output = output + res_skip_acts[:, self.hidden_channels :, :]
+            else:
+                output = output + res_skip_acts
+        return output
+
+    def remove_weight_norm(self):
+        for l in self.in_layers:
+            remove_weight_norm(l)
+        for l in self.res_skip_layers:
+            remove_weight_norm(l)
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input, n_channels):
+    n_channels_int = n_channels[0]
+    t_act = torch.tanh(input[:, :n_channels_int, :])
+    s_act = torch.sigmoid(input[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+if __name__ == "__main__":
+    content_enc = torch.randn(3, 192, 100)
+    content_mask = torch.ones(3, 1, 100)
+    ref_mel = torch.randn(3, 128, 30)
+    ref_mask = torch.ones(3, 1, 30)
+    model = MRTE()
+    out = model(content_enc, content_mask, ref_mel, ref_mask)
+    print(out.shape)
diff --git a/utils/tts/gpt_sovits/module/quantize.py b/utils/tts/gpt_sovits/module/quantize.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecf4cf51c1adfb3464a851d06c51e5d6c0a0ad6c
--- /dev/null
+++ b/utils/tts/gpt_sovits/module/quantize.py
@@ -0,0 +1,114 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Residual vector quantizer implementation."""
+
+import typing as tp
+from dataclasses import dataclass, field
+
+import torch
+from torch import nn
+
+from utils.tts.gpt_sovits.module.core_vq import ResidualVectorQuantization
+
+
+@dataclass
+class QuantizedResult:
+    quantized: torch.Tensor
+    codes: torch.Tensor
+    bandwidth: torch.Tensor  # bandwidth in kb/s used, per batch item.
+    penalty: tp.Optional[torch.Tensor] = None
+    metrics: dict = field(default_factory=dict)
+
+
+class ResidualVectorQuantizer(nn.Module):
+    """Residual Vector Quantizer.
+    Args:
+        dimension (int): Dimension of the codebooks.
+        n_q (int): Number of residual vector quantizers used.
+        bins (int): Codebook size.
+        decay (float): Decay for exponential moving average over the codebooks.
+        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
+        kmeans_iters (int): Number of iterations used for kmeans initialization.
+        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
+            that have an exponential moving average cluster size less than the specified threshold with
+            randomly selected vector from the current batch.
+    """
+
+    def __init__(
+        self,
+        dimension: int = 256,
+        n_q: int = 8,
+        bins: int = 1024,
+        decay: float = 0.99,
+        kmeans_init: bool = True,
+        kmeans_iters: int = 50,
+        threshold_ema_dead_code: int = 2,
+    ):
+        super().__init__()
+        self.n_q = n_q
+        self.dimension = dimension
+        self.bins = bins
+        self.decay = decay
+        self.kmeans_init = kmeans_init
+        self.kmeans_iters = kmeans_iters
+        self.threshold_ema_dead_code = threshold_ema_dead_code
+        self.vq = ResidualVectorQuantization(
+            dim=self.dimension,
+            codebook_size=self.bins,
+            num_quantizers=self.n_q,
+            decay=self.decay,
+            kmeans_init=self.kmeans_init,
+            kmeans_iters=self.kmeans_iters,
+            threshold_ema_dead_code=self.threshold_ema_dead_code,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        n_q: tp.Optional[int] = None,
+        layers: tp.Optional[list] = None,
+    ) -> QuantizedResult:
+        """Residual vector quantization on the given input tensor.
+        Args:
+            x (torch.Tensor): Input tensor.
+            n_q (int): Number of quantizer used to quantize. Default: All quantizers.
+            layers (list): Layer that need to return quantized. Defalt: None.
+        Returns:
+            QuantizedResult:
+                The quantized (or approximately quantized) representation with
+                the associated numbert quantizers and layer quantized required to return.
+        """
+        n_q = n_q if n_q else self.n_q
+        if layers and max(layers) >= n_q:
+            raise ValueError(
+                f"Last layer index in layers: A {max(layers)}. Number of quantizers in RVQ: B {self.n_q}. A must less than B."
+            )
+        quantized, codes, commit_loss, quantized_list = self.vq(x, n_q=n_q, layers=layers)
+        return quantized, codes, torch.mean(commit_loss), quantized_list
+
+    def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None, st: tp.Optional[int] = None) -> torch.Tensor:
+        """Encode a given input tensor with the specified sample rate at the given bandwidth.
+        The RVQ encode method sets the appropriate number of quantizer to use
+        and returns indices for each quantizer.
+        Args:
+            x (torch.Tensor): Input tensor.
+            n_q (int): Number of quantizer used to quantize. Default: All quantizers.
+            st (int): Start to encode input from which layers. Default: 0.
+        """
+        n_q = n_q if n_q else self.n_q
+        st = st or 0
+        codes = self.vq.encode(x, n_q=n_q, st=st)
+        return codes
+
+    def decode(self, codes: torch.Tensor, st: int = 0) -> torch.Tensor:
+        """Decode the given codes to the quantized representation.
+        Args:
+            codes (torch.Tensor): Input indices for each quantizer.
+            st (int): Start to decode input codes from which layers. Default: 0.
+        """
+        quantized = self.vq.decode(codes, st=st)
+        return quantized
diff --git a/utils/tts/gpt_sovits/module/transforms.py b/utils/tts/gpt_sovits/module/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..a11f799e023864ff7082c1f49c0cc18351a13b47
--- /dev/null
+++ b/utils/tts/gpt_sovits/module/transforms.py
@@ -0,0 +1,209 @@
+import torch
+from torch.nn import functional as F
+
+import numpy as np
+
+
+DEFAULT_MIN_BIN_WIDTH = 1e-3
+DEFAULT_MIN_BIN_HEIGHT = 1e-3
+DEFAULT_MIN_DERIVATIVE = 1e-3
+
+
+def piecewise_rational_quadratic_transform(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    tails=None,
+    tail_bound=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    if tails is None:
+        spline_fn = rational_quadratic_spline
+        spline_kwargs = {}
+    else:
+        spline_fn = unconstrained_rational_quadratic_spline
+        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
+
+    outputs, logabsdet = spline_fn(
+        inputs=inputs,
+        unnormalized_widths=unnormalized_widths,
+        unnormalized_heights=unnormalized_heights,
+        unnormalized_derivatives=unnormalized_derivatives,
+        inverse=inverse,
+        min_bin_width=min_bin_width,
+        min_bin_height=min_bin_height,
+        min_derivative=min_derivative,
+        **spline_kwargs
+    )
+    return outputs, logabsdet
+
+
+def searchsorted(bin_locations, inputs, eps=1e-6):
+    bin_locations[..., -1] += eps
+    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
+
+
+def unconstrained_rational_quadratic_spline(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    tails="linear",
+    tail_bound=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
+    outside_interval_mask = ~inside_interval_mask
+
+    outputs = torch.zeros_like(inputs)
+    logabsdet = torch.zeros_like(inputs)
+
+    if tails == "linear":
+        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
+        constant = np.log(np.exp(1 - min_derivative) - 1)
+        unnormalized_derivatives[..., 0] = constant
+        unnormalized_derivatives[..., -1] = constant
+
+        outputs[outside_interval_mask] = inputs[outside_interval_mask]
+        logabsdet[outside_interval_mask] = 0
+    else:
+        raise RuntimeError("{} tails are not implemented.".format(tails))
+
+    (
+        outputs[inside_interval_mask],
+        logabsdet[inside_interval_mask],
+    ) = rational_quadratic_spline(
+        inputs=inputs[inside_interval_mask],
+        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
+        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
+        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
+        inverse=inverse,
+        left=-tail_bound,
+        right=tail_bound,
+        bottom=-tail_bound,
+        top=tail_bound,
+        min_bin_width=min_bin_width,
+        min_bin_height=min_bin_height,
+        min_derivative=min_derivative,
+    )
+
+    return outputs, logabsdet
+
+
+def rational_quadratic_spline(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    left=0.0,
+    right=1.0,
+    bottom=0.0,
+    top=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    if torch.min(inputs) < left or torch.max(inputs) > right:
+        raise ValueError("Input to a transform is not within its domain")
+
+    num_bins = unnormalized_widths.shape[-1]
+
+    if min_bin_width * num_bins > 1.0:
+        raise ValueError("Minimal bin width too large for the number of bins")
+    if min_bin_height * num_bins > 1.0:
+        raise ValueError("Minimal bin height too large for the number of bins")
+
+    widths = F.softmax(unnormalized_widths, dim=-1)
+    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
+    cumwidths = torch.cumsum(widths, dim=-1)
+    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
+    cumwidths = (right - left) * cumwidths + left
+    cumwidths[..., 0] = left
+    cumwidths[..., -1] = right
+    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
+
+    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
+
+    heights = F.softmax(unnormalized_heights, dim=-1)
+    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
+    cumheights = torch.cumsum(heights, dim=-1)
+    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
+    cumheights = (top - bottom) * cumheights + bottom
+    cumheights[..., 0] = bottom
+    cumheights[..., -1] = top
+    heights = cumheights[..., 1:] - cumheights[..., :-1]
+
+    if inverse:
+        bin_idx = searchsorted(cumheights, inputs)[..., None]
+    else:
+        bin_idx = searchsorted(cumwidths, inputs)[..., None]
+
+    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
+    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
+
+    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
+    delta = heights / widths
+    input_delta = delta.gather(-1, bin_idx)[..., 0]
+
+    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
+    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
+
+    input_heights = heights.gather(-1, bin_idx)[..., 0]
+
+    if inverse:
+        a = (inputs - input_cumheights) * (
+            input_derivatives + input_derivatives_plus_one - 2 * input_delta
+        ) + input_heights * (input_delta - input_derivatives)
+        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
+            input_derivatives + input_derivatives_plus_one - 2 * input_delta
+        )
+        c = -input_delta * (inputs - input_cumheights)
+
+        discriminant = b.pow(2) - 4 * a * c
+        assert (discriminant >= 0).all()
+
+        root = (2 * c) / (-b - torch.sqrt(discriminant))
+        outputs = root * input_bin_widths + input_cumwidths
+
+        theta_one_minus_theta = root * (1 - root)
+        denominator = input_delta + (
+            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+            * theta_one_minus_theta
+        )
+        derivative_numerator = input_delta.pow(2) * (
+            input_derivatives_plus_one * root.pow(2)
+            + 2 * input_delta * theta_one_minus_theta
+            + input_derivatives * (1 - root).pow(2)
+        )
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, -logabsdet
+    else:
+        theta = (inputs - input_cumwidths) / input_bin_widths
+        theta_one_minus_theta = theta * (1 - theta)
+
+        numerator = input_heights * (
+            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
+        )
+        denominator = input_delta + (
+            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+            * theta_one_minus_theta
+        )
+        outputs = input_cumheights + numerator / denominator
+
+        derivative_numerator = input_delta.pow(2) * (
+            input_derivatives_plus_one * theta.pow(2)
+            + 2 * input_delta * theta_one_minus_theta
+            + input_derivatives * (1 - theta).pow(2)
+        )
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, logabsdet
diff --git a/utils/tts/gpt_sovits/text/__init__.py b/utils/tts/gpt_sovits/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..84a25c7afd78fca00ff53ea8c917a014770e6edf
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/__init__.py
@@ -0,0 +1,14 @@
+from utils.tts.gpt_sovits.text.symbols import *
+
+_symbol_to_id = {s: i for i, s in enumerate(symbols)}
+
+
+def cleaned_text_to_sequence(cleaned_text):
+    """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+    Args:
+      text: string to convert to a sequence
+    Returns:
+      List of integers corresponding to the symbols in the text
+    """
+    phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
+    return phones
diff --git a/utils/tts/gpt_sovits/text/__pycache__/__init__.cpython-310.pyc b/utils/tts/gpt_sovits/text/__pycache__/__init__.cpython-310.pyc
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diff --git a/utils/tts/gpt_sovits/text/__pycache__/tone_sandhi.cpython-310.pyc b/utils/tts/gpt_sovits/text/__pycache__/tone_sandhi.cpython-310.pyc
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diff --git a/utils/tts/gpt_sovits/text/chinese.py b/utils/tts/gpt_sovits/text/chinese.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ac54a3e4116c84cd218da8989ebb7b5d989f5fe
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/chinese.py
@@ -0,0 +1,173 @@
+import os
+import re
+
+import cn2an
+from pypinyin import lazy_pinyin, Style
+
+from utils.tts.gpt_sovits.text.symbols import punctuation
+from utils.tts.gpt_sovits.text.tone_sandhi import ToneSandhi
+from utils.tts.gpt_sovits.text.zh_normalization.text_normlization import TextNormalizer
+
+normalizer = lambda x: cn2an.transform(x, "an2cn")
+
+current_file_path = os.path.dirname(__file__)
+pinyin_to_symbol_map = {
+    line.split("\t")[0]: line.strip().split("\t")[1]
+    for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines()
+}
+
+import jieba_fast.posseg as psg
+
+
+rep_map = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "·": ",",
+    "、": ",",
+    "...": "…",
+    "$": ".",
+    "/": ",",
+    "—": "-",
+    "~": "…",
+    "～":"…",
+}
+
+tone_modifier = ToneSandhi()
+
+
+def replace_punctuation(text):
+    text = text.replace("嗯", "恩").replace("呣", "母")
+    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
+
+    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
+
+    replaced_text = re.sub(
+        r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text
+    )
+
+    return replaced_text
+
+
+def g2p(text):
+    pattern = r"(?<=[{0}])\s*".format("".join(punctuation))
+    sentences = [i for i in re.split(pattern, text) if i.strip() != ""]
+    phones, word2ph = _g2p(sentences)
+    return phones, word2ph
+
+
+def _get_initials_finals(word):
+    initials = []
+    finals = []
+    orig_initials = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.INITIALS)
+    orig_finals = lazy_pinyin(
+        word, neutral_tone_with_five=True, style=Style.FINALS_TONE3
+    )
+    for c, v in zip(orig_initials, orig_finals):
+        initials.append(c)
+        finals.append(v)
+    return initials, finals
+
+
+def _g2p(segments):
+    phones_list = []
+    word2ph = []
+    for seg in segments:
+        pinyins = []
+        # Replace all English words in the sentence
+        seg = re.sub("[a-zA-Z]+", "", seg)
+        seg_cut = psg.lcut(seg)
+        initials = []
+        finals = []
+        seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
+        for word, pos in seg_cut:
+            if pos == "eng":
+                continue
+            sub_initials, sub_finals = _get_initials_finals(word)
+            sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
+            initials.append(sub_initials)
+            finals.append(sub_finals)
+
+            # assert len(sub_initials) == len(sub_finals) == len(word)
+        initials = sum(initials, [])
+        finals = sum(finals, [])
+        #
+        for c, v in zip(initials, finals):
+            raw_pinyin = c + v
+            # NOTE: post process for pypinyin outputs
+            # we discriminate i, ii and iii
+            if c == v:
+                assert c in punctuation
+                phone = [c]
+                word2ph.append(1)
+            else:
+                v_without_tone = v[:-1]
+                tone = v[-1]
+
+                pinyin = c + v_without_tone
+                assert tone in "12345"
+
+                if c:
+                    # 多音节
+                    v_rep_map = {
+                        "uei": "ui",
+                        "iou": "iu",
+                        "uen": "un",
+                    }
+                    if v_without_tone in v_rep_map.keys():
+                        pinyin = c + v_rep_map[v_without_tone]
+                else:
+                    # 单音节
+                    pinyin_rep_map = {
+                        "ing": "ying",
+                        "i": "yi",
+                        "in": "yin",
+                        "u": "wu",
+                    }
+                    if pinyin in pinyin_rep_map.keys():
+                        pinyin = pinyin_rep_map[pinyin]
+                    else:
+                        single_rep_map = {
+                            "v": "yu",
+                            "e": "e",
+                            "i": "y",
+                            "u": "w",
+                        }
+                        if pinyin[0] in single_rep_map.keys():
+                            pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
+
+                assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
+                new_c, new_v = pinyin_to_symbol_map[pinyin].split(" ")
+                new_v = new_v + tone
+                phone = [new_c, new_v]
+                word2ph.append(len(phone))
+
+            phones_list += phone
+    return phones_list, word2ph
+
+
+def text_normalize(text):
+    # https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization
+    tx = TextNormalizer()
+    sentences = tx.normalize(text)
+    dest_text = ""
+    for sentence in sentences:
+        dest_text += replace_punctuation(sentence)
+    return dest_text
+
+
+if __name__ == "__main__":
+    text = "啊——但是《原神》是由,米哈\游自主，研发的一款全.新开放世界.冒险游戏"
+    text = "呣呣呣～就是…大人的鼹鼠党吧？"
+    text = "你好"
+    text = text_normalize(text)
+    print(g2p(text))
+
+
+# # 示例用法
+# text = "这是一个示例文本：,你好！这是一个测试..."
+# print(g2p_paddle(text))  # 输出: 这是一个示例文本你好这是一个测试
diff --git a/utils/tts/gpt_sovits/text/cleaner.py b/utils/tts/gpt_sovits/text/cleaner.py
new file mode 100644
index 0000000000000000000000000000000000000000..156fa81fc986f756314d9056a3179cead68c0e50
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/cleaner.py
@@ -0,0 +1,58 @@
+from utils.tts.gpt_sovits.text import chinese, cleaned_text_to_sequence, symbols, english
+
+language_module_map = {"zh": chinese, "en": english}
+special = [
+    # ("%", "zh", "SP"),
+    ("￥", "zh", "SP2"),
+    ("^", "zh", "SP3"),
+    # ('@', 'zh', "SP4")#不搞鬼畜了，和第二版保持一致吧
+]
+
+
+def clean_text(text, language):
+    if(language not in language_module_map):
+        language="en"
+        text=" "
+    for special_s, special_l, target_symbol in special:
+        if special_s in text and language == special_l:
+            return clean_special(text, language, special_s, target_symbol)
+    language_module = language_module_map[language]
+    norm_text = language_module.text_normalize(text)
+    if language == "zh":
+        phones, word2ph = language_module.g2p(norm_text)
+        assert len(phones) == sum(word2ph)
+        assert len(norm_text) == len(word2ph)
+    else:
+        phones = language_module.g2p(norm_text)
+        word2ph = None
+
+    for ph in phones:
+        assert ph in symbols
+    return phones, word2ph, norm_text
+
+
+def clean_special(text, language, special_s, target_symbol):
+    """
+    特殊静音段sp符号处理
+    """
+    text = text.replace(special_s, ",")
+    language_module = language_module_map[language]
+    norm_text = language_module.text_normalize(text)
+    phones = language_module.g2p(norm_text)
+    new_ph = []
+    for ph in phones[0]:
+        assert ph in symbols
+        if ph == ",":
+            new_ph.append(target_symbol)
+        else:
+            new_ph.append(ph)
+    return new_ph, phones[1], norm_text
+
+
+def text_to_sequence(text, language):
+    phones = clean_text(text)
+    return cleaned_text_to_sequence(phones)
+
+
+if __name__ == "__main__":
+    print(clean_text("你好%啊啊啊额、还是到付红四方。", "zh"))
diff --git a/utils/tts/gpt_sovits/text/cmudict-fast.rep b/utils/tts/gpt_sovits/text/cmudict-fast.rep
new file mode 100644
index 0000000000000000000000000000000000000000..ec1564a87ea73e92c254c7883b410bcc5fca6e26
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/cmudict-fast.rep
@@ -0,0 +1,135010 @@
+'BOUT B AW1 T
+'CAUSE K AH0 Z
+'COURSE K AO1 R S
+'CUSE K Y UW1 Z
+'EM AH0 M
+'FRISCO F R IH1 S K OW0
+'GAIN G EH1 N
+'KAY K EY1
+'M AH0 M
+'N AH0 N
+'ROUND R AW1 N D
+'S EH1 S
+'TIL T IH1 L
+'TIS T IH1 Z
+'TWAS T W AH1 Z
+A AH0
+A(2) EY1
+A'S EY1 Z
+A. EY1
+A.'S EY1 Z
+A.D. EY2 D IY1
+A.M. EY2 EH1 M
+A.S EY1 Z
+AAA T R IH2 P AH0 L EY1
+AABERG AA1 B ER0 G
+AACHEN AA1 K AH0 N
+AACHENER AA1 K AH0 N ER0
+AAKER AA1 K ER0
+AALIYAH AA2 L IY1 AA2
+AALSETH AA1 L S EH0 TH
+AAMODT AA1 M AH0 T
+AANCOR AA1 N K AO2 R
+AARDEMA AA0 R D EH1 M AH0
+AARDVARK AA1 R D V AA2 R K
+AARDVARKS AA1 R D V AA2 R K S
+AARGH AA1 R G
+AARHUS AA2 HH UW1 S
+AARON EH1 R AH0 N
+AARON'S EH1 R AH0 N Z
+AARONS EH1 R AH0 N Z
+AARONSON EH1 R AH0 N S AH0 N
+AARONSON(2) AA1 R AH0 N S AH0 N
+AARONSON'S EH1 R AH0 N S AH0 N Z
+AARONSON'S(2) AA1 R AH0 N S AH0 N Z
+AARTI AA1 R T IY2
+AASE AA1 S
+AASEN AA1 S AH0 N
+AB AE1 B
+AB(2) EY1 B IY1
+ABA EY2 B IY2 EY1
+ABABA AH0 B AA1 B AH0
+ABABA(2) AA1 B AH0 B AH0
+ABACHA AE1 B AH0 K AH0
+ABACK AH0 B AE1 K
+ABACO AE1 B AH0 K OW2
+ABACUS AE1 B AH0 K AH0 S
+ABAD AH0 B AA1 D
+ABADAKA AH0 B AE1 D AH0 K AH0
+ABADI AH0 B AE1 D IY0
+ABADIE AH0 B AE1 D IY0
+ABAIR AH0 B EH1 R
+ABALKIN AH0 B AA1 L K IH0 N
+ABALONE AE2 B AH0 L OW1 N IY0
+ABALONES AE2 B AH0 L OW1 N IY0 Z
+ABALOS AA0 B AA1 L OW0 Z
+ABANDON AH0 B AE1 N D AH0 N
+ABANDONED AH0 B AE1 N D AH0 N D
+ABANDONING AH0 B AE1 N D AH0 N IH0 NG
+ABANDONMENT AH0 B AE1 N D AH0 N M AH0 N T
+ABANDONMENTS AH0 B AE1 N D AH0 N M AH0 N T S
+ABANDONS AH0 B AE1 N D AH0 N Z
+ABANTO AH0 B AE1 N T OW0
+ABARCA AH0 B AA1 R K AH0
+ABARE AA0 B AA1 R IY0
+ABASCAL AE1 B AH0 S K AH0 L
+ABASH AH0 B AE1 SH
+ABASHED AH0 B AE1 SH T
+ABASIA AH0 B EY1 ZH Y AH0
+ABATE AH0 B EY1 T
+ABATED AH0 B EY1 T IH0 D
+ABATEMENT AH0 B EY1 T M AH0 N T
+ABATEMENTS AH0 B EY1 T M AH0 N T S
+ABATES AH0 B EY1 T S
+ABATING AH0 B EY1 T IH0 NG
+ABATTOIR AE2 B AH0 T W AA1 R
+ABBA AE1 B AH0
+ABBADO AH0 B AA1 D OW0
+ABBAS AH0 B AA1 S
+ABBASI AA0 B AA1 S IY0
+ABBATE AA1 B EY0 T
+ABBATIELLO AA0 B AA0 T IY0 EH1 L OW0
+ABBE AE1 B IY0
+ABBE(2) AE0 B EY1
+ABBENHAUS AE1 B AH0 N HH AW2 S
+ABBETT AH0 B EH1 T
+ABBEVILLE AE1 B V IH0 L
+ABBEY AE1 B IY0
+ABBEY'S AE1 B IY0 Z
+ABBIE AE1 B IY0
+ABBITT AE1 B IH0 T
+ABBOT AE1 B AH0 T
+ABBOTSTOWN AE1 B AH0 T S T AW1 N
+ABBOTT AE1 B AH0 T
+ABBOTT'S AE1 B AH0 T S
+ABBOTTSTOWN AE1 B AH0 T S T AW1 N
+ABBOUD AH0 B UW1 D
+ABBOUD(2) AH0 B AW1 D
+ABBREVIATE AH0 B R IY1 V IY0 EY2 T
+ABBREVIATED AH0 B R IY1 V IY0 EY2 T IH0 D
+ABBREVIATES AH0 B R IY1 V IY0 EY2 T S
+ABBREVIATING AH0 B R IY1 V IY0 EY2 T IH0 NG
+ABBREVIATION AH0 B R IY2 V IY0 EY1 SH AH0 N
+ABBREVIATIONS AH0 B R IY2 V IY0 EY1 SH AH0 N Z
+ABBRUZZESE AA0 B R UW0 T S EY1 Z IY0
+ABBS AE1 B Z
+ABBY AE1 B IY0
+ABC EY1 B IY2 S IY2
+ABC'S EY1 B IY2 S IY2 Z
+ABCO AE1 B K OW0
+ABCOTEK AE1 B K OW0 T EH2 K
+ABCS EY1 B IY2 S IY2 Z
+ABD EY2 B IY2 D IY1
+ABDALLA AE2 B D AE1 L AH0
+ABDALLAH AE2 B D AE1 L AH0
+ABDEL AE1 B D EH2 L
+ABDELLA AE2 B D EH1 L AH0
+ABDICATE AE1 B D AH0 K EY2 T
+ABDICATED AE1 B D AH0 K EY2 T IH0 D
+ABDICATES AE1 B D AH0 K EY2 T S
+ABDICATING AE1 B D IH0 K EY2 T IH0 NG
+ABDICATION AE2 B D IH0 K EY1 SH AH0 N
+ABDNOR AE1 B D N ER0
+ABDO AE1 B D OW0
+ABDOLLAH AE2 B D AA1 L AH0
+ABDOMEN AE0 B D OW1 M AH0 N
+ABDOMEN(2) AE1 B D AH0 M AH0 N
+ABDOMINAL AE0 B D AA1 M AH0 N AH0 L
+ABDOMINAL(2) AH0 B D AA1 M AH0 N AH0 L
+ABDUCT AE0 B D AH1 K T
+ABDUCTED AE0 B D AH1 K T IH0 D
+ABDUCTED(2) AH0 B D AH1 K T IH0 D
+ABDUCTEE AE0 B D AH2 K T IY1
+ABDUCTEES AE0 B D AH2 K T IY1 Z
+ABDUCTING AE0 B D AH1 K T IH0 NG
+ABDUCTING(2) AH0 B D AH1 K T IH0 NG
+ABDUCTION AE0 B D AH1 K SH AH0 N
+ABDUCTION(2) AH0 B D AH1 K SH AH0 N
+ABDUCTIONS AE0 B D AH1 K SH AH0 N Z
+ABDUCTIONS(2) AH0 B D AH1 K SH AH0 N Z
+ABDUCTOR AE0 B D AH1 K T ER0
+ABDUCTOR(2) AH0 B D AH1 K T ER0
+ABDUCTORS AE0 B D AH1 K T ER0 Z
+ABDUCTORS(2) AH0 B D AH1 K T ER0 Z
+ABDUCTS AE0 B D AH1 K T S
+ABDUL AE0 B D UW1 L
+ABDULAZIZ AE0 B D UW2 L AH0 Z IY1 Z
+ABDULLA AA0 B D UW1 L AH0
+ABDULLAH AE2 B D AH1 L AH0
+ABE EY1 B
+ABED AH0 B EH1 D
+ABEDI AH0 B EH1 D IY0
+ABEE AH0 B IY1
+ABEL EY1 B AH0 L
+ABELA AA0 B EH1 L AH0
+ABELARD AE1 B IH0 L ER0 D
+ABELE AH0 B EH1 L
+ABELES AH0 B EH1 L Z
+ABELES(2) EY1 B AH0 L IY2 Z
+ABELL EY1 B AH0 L
+ABELLA AH0 B EH1 L AH0
+ABELN AE1 B IH0 L N
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+HAMMERSLEY HH AE1 M ER0 S L IY0
+HAMMERSMITH HH AE1 M ER0 S M IH2 TH
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+HAMMERSTROM HH AE1 M ER0 S T R AH0 M
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+HANDBILLS HH AE1 N D B IH2 L Z
+HANDBOOK HH AE1 N D B UH2 K
+HANDBOOKS HH AE1 N D B UH2 K S
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+HANDED HH AE1 N D AH0 D
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+HANDELAND HH AE1 N D IH0 L AH0 N D
+HANDELMAN HH AE1 N D AH0 L M AH0 N
+HANDELS HH AE1 N D AH0 L Z
+HANDELSBANK HH AE1 N D AH0 L Z B AE2 NG K
+HANDELSBANKEN HH AE2 N D AH0 L S B AE1 NG K AH0 N
+HANDELSMAN HH AE1 N D IH0 L S M AH0 N
+HANDER HH AE1 N D ER0
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+HANDGUNS HH AE1 N D G AH2 N Z
+HANDHELD HH AE1 N D HH EH1 L D
+HANDHOLD HH AE1 N D HH OW2 L D
+HANDHOLDING HH AE1 N D HH OW2 L D IH0 NG
+HANDICAP HH AE1 N D IY0 K AE2 P
+HANDICAPPED HH AE1 N D IY0 K AE2 P T
+HANDICAPPER HH AE1 N D IY0 K AE2 P ER0
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+HANDICAPPING HH AE1 N D IY0 K AE2 P IH0 NG
+HANDICAPS HH AE1 N D IY0 K AE2 P S
+HANDICRAFT HH AE1 N D IY0 K R AE2 F T
+HANDICRAFTS HH AE1 N D IY0 K R AE2 F T S
+HANDIER HH AE1 N D IY0 ER0
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+HANDKERCHIEFS HH AE1 NG K ER0 CH AH0 F S
+HANDKERCHIEFS(2) HH AE1 NG K ER0 CH IY0 F S
+HANDLE HH AE1 N D AH0 L
+HANDLEBAR HH AE1 N D AH0 L B AA2 R
+HANDLEBARS HH AE1 N D AH0 L B AA2 R Z
+HANDLED HH AE1 N D AH0 L D
+HANDLER HH AE1 N D L ER0
+HANDLER(2) HH AE1 N D AH0 L ER0
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+HANDLERS(2) HH AE1 N D AH0 L ER0 Z
+HANDLES HH AE1 N D AH0 L Z
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+HANDLIN HH AE1 N D L IH0 N
+HANDLING HH AE1 N D L IH0 NG
+HANDLING(2) HH AE1 N D AH0 L IH0 NG
+HANDLON HH AE1 N D L AH0 N
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+HANDLOOMS HH AE1 N D L UW2 M Z
+HANDLY HH AE1 N D L IY0
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+HANDPICKED HH AE1 N D P IH1 K T
+HANDRAIL HH AE1 N D R EY2 L
+HANDRAILS HH AE1 N D R EY2 L Z
+HANDRICH HH AE1 N D R IH0 K
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+HANDS HH AE1 N D Z
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+HANDS-ON HH AE2 N Z AO1 N
+HANDSAW HH AE1 N D S AO2
+HANDSAWS HH AE1 N D S AO2 Z
+HANDSET HH AE1 N D S EH2 T
+HANDSETS HH AE1 N D S EH2 T S
+HANDSHAKE HH AE1 N D SH EY2 K
+HANDSHAKES HH AE1 N D SH EY2 K S
+HANDSHAKING HH AE1 N D SH EY2 K IH0 NG
+HANDSOME HH AE1 N S AH0 M
+HANDSOMELY HH AE1 N S AH0 M L IY0
+HANDSTAND HH AE1 N D S T AE2 N D
+HANDSTANDS HH AE1 N D S T AE2 N D Z
+HANDWERK HH AE1 N D W ER0 K
+HANDWERKER HH AE1 N D W ER0 K ER0
+HANDWOVEN HH AE1 N D W OW1 V AH0 N
+HANDWOVEN(2) HH AE1 N W OW1 V AH0 N
+HANDWRITING HH AE1 N D R AY2 T IH0 NG
+HANDWRITTEN HH AE1 N D R IH2 T AH0 N
+HANDY HH AE1 N D IY0
+HANDYMAN HH AE1 N D IY0 M AE2 N
+HANDYMEN HH AE1 N D IY0 M EH1 N
+HANE HH EY1 N
+HANEDA HH AH0 N EY1 D AH0
+HANEL HH AE1 N AH0 L
+HANELINE HH AE1 N IH0 L AY2 N
+HANEMANN HH EY1 N M AH0 N
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+HANF HH AE1 N F
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+HANG HH AE1 NG
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+HANGS HH AE1 NG Z
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+HANGUL HH AE1 N G UH2 L
+HANGUP HH AE1 NG G AH2 P
+HANGUPS HH AE1 NG G AH2 P S
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+HANIFEN HH AE1 N IH0 F AH0 N
+HANIFIN HH AE1 N IH0 F IH0 N
+HANIGAN HH AE1 N IH0 G AH0 N
+HANING HH EY1 N IH0 NG
+HANISCH HH AE1 N IH0 SH
+HANISEE HH AE1 N IH0 S IY0
+HANISH HH AE1 N IH0 SH
+HANJIN HH AE1 N JH IH0 N
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+HANKE HH AE1 NG K
+HANKEL HH AE1 NG K AH0 L
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+HANKES HH AE1 NG K S
+HANKEY HH AE1 NG K IY0
+HANKIN HH AE1 NG K IH0 N
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+HANKINSON HH AE1 NG K IH0 N S AH0 N
+HANKLA HH AE1 NG K L AH0
+HANKO HH AE1 NG K OW0
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+HANNES HH AE1 N Z
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+HANRAHAN HH AE1 N R AH0 HH AE0 N
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+HANSMA HH AE1 N S M AH0
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+HANSOM HH AE1 N S AH0 M
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+HANSSEN HH AE1 N S AH0 N
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+HANUKKAH HH AA1 N AH0 K AH0
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+HANUKKAHS HH AA1 N AH0 K AH0 Z
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+HAO HH AW1
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+HAPEMAN HH EY1 P M AH0 N
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+HAPPENINGS(2) HH AE1 P N IH0 NG Z
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+HAPPENSTANCE HH AE1 P AH0 N S T AE2 N S
+HAPPIER HH AE1 P IY0 ER0
+HAPPIEST HH AE1 P IY0 AH0 S T
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+HAPPINESS HH AE1 P IY0 N AH0 S
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+HARADIM(2) HH AH0 R AE2 D IY1 M
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+HARASSING HH ER0 AE1 S IH0 NG
+HARASSMENT HH ER0 AE1 S M AH0 N T
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+HARAWAY HH AA1 R AH0 W EY0
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+HARBAUGH HH AA1 R B AO2
+HARBECK HH AA1 R B EH2 K
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+HARBIN HH AA1 R B IH0 N
+HARBINGER HH AA1 R B IH0 N JH ER0
+HARBINGERS HH AA1 R B IH0 NG ER0 Z
+HARBINSON HH AA1 R B IH0 N S AH0 N
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+HARBOLD HH AA1 R B OW2 L D
+HARBOR HH AA1 R B ER0
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+HARBORED HH AA1 R B ER0 D
+HARBORING HH AA1 R B ER0 IH0 NG
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+HARBORSIDE HH AA1 R B ER0 S AY2 D
+HARBORY HH AA1 R B AH0 R IY0
+HARBOUR HH AA1 R B ER0
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+HARBURY HH AA1 R B ER0 IY0
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+HARCOURT HH AA1 R K AO2 R T
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+HARCROW HH AA1 R K R OW0
+HARCUM HH AA1 R K AH0 M
+HARD HH AA1 R D
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+HARD-WORKING HH AA2 R D HH W ER1 K IH0 NG
+HARDACRE HH AA1 R D EY2 K ER0
+HARDAGE HH AA1 R D IH0 JH
+HARDART HH AA1 R D AA2 R T
+HARDAWAY HH AA1 R D AH0 W EY2
+HARDBACK HH AA1 R D B AE2 K
+HARDBALL HH AA1 R D B AO2 L
+HARDBOARD HH AA1 R D B AO2 R D
+HARDCASTLE HH AA1 R D K AE2 S AH0 L
+HARDCASTLE'S HH AA1 R D K AE2 S AH0 L Z
+HARDCORE HH AA1 R D K AO1 R
+HARDCOVER HH AA1 R D K AH2 V ER0
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+HARDEBECK HH AA1 R D B EH0 K
+HARDEE HH AA1 R D IY1
+HARDEE'S HH AA1 R D IY1 Z
+HARDEGREE HH AA0 R D IH0 G R IY1
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+HARDEN HH AA1 R D AH0 N
+HARDEN'S HH AA1 R D AH0 N Z
+HARDENBROOK HH AA1 R D AH0 N B R UH2 K
+HARDENED HH AA1 R D AH0 N D
+HARDENER HH AA1 R D AH0 N ER0
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+HARDENING HH AA1 R D AH0 N IH0 NG
+HARDENING(2) HH AA1 R D N IH0 NG
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+HARDER HH AA1 R D ER0
+HARDERS HH AA1 R D ER0 Z
+HARDEST HH AA1 R D AH0 S T
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+HARDEY HH AA1 R D IY0
+HARDGOOD HH AA1 R D G UH2 D
+HARDGOODS HH AA1 R D G UH2 D Z
+HARDGRAVE HH AA1 R D G R EY2 V
+HARDGROVE HH AA1 R D G R OW2 V
+HARDHEAD HH AA1 R D HH EH2 D
+HARDHEADED HH AA1 R D HH EH2 D IH0 D
+HARDICK HH AA1 R D IH0 K
+HARDIE HH AA1 R D IY0
+HARDIER HH AA1 R D IY0 ER0
+HARDIEST HH AA1 R D IY0 AH0 S T
+HARDIGREE HH AA0 R D IH0 G R IY1
+HARDIMAN HH AA1 R D IH0 M AH0 N
+HARDIMON HH AA1 R D IH0 M AA0 N
+HARDIN HH AA1 R D IH0 N
+HARDING HH AA1 R D IH0 NG
+HARDING'S HH AA1 R D IH0 NG S
+HARDINGER HH AA1 R D IH0 NG ER0
+HARDISON HH AA1 R D IH0 S AH0 N
+HARDISTER HH AA1 R D IH0 S T ER0
+HARDISTY HH AA1 R D IH0 S T IY0
+HARDLINE HH AA1 R D L AY2 N
+HARDLINER HH AA1 R D L AY2 N ER0
+HARDLINERS HH AA1 R D L AY2 N ER0 Z
+HARDLY HH AA1 R D L IY0
+HARDMAN HH AA1 R D M AH0 N
+HARDNESS HH AA1 R D N AH0 S
+HARDNETT HH AA1 R D N IH0 T
+HARDPRESSED HH AA1 R D P R EH2 S T
+HARDRICK HH AA1 R D R IH0 K
+HARDS HH AA1 R D Z
+HARDSCRABBLE HH AA1 R D S K R AE2 B AH0 L
+HARDSHIP HH AA1 R D SH IH0 P
+HARDSHIPS HH AA1 R D SH IH0 P S
+HARDT HH AA1 R T
+HARDTKE HH AA1 R D K IY0
+HARDWARE HH AA1 R D W EH2 R
+HARDWAY HH AA1 R D W EY2
+HARDWICK HH AA1 R D W IH2 K
+HARDWICKE HH AA1 R D W IH0 K
+HARDWIN HH AA1 R D W IH0 N
+HARDWIRED HH AA2 R D W AY1 ER0 D
+HARDWOOD HH AA1 R D W UH2 D
+HARDWOODS HH AA1 R D W UH2 D Z
+HARDWORK HH AA1 R D W ER2 K
+HARDWORKING HH AA1 R D W ER2 K IH0 NG
+HARDY HH AA1 R D IY0
+HARDY'S HH AA1 R D IY0 Z
+HARDYMON HH AA1 R D IY0 M AA2 N
+HARE HH EH1 R
+HARE'S HH EH1 R Z
+HAREBRAINED HH EH1 R B R EY2 N D
+HARELSON HH AE1 R IH0 L S AH0 N
+HAREM HH EH1 R AH0 M
+HAREN HH EH1 R AH0 N
+HARER HH EH1 R ER0
+HARES HH EH1 R Z
+HAREWOOD HH EH1 R W UH2 D
+HARFF HH AA1 R F
+HARFORD HH AA1 R F ER0 D
+HARGADON HH AA0 R G AA0 D AO1 N
+HARGAN HH AA1 R G AH0 N
+HARGARTEN HH AA1 R G AA0 R T AH0 N
+HARGENS HH AA1 R G AH0 N Z
+HARGER HH AA1 R G ER0
+HARGETT HH AA1 R JH IH0 T
+HARGIS HH AA1 R G IH0 S
+HARGRAVE HH AA1 R G R EY2 V
+HARGRAVES HH AA1 R G R EY2 V Z
+HARGREAVES HH AA1 R G R IY2 V Z
+HARGROVE HH AA1 R G R OW2 V
+HARGUS HH AA1 R G AH0 S
+HARI HH AA1 R IY0
+HARIG HH AE1 R IH0 G
+HARIMA HH EH0 R IY1 M AH0
+HARING HH EH1 R IH0 NG
+HARIRI HH ER0 IH1 R IY0
+HARIS HH AA1 R IY0 S
+HARIS(2) HH EH1 R IH0 S
+HARITOS HH EH0 R IY1 T OW0 S
+HARIZ HH EH1 R IH0 Z
+HARJO HH AA1 R JH OW0
+HARJU HH AA1 R Y UW0
+HARK HH AA1 R K
+HARKAVY HH AA1 R K AH0 V IY0
+HARKE HH AA1 R K
+HARKEN HH AA1 R K AH0 N
+HARKENS HH AA1 R K AH0 N Z
+HARKER HH AA1 R K ER0
+HARKEY HH AA1 R K IY0
+HARKIN HH AA1 R K IH0 N
+HARKIN'S HH AA1 R K IH0 N Z
+HARKING HH AA1 R K IH0 NG
+HARKINS HH AA1 R K IH0 N Z
+HARKLEROAD HH AA1 R K L ER0 OW0 D
+HARKLESS HH AA1 R K L AH0 S
+HARKNESS HH AA1 R K N AH0 S
+HARKRADER HH AA1 R K R AH0 D ER0
+HARKRIDER HH AA1 R K R AY2 D ER0
+HARKS HH AA1 R K S
+HARL HH AA1 R L
+HARLACHER HH AA1 R L AH0 K ER0
+HARLAN HH AA1 R L AH0 N
+HARLAND HH AA1 R L AH0 N D
+HARLE HH AA1 R AH0 L
+HARLEM HH AA1 R L AH0 M
+HARLEM'S HH AA1 R L AH0 M Z
+HARLEMAN HH AA1 R AH0 L M AH0 N
+HARLEQUIN HH AA1 R L AH0 K W AH0 N
+HARLESS HH AA1 R L IH0 S
+HARLEY HH AA1 R L IY0
+HARLEY'S HH AA1 R L IY0 Z
+HARLEYSVILLE HH AA1 R L IY0 Z V IH2 L
+HARLIN HH AA1 R L IH0 N
+HARLIN'S HH AA1 R L IH0 N Z
+HARLING HH AA1 R L IH0 NG
+HARLINGEN HH AA1 R L IH0 NG AH0 N
+HARLISON HH AA1 R L IH0 S AH0 N
+HARLOFF HH AA1 R L AO0 F
+HARLOT HH AA1 R L AH0 T
+HARLOW HH AA1 R L OW0
+HARM HH AA1 R M
+HARM'S HH AA1 R M Z
+HARMAN HH AA1 R M AH0 N
+HARMATA HH AA0 R M AA1 T AH0
+HARMATTAN HH AA2 R M AH0 T AE1 N
+HARMED HH AA1 R M D
+HARMEL HH AA1 R M AH0 L
+HARMEN HH AA1 R M AH0 N
+HARMENING HH AA1 R M AH0 N IH0 NG
+HARMER HH AA1 R M ER0
+HARMES HH AA1 R M Z
+HARMETZ HH AA1 R M EH2 T S
+HARMEYER HH AA1 R M AY2 ER0
+HARMFUL HH AA1 R M F AH0 L
+HARMFULLY HH AA1 R M F AH0 L IY0
+HARMFULNESS HH AA1 R M F AH0 L N AH0 S
+HARMING HH AA1 R M IH0 NG
+HARMISON HH AA1 R M IH0 S AH0 N
+HARMLESS HH AA1 R M L AH0 S
+HARMLESSLY HH AA1 R M L AH0 S L IY0
+HARMON HH AA1 R M AH0 N
+HARMON'S HH AA1 R M AH0 N Z
+HARMONIA HH AA0 R M OW1 N IY0 AH0
+HARMONIC HH AA0 R M AA1 N IH0 K
+HARMONICA HH AA0 R M AA1 N IH0 K AH0
+HARMONICS HH AA0 R M AA1 N IH0 K S
+HARMONIE HH AA1 R M AH0 N IY0
+HARMONIES HH AA1 R M AH0 N IY0 Z
+HARMONIOUS HH AA0 R M OW1 N IY0 AH0 S
+HARMONIOUSLY HH AA0 R M OW1 N IY0 AH0 S L IY0
+HARMONIUM HH AA0 R M OW1 N IY0 AH0 M
+HARMONIZATION HH AA2 R M AH0 N IH0 Z EY1 SH AH0 N
+HARMONIZE HH AA1 R M AH0 N AY2 Z
+HARMONIZED HH AA1 R M AH0 N AY2 Z D
+HARMONIZES HH AA1 R M AH0 N AY2 Z IH0 Z
+HARMONIZING HH AA1 R M AH0 N AY2 Z IH0 NG
+HARMONY HH AA1 R M AH0 N IY0
+HARMS HH AA1 R M Z
+HARMSEN HH AA1 R M S AH0 N
+HARN HH AA1 R N
+HARNACK HH AA1 R N AH0 K
+HARNAGE HH AA1 R N IH0 JH
+HARNDEN HH AA1 R N D AH0 N
+HARNE HH AA1 R N
+HARNED HH AA1 R N D
+HARNER HH AA1 R N ER0
+HARNESS HH AA1 R N AH0 S
+HARNESS(2) HH AA1 R N IH0 S
+HARNESSED HH AA1 R N AH0 S T
+HARNESSES HH AA1 R N AH0 S AH0 Z
+HARNESSES(2) HH AA1 R N AH0 S IH0 Z
+HARNESSING HH AA1 R N AH0 S IH0 NG
+HARNETT HH AA1 R N IH0 T
+HARNEY HH AA1 R N IY0
+HARNISCH HH AA1 R N IH0 SH
+HARNISCHFEGER HH AA1 R N IH0 SH F EH2 G ER0
+HARNISCHFEGER'S HH AA1 R N IH0 SH F EH2 G ER0 Z
+HARNISH HH AA1 R N IH0 SH
+HARNOIS HH AA0 R N W AA1
+HARO HH AA1 R OW0
+HAROLD HH EH1 R AH0 L D
+HAROLD'S HH EH1 R AH0 L D Z
+HAROLDSON HH AE1 R OW0 L D S AH0 N
+HARP HH AA1 R P
+HARPE HH AA1 R P
+HARPED HH AA1 R P T
+HARPEL HH AA0 R P EH1 L
+HARPENAU HH AA1 R P IH0 N OW0
+HARPER HH AA1 R P ER0
+HARPER'S HH AA1 R P ER0 Z
+HARPERCOLLINS HH AA1 R P ER0 K AO1 L IH0 N Z
+HARPERS HH AA1 R P ER0 Z
+HARPHAM HH AA1 R F AH0 M
+HARPIN HH AA0 R P AE1 N
+HARPING HH AA1 R P IH0 NG
+HARPIST HH AA1 R P IH0 S T
+HARPISTS HH AA1 R P IH0 S T S
+HARPLEY HH AA1 R P L IY0
+HARPO HH AA1 R P OW0
+HARPOLD HH AA1 R P OW2 L D
+HARPOLE HH AA1 R P OW2 L
+HARPOON HH AA0 R P UW1 N
+HARPOONS HH AA0 R P UW1 N Z
+HARPOOTLIAN HH AA0 R P UW1 T L IY0 AH0 N
+HARPS HH AA1 R P S
+HARPSICHORD HH AA1 R P S AH0 K AO2 R D
+HARPST HH AA1 R P S T
+HARPSTER HH AA1 R P S T ER0
+HARQUEBUS HH AA1 R K W AH0 B AH0 S
+HARR HH AE1 R
+HARRA HH AA1 R AH0
+HARRAH HH AE1 R AH0
+HARRAH'S HH EH1 R AH0 Z
+HARRAL HH AE1 R AH0 L
+HARRALSON HH AE1 R AH0 L S AH0 N
+HARRE HH AE1 R
+HARREL HH AE1 R AH0 L
+HARRELD HH AE1 R IH0 L D
+HARRELL HH EH1 R AH0 L
+HARRELL'S HH AE1 R AH0 L Z
+HARRELSON HH EH1 R IH0 L S AH0 N
+HARREN HH AE1 R AH0 N
+HARRER HH AA1 R ER0
+HARRIED HH EH1 R IY0 D
+HARRIER HH EH1 R IY0 ER0
+HARRIES HH EH1 R IY0 Z
+HARRIET HH EH1 R IY0 AH0 T
+HARRIETTE HH AE1 R IY0 EH0 T
+HARRIGAN HH EH1 R IH0 G AH0 N
+HARRIGER HH AE1 R IH0 G ER0
+HARRILL HH AE1 R AH0 L
+HARRIMAN HH EH1 R IH0 M AH0 N
+HARRING HH AE1 R IH0 NG
+HARRINGTON HH EH1 R IH0 NG T AH0 N
+HARRIOTT HH AE1 R IY0 AH0 T
+HARRIS HH EH1 R IH0 S
+HARRIS' HH EH1 R IH0 S
+HARRIS'S HH EH1 R IH0 S IH0 Z
+HARRISBURG HH AE1 R IH0 S B ER0 G
+HARRISBURG(2) HH EH1 R IH0 S B ER0 G
+HARRISBURG'S HH AE1 R IH0 S B ER0 G Z
+HARRISBURG'S(2) HH EH1 R IH0 S B ER0 G Z
+HARRISBURGH HH AE1 R IH0 S B ER0 G
+HARRISBURGH(2) HH EH1 R IH0 S B ER0 G
+HARRISBURGH'S HH AE1 R IH0 S B ER0 G Z
+HARRISBURGH'S(2) HH EH1 R IH0 S B ER0 G Z
+HARRISES HH AE1 R IH0 S IH0 Z
+HARRISON HH EH1 R IH0 S AH0 N
+HARRISON'S HH EH1 R IH0 S AH0 N Z
+HARRISS HH AE1 R IH0 S
+HARRITY HH AE1 R IH0 T IY0
+HARROD HH EH1 R AH0 D
+HARROD'S HH EH1 R AH0 D Z
+HARRODS HH EH1 R AH0 D Z
+HARROLD HH EH1 R AH0 L D
+HARRON HH AE1 R AH0 N
+HARROP HH EH1 R AH0 P
+HARROUN HH ER0 UW1 N
+HARROW HH AE1 R OW0
+HARROWER HH AE1 R OW0 W ER0
+HARROWING HH EH1 R OW0 IH0 NG
+HARRY HH EH1 R IY0
+HARRY'S HH EH1 R IY0 Z
+HARRYMAN HH AE1 R IY0 M AH0 N
+HARSCH HH AA1 R SH
+HARSCO HH AA1 R S K OW0
+HARSH HH AA1 R SH
+HARSHA HH AA1 R SH AH0
+HARSHAM HH AA1 R SH AH0 M
+HARSHAW HH AA1 R SH AO2
+HARSHBARGER HH AA1 R SH B AA2 R G ER0
+HARSHBERGER HH AA1 R SH B ER0 G ER0
+HARSHER HH AA1 R SH ER0
+HARSHEST HH AA1 R SH AH0 S T
+HARSHFIELD HH AA1 R SH F IY2 L D
+HARSHLY HH AA1 R SH L IY0
+HARSHMAN HH AA1 R SH M AH0 N
+HARSHNESS HH AA1 R SH N AH0 S
+HARSTAD HH AA1 R S T AH0 D
+HARSTON HH AA1 R S T AH0 N
+HART HH AA1 R T
+HART'S HH AA1 R T S
+HARTE HH AA1 R T
+HARTEL HH AA1 R T AH0 L
+HARTELL HH AA1 R T AH0 L
+HARTEN HH AA1 R T AH0 N
+HARTENSTEIN HH AA1 R T AH0 N S T AY2 N
+HARTENSTEIN(2) HH AA1 R T AH0 N S T IY2 N
+HARTER HH AA1 R T ER0
+HARTFIEL HH AA1 R T F IY2 L
+HARTFIELD HH AA1 R T F IY2 L D
+HARTFORD HH AA1 R T F ER0 D
+HARTFORD'S HH AA1 R T F ER0 D Z
+HARTGRAVES HH AA1 R T G R EY2 V Z
+HARTGROVE HH AA1 R T G R OW2 V
+HARTH HH AA1 R TH
+HARTIG HH AA1 R T IH0 G
+HARTIGAN HH AA1 R T IH0 G AH0 N
+HARTIN HH AA1 R T IH0 N
+HARTING HH AA1 R T IH0 NG
+HARTINGER HH AA1 R T IH0 NG ER0
+HARTIS HH AA1 R T IH0 S
+HARTJE HH AA1 R T JH EY0
+HARTKE HH AA1 R T K IY0
+HARTKOPF HH AA1 R T K AO0 F
+HARTL HH AA1 R T AH0 L
+HARTLAGE HH AA1 R T L IH0 JH
+HARTLAND HH AA1 R T L AH0 N D
+HARTLAUB HH AA1 R T L AW2 B
+HARTLE HH AA1 R T AH0 L
+HARTLESS HH AA1 R T L AH0 S
+HARTLEY HH AA1 R T L IY0
+HARTLIEB HH AA1 R T L IY2 B
+HARTLINE HH AA1 R T L AY2 N
+HARTLING HH AA1 R T L IH0 NG
+HARTMAN HH AA1 R T M AH0 N
+HARTMANN HH AA1 R T M AH0 N
+HARTMARX HH AA1 R T M AA2 R K S
+HARTNELL HH AA1 R T N AH0 L
+HARTNER HH AA1 R T N ER0
+HARTNESS HH AA1 R T N AH0 S
+HARTNETT HH AA1 R T N IH0 T
+HARTNEY HH AA1 R T N IY0
+HARTOG HH AA1 R T AH0 G
+HARTON HH AA1 R T AH0 N
+HARTONG HH AA1 R T AO0 NG
+HARTRANFT HH AA1 R T R AH2 N F T
+HARTS HH AA1 R T S
+HARTSELL HH AA1 R T S AH0 L
+HARTSFIELD HH AA1 R T S F IY2 L D
+HARTSHORN HH AA1 R T S HH AO2 R N
+HARTSHORNE HH AA1 R CH ER0 N
+HARTSOCK HH AA1 R T S AH0 K
+HARTSOE HH AA1 R T S OW0
+HARTSON HH AA1 R T S AH0 N
+HARTSOOK HH AA1 R T S UH0 K
+HARTSOUGH HH AA1 R T S AW0
+HARTSTEIN HH AA1 R T S T AY2 N
+HARTSTEIN(2) HH AA1 R T S T IY2 N
+HARTSVILLE HH AA1 R T S V IH0 L
+HARTT HH AA1 R T
+HARTTER HH AA1 R T ER0
+HARTUNG HH AA1 R T AH0 NG
+HARTWELL HH AA1 R T W EH2 L
+HARTWICK HH AA1 R T W IH2 K
+HARTWIG HH AA1 R T W IH0 K
+HARTWOOD HH AA1 R T W UH2 D
+HARTY HH AA1 R T IY0
+HARTZ HH AA1 R T S
+HARTZEL HH AA1 R T Z AH0 L
+HARTZELL HH AA1 R T Z AH0 L
+HARTZLER HH AA1 R T S L ER0
+HARTZOG HH AA1 R T Z AH0 G
+HARUO HH AA0 R UW1 OW0
+HARV HH AA1 R V
+HARVARD HH AA1 R V ER0 D
+HARVARD'S HH AA1 R V ER0 D Z
+HARVATH HH AA1 R V AH0 TH
+HARVE HH AA1 R V
+HARVEL HH AA1 R V AH0 L
+HARVELL HH AA1 R V AH0 L
+HARVEST HH AA1 R V AH0 S T
+HARVESTABLE HH AA1 R V AH0 S T AH0 B AH0 L
+HARVESTED HH AA1 R V AH0 S T IH0 D
+HARVESTER HH AA1 R V AH0 S T ER0
+HARVESTERS HH AA1 R V AH0 S T ER0 Z
+HARVESTING HH AA1 R V AH0 S T IH0 NG
+HARVESTS HH AA1 R V AH0 S T S
+HARVEY HH AA1 R V IY0
+HARVEY'S HH AA1 R V IY0 Z
+HARVIE HH AA1 R V IY0
+HARVILL HH AA1 R V IH0 L
+HARVILLE HH AA1 R V IH2 L
+HARVIN HH AA1 R V IH0 N
+HARVISON HH AA1 R V IH0 S AH0 N
+HARWARD HH AA1 R W ER0 D
+HARWELL HH AA1 R W EH2 L
+HARWICK HH AA1 R W IH0 K
+HARWOOD HH AA1 R W UH2 D
+HARYANA HH EH0 R Y AA1 N AH0
+HAS HH AE1 Z
+HAS(2) HH AH0 Z
+HAS-BEEN HH AE1 Z B IH2 N
+HAS-BEENS HH AE1 Z B IH2 N Z
+HASAN HH EY1 Z AH0 N
+HASBRO HH AE1 Z B R OW0
+HASBRO'S HH AE1 Z B R OW2 Z
+HASBROOK HH AE1 S B R UH0 K
+HASBROUCK HH AE1 Z B R UH2 K
+HASCALL HH AE1 S K AH0 L
+HASCH HH AE1 SH
+HASCHKE HH AE1 SH K
+HASE HH EY1 Z
+HASEGAWA HH AA2 S EY0 G AA1 W AH0
+HASEK HH AA1 S EH0 K
+HASELDEN HH AE1 S IH0 L D AH0 N
+HASELEY HH AE1 S IH0 L IY0
+HASELHORST HH AE1 S IH0 L HH AO0 R S T
+HASELTINE HH AE1 S IH0 L T IY0 N
+HASELTON HH AH0 S EH1 L T AH0 N
+HASEMAN HH EY1 S M AH0 N
+HASENAUER HH AE1 S IH0 N AW0 ER0
+HASENFUS HH EY1 Z AH0 N F AH2 S
+HASER HH EH1 Z ER0
+HASH HH AE1 SH
+HASHAGEN HH AE1 SH AH0 G AH0 N
+HASHED HH AE1 SH T
+HASHEM HH AE1 SH IH0 M
+HASHEMI HH AH0 SH EY1 M IY0
+HASHEMITE HH AE1 SH AH0 M AY2 T
+HASHER HH AE1 SH ER0
+HASHER'S HH AE1 SH ER0 Z
+HASHERS HH AE1 SH ER0 Z
+HASHES HH AE1 SH AH0 Z
+HASHI HH AE1 SH IY2
+HASHIM HH AE1 SH IH0 M
+HASHIMOTO HH AA0 SH IY0 M OW1 T OW0
+HASHING HH AE1 SH IH0 NG
+HASHISH HH AE1 SH IH0 SH
+HASHISH(2) HH AH0 SH IY1 SH
+HASHMAN HH AE1 SH M AH0 N
+HASHMARK HH AE1 SH M AA2 R K
+HASHMI HH AE1 SH M IY0
+HASHMI(2) HH AA1 SH M IY0
+HASHTAG HH AE1 SH T AE2 G
+HASID HH AH0 S IH1 D
+HASIDIC HH AH0 S IH1 D IH0 K
+HASIDIC(2) HH AH0 S IY1 D IH0 K
+HASIDIM HH AH0 S IH1 D IH0 M
+HASIDIM(2) HH AH0 S IY1 D IH0 M
+HASIDS HH AH0 S IH1 D Z
+HASKE HH EY1 S K
+HASKELL HH AE1 S K AH0 L
+HASKETT HH AE1 S K IH0 T
+HASKEW HH AE1 S K Y UW0
+HASKIN HH AE1 S K IH0 N
+HASKINS HH AE1 S K IH0 N Z
+HASLAM HH AE1 S L AH0 M
+HASLER HH AE1 S AH0 L ER0
+HASLER(2) HH AE1 S L ER0
+HASLETT HH AE1 S L IH0 T
+HASLEY HH AE1 Z L IY0
+HASMAN HH AE1 S M AH0 N
+HASN'T HH AE1 Z AH0 N T
+HASO HH AA1 S OW2
+HASO'S HH AA1 S OW2 Z
+HASPEL HH AE1 S P AH0 L
+HASS HH AE1 S
+HASSAN HH AH0 S AA1 N
+HASSE HH AA1 S
+HASSEBROCK HH AE1 S IH0 B R AA1 K
+HASSEL HH AE1 S AH0 L
+HASSELBACH HH AE1 S IH0 L B AA0 K
+HASSELBRING HH AE1 S IH0 L B R IH0 NG
+HASSELL HH AE1 S AH0 L
+HASSELMAN HH AE1 S AH0 L M AH0 N
+HASSEN HH AE1 S AH0 N
+HASSENBERG HH AE1 S AH0 N B ER0 G
+HASSENFELD HH AE1 S AH0 N F EH2 L D
+HASSETT HH AE1 S IH0 T
+HASSEY HH AE1 S IY0
+HASSIG HH AE1 S IH0 G
+HASSING HH AE1 S IH0 NG
+HASSINGER HH AE1 S IH0 N JH ER0
+HASSLE HH AE1 S AH0 L
+HASSLED HH AE1 S AH0 L D
+HASSLER HH AE1 S L ER0
+HASSLES HH AE1 S AH0 L Z
+HASSLING HH AE1 S AH0 L IH0 NG
+HASSLING(2) HH AE1 S L IH0 NG
+HASSMAN HH AE1 S M AH0 N
+HASSON HH AE1 S AH0 N
+HAST HH AE1 S T
+HASTA HH AE1 S T AH0
+HASTA(2) AA1 S T AH0
+HASTE HH EY1 S T
+HASTEN HH EY1 S AH0 N
+HASTENED HH EY1 S AH0 N D
+HASTENING HH EY1 S AH0 N IH0 NG
+HASTENING(2) HH EY1 S N IH0 NG
+HASTENS HH EY1 S AH0 N Z
+HASTERT HH AE1 S T ER0 T
+HASTEY HH EY1 S T IY0
+HASTIE HH EY1 S T IY0
+HASTILY HH EY1 S T AH0 L IY0
+HASTING HH EY1 S T IH0 NG
+HASTINGS HH EY1 S T IH0 NG Z
+HASTON HH AE1 S T AH0 N
+HASTY HH EY1 S T IY0
+HASWELL HH AE1 S W EH0 L
+HASZ HH AA1 SH
+HAT HH AE1 T
+HAT'S HH AE1 T S
+HATA HH AA1 T AH0
+HATALA HH AE1 T AH0 L AH0
+HATAWAY HH AE1 T AH0 W EY2
+HATCH HH AE1 CH
+HATCH'S HH AE1 CH IH0 Z
+HATCHBACK HH AE1 CH B AE2 K
+HATCHED HH AE1 CH T
+HATCHEL HH AE1 CH AH0 L
+HATCHELL HH AE1 CH AH0 L
+HATCHER HH AE1 CH ER0
+HATCHERIES HH AE1 CH ER0 IY0 Z
+HATCHERY HH AE1 CH ER0 IY0
+HATCHES HH AE1 CH IH0 Z
+HATCHET HH AE1 CH AH0 T
+HATCHETS HH AE1 CH AH0 T S
+HATCHETT HH AE1 CH IH0 T
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+HEMANT HH EH1 M AH0 N T
+HEMATITE HH EH1 M AH0 T AY2 T
+HEMATOLOGY HH EH2 M AH0 T AA1 L AH0 JH IY0
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+HEMBERGER HH EH1 M B ER0 G ER0
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+HEMBRICK HH EH1 M B R IH2 K
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+HEMENWAY HH EH1 M AH0 N W EY2
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+HEMING HH EH1 M IH0 NG
+HEMINGER HH EH1 M IH0 NG ER0
+HEMINGWAY HH EH1 M IH0 NG W EY2
+HEMINGWAY'S HH EH1 M IH0 NG W EY2 Z
+HEMIPLEGIA HH EH2 M AH0 P L IY1 JH IY0 AH0
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+HEMISPHERE HH EH1 M IH0 S F IH2 R
+HEMISPHERIC HH EH2 M AH0 S F IH1 R IH0 K
+HEMKER HH EH1 M K ER0
+HEMLER HH EH1 M L ER0
+HEMLINE HH EH1 M L AY2 N
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+HEMLO HH EH1 M L OW0
+HEMLOCK HH EH1 M L AA2 K
+HEMM HH EH1 M
+HEMME HH EH1 M
+HEMMED HH EH1 M D
+HEMMELGARN HH EH1 M IH0 L G AA0 R N
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+HEMMER HH EH1 M ER0
+HEMMERICH HH EH1 M ER0 IH0 K
+HEMMERLE HH EH1 M ER0 L IY0
+HEMMERLING HH EH1 M ER0 L IH0 NG
+HEMMERT HH EH1 M ER0 T
+HEMMETER HH EH1 M IH0 T ER0
+HEMMING HH EH1 M IH0 NG
+HEMMINGER HH EH1 M IH0 NG ER0
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+HEMMINGS HH EH1 M IH0 NG Z
+HEMMINGSEN HH EH1 M IH0 NG S AH0 N
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+HEMO HH IY1 M OW0
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+HEMODYNAMICS HH EH2 M OW0 D AY0 N AE1 M IH0 K S
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+HEMOND HH EH1 M AH0 N D
+HEMOPHILIA HH IY2 M AH0 F IY1 L IY0 AH0
+HEMOPHILIAC HH IY0 M AH0 F IH1 L IY0 AE0 K
+HEMOPHILIAC(2) HH IY0 M OW0 F IH1 L IY0 AE0 K
+HEMOPHILIAC(3) HH IY0 M AH0 F IH1 L Y AE0 K
+HEMOPHILIAC(4) HH IY0 M OW0 F IH1 L Y AE0 K
+HEMOPHILIACS HH IY2 M AH0 F IH1 L IY0 AE2 K S
+HEMORRHAGE HH EH1 M ER0 IH0 JH
+HEMORRHAGE(2) HH EH1 M R AH0 JH
+HEMORRHAGED HH EH1 M ER0 IH0 JH D
+HEMORRHAGIC HH EH2 M ER0 AE1 G IH0 K
+HEMORRHAGING HH EH1 M ER0 IH0 JH IH0 NG
+HEMORRHOID HH EH1 M ER0 OY2 D
+HEMORRHOIDS HH EH1 M ER0 OY2 D Z
+HEMOTEC HH EH1 M OW0 T EH2 K
+HEMP HH EH1 M P
+HEMPEL HH EH1 M P AH0 L
+HEMPEN HH EH1 M P AH0 N
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+HEMPFLING(2) HH EH1 M P F L IH0 NG
+HEMPHILL HH EH1 M P HH IH2 L
+HEMPSTEAD HH EH1 M P S T EH0 D
+HEMRIC HH EH1 M R IH0 K
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+HENCH HH EH1 N CH
+HENCHMAN HH EH1 N CH M AH0 N
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+HENDERSHOTT HH EH1 N D ER0 SH AH0 T
+HENDERSON HH EH1 N D ER0 S AH0 N
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+HENDON HH EH1 N D OW0 N
+HENDRA HH EH1 N D R AH0
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+HENDRICH HH EH1 N D R IH0 K
+HENDRICK HH EH1 N D R IH0 K
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+HENDRICKSEN HH EH1 N D R IH0 K S AH0 N
+HENDRICKSON HH EH1 N D R IH0 K S AH0 N
+HENDRIE HH EH1 N D ER0 IY0
+HENDRIK HH EH1 N D R IH0 K
+HENDRIKS HH EH1 N D R IH0 K S
+HENDRIKSEN HH EH1 N D R IH0 K S AH0 N
+HENDRIX HH EH1 N D R IH0 K S
+HENDRIXSON HH EH1 N D R IH0 K S AH0 N
+HENDRON HH EH1 N D R AH0 N
+HENDRY HH EH1 N D R IY0
+HENDRY'S HH EH1 N D R IY0 Z
+HENDRYX HH EH1 N D R IH0 K S
+HENDY HH EH1 N D IY0
+HENEGAR HH EH1 N IH0 G ER0
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+HENES HH IY1 N Z
+HENEY HH EH1 N IY0
+HENG HH EH1 NG
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+HENIGAN HH EH1 N IH0 G AH0 N
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+HENION HH EH1 N Y AH0 N
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+HENKE HH EH1 NG K
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+HENKELMAN HH EH1 NG K AH0 L M AH0 N
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+HENKES HH EH1 NG K S
+HENKIN HH EH1 NG K IH0 N
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+HENLE HH EH1 N AH0 L
+HENLEY HH EH1 N L IY0
+HENLEY'S HH EH1 N L IY0 Z
+HENLEYS HH EH1 N L IY0 Z
+HENLINE HH EH1 N L AY2 N
+HENLY HH EH1 N L IY0
+HENMAN HH EH1 N M AH0 N
+HENN HH EH1 N
+HENNA HH EH1 N AH0
+HENNAN HH EH1 N AH0 N
+HENNE HH EH1 N
+HENNE(2) HH EH1 N IY0
+HENNEBERGER HH EH1 N B ER0 G ER0
+HENNEBERRY HH EH1 N B EH0 R IY0
+HENNEKE HH EH1 N IH0 K
+HENNELLY HH EH1 N AH0 L IY0
+HENNEMAN HH EH1 N M AH0 N
+HENNEN HH EH1 N AH0 N
+HENNEPIN HH EH1 N IH0 P IH0 N
+HENNER HH EH1 N ER0
+HENNES HH EH1 N Z
+HENNESS HH EH1 N IH0 S
+HENNESSEE HH EH1 N IH0 S IY0
+HENNESSEY HH EH1 N AH0 S IY0
+HENNESSEY'S HH EH1 N AH0 S IY0 Z
+HENNESSY HH EH1 N AH0 S IY0
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+HENNICK HH EH1 N IH0 K
+HENNIG HH EH1 N IH0 G
+HENNIGAN HH EH1 N IH0 G AH0 N
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+HENNINGS HH EH1 N IH0 NG Z
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+HENNON HH EH1 N AH0 N
+HENPECK HH EH1 N P EH2 K
+HENPECKED HH EH1 N P EH2 K T
+HENRI HH EH1 N R IY0
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+HENRI(3) AA2 N R IY1
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+HENRICHS HH EH1 N R IH0 K S
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+HENRICKSEN HH EH1 N R IH0 K S AH0 N
+HENRICKSON HH EH1 N R IH0 K S AH0 N
+HENRIE HH EH1 N ER0 IY0
+HENRIETTA HH EH2 N R IY0 EH1 T AH0
+HENRIETTE HH EH2 N R IY0 EH1 T
+HENRIK HH EH1 N R IH0 K
+HENRIKA HH EH1 N R IH0 K AH0
+HENRIKSEN HH EH1 N R IH0 K S AH0 N
+HENRIKSON HH EH1 N R IH0 K S AH0 N
+HENRIQUE AA0 N R IY1 K
+HENRIQUES HH EH0 N R IY1 K EH0 Z
+HENRIQUES(2) AA0 N R IY1 K
+HENRIQUEZ HH EH0 N R IY1 K EH0 Z
+HENRIQUEZ(2) AA0 N R IY1 K
+HENRY HH EH1 N R IY0
+HENRY'S HH EH1 N R IY0 Z
+HENS HH EH1 N Z
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+HENSCH HH EH1 N SH
+HENSCHEL HH EH1 N SH AH0 L
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+HENSIL HH EH1 N S AH0 L
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+HENTGES HH EH1 N T JH IH0 Z
+HENTHORN HH EH1 N TH AO2 R N
+HENTHORNE HH EH1 N TH AO2 R N
+HENTIC HH EH1 N T IH0 K
+HENTOFF HH EH1 N T AO0 F
+HENTON HH EH1 N T AH0 N
+HENTSCHEL HH EH1 N CH AH0 L
+HENTZ HH EH1 N T S
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+HENZE HH EH1 N Z
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+HENZLER HH EH1 N Z L ER0
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+HEP HH EH1 P
+HEPARIN HH EH1 P ER0 IH0 N
+HEPATIC HH AH0 P AE1 T IH0 K
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+HEPPE HH EH1 P
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+HEPTATHLON HH EH0 P T AE1 TH L AA0 N
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+HER(2) HH ER0
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+HUZZAH HH UH2 Z AA1
+HWA HH W AA1
+HWAN HH W AA1 N
+HWAN'S HH W AA1 N Z
+HWANG HH W AE1 NG
+HWANG(2) HH W AA1 NG
+HWANG-HO HH W AE1 NG HH OW1
+HWANG-HO(2) HH W AA1 NG HH OW1
+HWE HH W EY1
+HY HH AY1
+HYACINTH HH AY1 AH0 S IH2 N TH
+HYACINTHA HH AY2 AH0 S IH1 N TH AH0
+HYACINTHE HH AY1 AH0 S IH0 N TH
+HYACINTHIA HH AY2 AH0 S IH1 N TH IY0 AH0
+HYACINTHIE HH AY1 AH0 S IH2 N TH IY0
+HYACINTHS HH AY1 AH0 S IH0 N TH S
+HYADES HH AY1 AH0 D IY2 Z
+HYAKUTAKE HH AY1 AH0 K UW0 T AA2 K IY0
+HYAKUTAKE(2) HH AY1 AH0 K Y UW0 T AA2 K IY0
+HYALURONIC HH AY2 AH0 L ER0 AA1 N IH0 K
+HYAMS HH AY1 AH0 M Z
+HYANNIS HH AY0 AE1 N IH0 S
+HYANNISPORT HH AY0 AE1 N IH0 S P AO0 R T
+HYATT HH AY1 AH0 T
+HYATT'S HH AY1 AH0 T S
+HYBL HH IH1 B AH0 L
+HYBL'S HH IH1 B AH0 L Z
+HYBRID HH AY1 B R AH0 D
+HYBRID(2) HH AY1 B R IH0 D
+HYBRIDIZATION HH AY2 B R AH0 D AH0 Z EY1 SH AH0 N
+HYBRIDIZE HH AY1 B R AH0 D AY2 Z
+HYBRIDS HH AY1 B R AH0 D Z
+HYBRIENKO HH AY1 B R IY0 EH2 N K OW0
+HYBRITECH HH AY1 B R IH0 T EH2 K
+HYCHE HH AY1 CH
+HYCOR HH AY1 K AO2 R
+HYCROFT HH AY1 K R AO2 F T
+HYDE HH AY1 D
+HYDE'S HH AY1 D Z
+HYDEA HH AY2 D IY1 AH0
+HYDEA(2) HH AY2 D EY1 AH0
+HYDEIA HH AY1 D EY1 AH0
+HYDEN HH AY1 D AH0 N
+HYDER HH AY1 D ER0
+HYDERABAD HH AY0 D EH1 R AH0 B AE2 D
+HYDERABAD(2) HH AY1 D ER0 AH0 B AE2 D
+HYDOCK HH AY1 D AH0 K
+HYDRA HH AY1 D R AH0
+HYDRANT HH AY1 D R AH0 N T
+HYDRANTS HH AY1 D R AH0 N T S
+HYDRAS HH AY1 D R AH0 Z
+HYDRATE HH AY1 D R EY2 T
+HYDRATED HH AY1 D R EY2 T AH0 D
+HYDRATES HH AY1 D R EY2 T S
+HYDRATION HH AY0 D R EY1 SH AH0 N
+HYDRAULIC HH AY0 D R AO1 L IH0 K
+HYDRAULICS HH AY0 D R AO1 L IH0 K S
+HYDRAZINE HH AY1 D R AH0 Z IY2 N
+HYDRICK HH IH1 D R IH0 K
+HYDRIDE HH AY1 D R AY2 D
+HYDRO HH AY1 D R OW2
+HYDRO'S HH AY1 D R OW2 Z
+HYDROCARBON HH AY2 D R OW0 K AA1 R B AH0 N
+HYDROCARBONS HH AY2 D R OW0 K AA1 R B AH0 N Z
+HYDROCEPHALIC HH AY2 D R OW0 S EH0 F AA1 L IH0 K
+HYDROCEPHALUS HH AY2 D R OW0 S EH1 F AH0 L AH0 S
+HYDROELECTRIC HH AY2 D R OW0 IH0 L EH1 K T R IH0 K
+HYDROENCEPHALUS HH AY2 D R OW0 AH0 N S EH1 F AH0 L AH0 S
+HYDROFOIL HH AY1 D R AH0 F OY2 L
+HYDROGEN HH AY1 D R AH0 JH AH0 N
+HYDROGENATE HH AY1 D R AH0 JH AH0 N EY2 T
+HYDROGENATED HH AY1 D R AH0 JH AH0 N EY2 T IH0 D
+HYDROGENATED(2) HH AY0 D R AA1 JH AH0 N EY2 T IH0 D
+HYDROGENATES HH AY1 D R AH0 JH AH0 N EY2 T S
+HYDROGENATING HH AY1 D R AH0 JH AH0 N EY2 T IH0 NG
+HYDROGENATION HH AY2 D R AA2 JH AH0 N EY1 SH AH0 N
+HYDROGENS HH AY1 D R AH0 JH AH0 N Z
+HYDROGRAPHIC HH AY2 D R AH0 G R AE1 F IH0 K
+HYDROLYSIS HH AY0 D R AA1 L AH0 S AH0 S
+HYDROLYZE HH AY1 D R AH0 L AY2 Z
+HYDROLYZED HH AY1 D R AH0 L AY2 Z D
+HYDROLYZING HH AY1 D R AH0 L AY2 Z IH0 NG
+HYDROMETER HH AY0 D R AA1 M AH0 T ER0
+HYDRON HH AY1 D R AH0 N
+HYDROPHILIC HH AY2 D R AH0 F IH1 L IH0 K
+HYDROPIC HH AY2 D R AO1 P IH0 K
+HYDROPONIC HH AY2 D R AH0 P AA1 N IH0 K
+HYDROPOWER HH AY1 D R OW0 P AW2 R
+HYDROSOL HH AY1 D R AH0 S AA2 L
+HYDROSULFIDE HH AY2 D R OW0 S AH1 L F AY2 D
+HYDROTHERAPY HH AY2 D R OW0 TH EH1 R AH0 P IY0
+HYDROTHERMAL HH AY2 D R OW0 TH ER1 M AH0 L
+HYDROUS HH AY1 D R AH0 S
+HYDROX HH AY1 D R AO2 K S
+HYDROXIDE HH AY0 D R AA1 K S AY0 D
+HYDROXIDES HH AY0 D R AA1 K S AY0 D Z
+HYDROXY HH AY2 D R AO1 K S IY0
+HYDSTRA HH AY1 D S T R AH0
+HYE HH AY0
+HYENA HH AY0 IY1 N AH0
+HYENAS HH AY0 IY1 N AH0 Z
+HYER HH AY1 ER0
+HYERS HH AY1 ER0 Z
+HYGEIA HH AY2 JH EY1 AH0
+HYGIENE HH AY1 JH IY2 N
+HYGIENIST HH AY2 G IY1 N IH0 S T
+HYGIENIST(2) HH AY2 G EH1 N IH0 S T
+HYGIENISTS HH AY2 G IY1 N IH0 S T S
+HYGIENISTS(2) HH AY2 G IY1 N IH0 S
+HYGIENISTS(3) HH AY2 G EH1 N IH0 S T S
+HYGIENISTS(4) HH AY2 G EH1 N IH0 S
+HYGROMETER HH AY0 G R AA1 M AH0 T ER0
+HYKES HH AY1 K S
+HYLAND HH AY1 L AH0 N D
+HYLE HH AY1 L
+HYLER HH AY1 L ER0
+HYLSA HH AY1 L S AH0
+HYLTON HH IH1 L T AH0 N
+HYMAN HH AY1 M AH0 N
+HYMANS HH AY1 M AH0 N Z
+HYMAS HH AY1 M AH0 Z
+HYMEL HH AY1 M AH0 L
+HYMEN HH AY1 M AH0 N
+HYMENEAL HH IH2 M AH0 N IY1 AH0 L
+HYMER HH AY1 M ER0
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+HYMIE HH AY1 M IY0
+HYMIES HH AY1 M IY0 Z
+HYMN HH IH1 M
+HYMNAL HH IH1 M N AH0 L
+HYMNALS HH IH1 M N AH0 L Z
+HYMNOLOGY HH IH0 M N AA1 L AH0 JH IY0
+HYMNS HH IH1 M Z
+HYMOWITZ HH IH1 M AH0 W IH0 T S
+HYMOWITZ(2) HH AY1 M AH0 W IH0 T S
+HYND HH IH1 N D
+HYNDAI HH IH1 N D AY1
+HYNDMAN HH IH1 N D M AH0 N
+HYNDS HH IH1 N D Z
+HYNEK HH AY1 N IH0 K
+HYNES HH AY1 N Z
+HYNES'S HH AY1 N Z IH0 Z
+HYNSON HH IH1 N S AH0 N
+HYOGO HH Y OW1 G OW0
+HYOTAN HH Y OW1 T AE2 N
+HYPE HH AY1 P
+HYPED HH AY1 P T
+HYPER HH AY1 P ER0
+HYPERACTIVE HH AY2 P ER0 AE1 K T IH0 V
+HYPERACTIVITY HH AY2 P ER0 AE0 K T IH1 V IH0 T IY0
+HYPERBARIC HH AY0 P ER0 B AA1 R IH0 K
+HYPERBARIC(2) HH AY0 P ER1 B AE1 R IH0 K
+HYPERBOLA HH AY0 P ER1 B AH0 L AH0
+HYPERBOLE HH AY0 P ER1 B AH0 L IY2
+HYPERBOLIC HH AY2 P ER0 B AA1 L IH0 K
+HYPERBOREAN HH AY2 P ER0 B AO1 R IY0 AH0 N
+HYPERCARD HH AY2 P ER0 K AA1 R D
+HYPERCHROMATIC HH AY2 P ER0 K R OW0 M AE1 T IH0 K
+HYPERCRITICAL HH AY2 P ER0 K R IH1 T IH0 K AH0 L
+HYPERDRIVE HH AY1 P ER0 D R AY2 V
+HYPERINFLATION HH AY2 P ER0 IH0 N F L EY1 SH AH0 N
+HYPERION HH AY0 P IH1 R IY0 AH0 N
+HYPERKINETIC HH AY2 P ER0 K IH0 N EH1 T IH0 K
+HYPERLINK HH AY1 P ER0 L IH0 NG K
+HYPERLINKS HH AY1 P ER0 L IH0 NG K S
+HYPERMARKET HH AY1 P ER0 M AA2 R K IH0 T
+HYPERMARKETS HH AY1 P ER0 M AA2 R K IH0 T S
+HYPEROPIA HH AY2 P ER0 OW1 P IY2 AA0
+HYPERPLASIA HH AY2 P ER0 P L EY1 ZH AA0
+HYPERSENSITIVE HH AY2 P ER0 S EH1 N S IH0 T IH0 V
+HYPERSENSITIVITY HH AY2 P ER0 S EH1 N S IH0 T IH2 V IH0 T IY2
+HYPERSONIC HH AY2 P ER0 S AA1 N IH0 K
+HYPERSPACE HH AY2 P ER0 S P EY1 S
+HYPERTENSION HH AY2 P ER0 T EH1 N SH AH0 N
+HYPERTENSIVE HH AY2 P ER0 T EH1 N S IH0 V
+HYPERTEXT HH AY1 P ER0 T EH2 K S T
+HYPERTONIC HH AY2 P ER0 T AA1 N IH0 K
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+HYPERTROPHY HH AY1 P ER0 T R OW2 F IY0
+HYPES HH AY1 P S
+HYPHAE HH AY1 F IY2
+HYPHEN HH AY1 F AH0 N
+HYPHENATE HH AY1 F AH0 N EY2 T
+HYPHENATED HH AY1 F AH0 N EY2 T IH0 D
+HYPHENATES HH AY1 F AH0 N EY2 T S
+HYPHENATING HH AY1 F AH0 N EY2 T IH0 NG
+HYPHENATION HH AY2 F AH0 N EY1 SH AH0 N
+HYPING HH AY1 P IH0 NG
+HYPNOSIS HH IH0 P N OW1 S AH0 S
+HYPNOTIC HH IH0 P N AA1 T IH0 K
+HYPNOTICS HH IH0 P N AA1 T IH0 K S
+HYPNOTISM HH IH1 P N AH0 T IH2 Z AH0 M
+HYPNOTISM'S HH IH1 P N AH0 T IH2 Z AH0 M Z
+HYPNOTIST HH IH1 P N AH0 T IH0 S T
+HYPNOTISTS HH IH1 P N AH0 T IH0 S T S
+HYPNOTIZE HH IH1 P N AH0 T AY2 Z
+HYPNOTIZED HH IH1 P N AH0 T AY2 Z D
+HYPNOTIZES HH IH1 P N AH0 T AY2 Z AH0 Z
+HYPO HH AY1 P OW0
+HYPOCHONDRIA HH AY2 P AH0 K AA1 N D R IY0 AH0
+HYPOCHONDRIAC HH AY2 P AH0 K AA1 N D R IY0 AE0 K
+HYPOCRISY HH IH0 P AA1 K R AH0 S IY0
+HYPOCRITE HH IH1 P AH0 K R IH2 T
+HYPOCRITES HH IH1 P AH0 K R IH2 T S
+HYPOCRITICAL HH IH2 P AH0 K R IH1 T IH0 K AH0 L
+HYPODERMIC HH AY2 P AH0 D ER1 M IH0 K
+HYPOGLYCEMIA HH AY2 P OW0 G L AY0 S IY1 M IY0 AH0
+HYPOGLYCEMIC HH AY2 P OW0 G L AY0 S IY1 M IH0 K
+HYPOLITE HH AY1 P AH0 L AY0 T
+HYPONEX HH AY1 P OW0 N EH2 K S
+HYPOTENSION HH AY2 P OW0 T EH1 N SH AH0 N
+HYPOTHALAMIC HH AY2 P OW0 TH AH0 L AE1 M IH0 K
+HYPOTHALAMUS HH AY2 P OW0 TH AA1 L AH0 M AH0 S
+HYPOTHEKEN HH AY2 P AA1 TH AH0 K AH0 N
+HYPOTHERMIA HH AY2 P AH0 TH ER1 M IY0 AH0
+HYPOTHESES HH AY0 P AA1 TH AH0 S IY2 Z
+HYPOTHESIS HH AY0 P AA1 TH AH0 S AH0 S
+HYPOTHESIZE HH AY0 P AA1 TH AH0 S AY2 Z
+HYPOTHESIZED HH AY0 P AA1 TH AH0 S AY2 Z D
+HYPOTHETICAL HH AY2 P AH0 TH EH1 T AH0 K AH0 L
+HYPOTHETICAL(2) HH AY2 P AH0 TH EH1 T IH0 K AH0 L
+HYPOTHETICALLY HH AY2 P AH0 TH EH1 T IH0 K L IY0
+HYPOTHETICALS HH AY2 P AH0 TH EH1 T AH0 K AH0 L Z
+HYPOXIA HH AY0 P AA1 K S IY0 AH0
+HYRAXES HH AY1 R AE0 K S AH0 Z
+HYRE HH AY1 R
+HYSELL HH AY1 S AH0 L
+HYSER HH AY1 Z ER0
+HYSLOP HH AY1 S L AH0 P
+HYSON HH AY1 S AH0 N
+HYSONG HH AY1 S AO2 NG
+HYSSOP HH IH1 S AH0 P
+HYSTER HH IH1 S T ER0
+HYSTERECTOMIES HH IH2 S T ER0 EH1 K T AH0 M IY0
+HYSTERECTOMY HH IH2 S T ER0 EH1 K T AH0 M IY0
+HYSTERIA HH IH0 S T EH1 R IY0 AH0
+HYSTERIC HH IH2 S T EH1 R IH0 K
+HYSTERICAL HH IH0 S T EH1 R IH0 K AH0 L
+HYSTERICALLY HH IH2 S T EH1 R IH0 K L IY0
+HYSTERICS HH IH2 S T EH1 R IH0 K S
+HYUN HH AY1 AH0 N
+HYUN(2) HH Y AH1 N
+HYUNDAE HH Y AH1 N D EY2
+HYUNDAI HH Y AH1 N D EY2
+HYUNDAI(2) HH AH1 N D EY2
+HYUNDAI'S HH AH1 N D EY2 Z
+HYUNDAIS HH Y AH1 N D EY2 Z
+HYWELL HH AY1 W EH0 L
+HZ HH ER1 T Z
+I AY1
+I'D AY1 D
+I'ERS AY1 ER0 Z
+I'LL AY1 L
+I'M AY1 M
+I'M(2) AH0 M
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+I'VE AY1 V
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+I.'S AY1 Z
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+IACOBELLI IY0 AA2 K OW0 B EH1 L IY0
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+IACOBUCCI IY0 AA0 K OW0 B UW1 CH IY0
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+IACONA AY2 AH0 K OW1 N AH0
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+IAGO IY0 AA1 G OW0
+IAIN IY0 EY1 N
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+IAN IY1 AH0 N
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+IANNIELLO IY0 AA2 N IY0 EH1 L OW0
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+IDENTIFIER AY0 D EH1 N T AH0 F AY2 ER0
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+RESTRAINED R IY0 S T R EY1 N D
+RESTRAINING R IY0 S T R EY1 N IH0 NG
+RESTRAINS R IY0 S T R EY1 N Z
+RESTRAINT R IH0 S T R EY1 N T
+RESTRAINT(2) R IY0 S T R EY1 N T
+RESTRAINTS R IH0 S T R EY1 N T S
+RESTRAINTS(2) R IY0 S T R EY1 N T S
+RESTREPO R EH0 S T R EH1 P OW0
+RESTRICT R IY0 S T R IH1 K T
+RESTRICTED R IY0 S T R IH1 K T AH0 D
+RESTRICTED(2) R IY0 S T R IH1 K T IH0 D
+RESTRICTING R IY0 S T R IH1 K T IH0 NG
+RESTRICTION R IY0 S T R IH1 K SH AH0 N
+RESTRICTIONS R IY0 S T R IH1 K SH AH0 N Z
+RESTRICTIVE R IY0 S T R IH1 K T IH0 V
+RESTRICTIVENESS R AH0 S T R IH1 K T IH0 V N AH0 S
+RESTRICTS R IY0 S T R IH1 K T S
+RESTROOM R EH1 S T R UW2 M
+RESTROOMS R EH1 S T R UW2 M Z
+RESTRUCTURE R IY0 S T R AH1 K CH ER0
+RESTRUCTURED R IY0 S T R AH1 K CH ER0 D
+RESTRUCTURES R IY0 S T R AH1 K CH ER0 Z
+RESTRUCTURING R IY0 S T R AH1 K CH ER0 IH0 NG
+RESTRUCTURINGS R IY0 S T R AH1 K CH ER0 IH0 NG Z
+RESTS R EH1 S T S
+RESTYLE R IY0 S T AY1 L
+RESTYLED R IY0 S T AY1 L D
+RESUBMIT R IY2 S AH0 B M IH1 T
+RESUBMITTED R IY2 S AH0 B M IH1 T IH0 D
+RESUBMITTING R IY2 S AH0 B M IH1 T IH0 NG
+RESULT R IH0 Z AH1 L T
+RESULT(2) R IY0 Z AH1 L T
+RESULTANT R IY0 Z AH1 L T AH0 N T
+RESULTED R IH0 Z AH1 L T IH0 D
+RESULTED(2) R IY0 Z AH1 L T AH0 D
+RESULTED(3) R IY0 Z AH1 L T IH0 D
+RESULTING R IH0 Z AH1 L T IH0 NG
+RESULTING(2) R IY0 Z AH1 L T IH0 NG
+RESULTS R IH0 Z AH1 L T S
+RESULTS(2) R IY0 Z AH1 L T S
+RESUME R IH0 Z UW1 M
+RESUME(2) R IY0 Z UW1 M
+RESUME(3) R EH1 Z AH0 M EY2
+RESUMED R IH0 Z UW1 M D
+RESUMED(2) R IY0 Z UW1 M D
+RESUMES R IH0 Z UW1 M Z
+RESUMES(2) R IY0 Z UW1 M Z
+RESUMES(3) R EH1 Z AH0 M EY2 Z
+RESUMING R IH0 Z UW1 M IH0 NG
+RESUMING(2) R IY0 Z UW1 M IH0 NG
+RESUMPTION R IH0 Z AH1 M P SH AH0 N
+RESUMPTION(2) R IY0 Z AH1 M P SH AH0 N
+RESUMPTION(3) R IH0 Z AH1 M SH AH0 N
+RESUMPTION(4) R IY0 Z AH1 M SH AH0 N
+RESUPPLY R IY0 S AH0 P L AY1
+RESURFACE R IY0 S ER1 F AH0 S
+RESURFACED R IY0 S ER1 F AH0 S T
+RESURFACING R IY0 S ER1 F AH0 S IH0 NG
+RESURGENCE R IY0 S ER1 JH AH0 N S
+RESURGENCY R IH0 S ER1 JH AH0 N S IY0
+RESURGENCY(2) R IY0 S ER1 JH AH0 N S IY0
+RESURGENT R IH0 S ER1 JH AH0 N T
+RESURGENT(2) R IY0 S ER1 JH AH0 N T
+RESURGING R IY0 S ER1 JH IH0 NG
+RESURRECT R EH2 Z ER0 EH1 K T
+RESURRECTED R EH2 Z ER0 EH1 K T IH0 D
+RESURRECTING R EH2 Z ER0 EH1 K T IH0 NG
+RESURRECTION R EH2 Z ER0 EH1 K SH AH0 N
+RESUSCITATE R IH0 S AH1 S IH0 T EY2 T
+RESUSCITATE(2) R IY0 S AH1 S IH0 T EY2 T
+RESUSCITATED R IH0 S AH1 S IH0 T EY2 T IH0 D
+RESUSCITATING R IH0 S AH1 S IH0 T EY2 T IH0 NG
+RESUSCITATION R IH0 S AH2 S IH0 T EY1 SH AH0 N
+RESVERATROL R EH0 S V ER1 AH0 T R AO0 L
+RET R EH1 T
+RETA R EH1 T AH0
+RETABLOS R IY0 T AE1 B L OW0 S
+RETAIL R IY1 T EY2 L
+RETAILED R IY1 T EY2 L D
+RETAILER R IY1 T EY2 L ER0
+RETAILER'S R IY1 T EY2 L ER0 Z
+RETAILERS R IY1 T EY2 L ER0 Z
+RETAILERS' R IY1 T EY2 L ER0 Z
+RETAILING R IY1 T EY2 L IH0 NG
+RETAILING'S R IY1 T EY2 L IH0 NG Z
+RETAILS R IY1 T EY2 L Z
+RETAIN R IH0 T EY1 N
+RETAIN(2) R IY0 T EY1 N
+RETAINED R IH0 T EY1 N D
+RETAINED(2) R IY0 T EY1 N D
+RETAINER R IH0 T EY1 N ER0
+RETAINER(2) R IY0 T EY1 N ER0
+RETAINERS R IH0 T EY1 N ER0 Z
+RETAINING R IH0 T EY1 N IH0 NG
+RETAINING(2) R IY0 T EY1 N IH0 NG
+RETAINS R IH0 T EY1 N Z
+RETAINS(2) R IY0 T EY1 N Z
+RETAKE R IY1 T EY1 K
+RETAKE(2) R IY0 T EY1 K
+RETAKEN R IY0 T EY1 K AH0 N
+RETAKING R IY0 T EY1 K IH0 NG
+RETALIATE R IH0 T AE1 L IY0 EY2 T
+RETALIATE(2) R IY0 T AE1 L IY0 EY2 T
+RETALIATED R IH0 T AE1 L IY0 EY2 T IH0 D
+RETALIATED(2) R IY0 T AE1 L IY0 EY2 T IH0 D
+RETALIATING R IH0 T AE1 L IY0 EY2 T IH0 NG
+RETALIATION R IY0 T AE2 L IY0 EY1 SH AH0 N
+RETALIATORY R IY0 T AE1 L Y AH0 T AO2 R IY0
+RETANA R EH0 T AE1 N AH0
+RETARD R IH0 T AA1 R D
+RETARD(2) R IY0 T AA1 R D
+RETARDANT R IY0 T AA1 R D AH0 N T
+RETARDATION R IY0 T AA0 R D EY1 SH AH0 N
+RETARDED R IH0 T AA1 R D IH0 D
+RETARDED(2) R IY0 T AA1 R D AH0 D
+RETARDED(3) R IY0 T AA1 R D IH0 D
+RETARDING R IH0 T AA1 R D IH0 NG
+RETARDING(2) R IY0 T AA1 R D IH0 NG
+RETARDS R IH0 T AA1 R D Z
+RETARDS(2) R IY0 T AA1 R D Z
+RETARGET R IY0 T AA1 R G AH0 T
+RETARGETER R IY0 T AA1 R G AH0 T ER0
+RETARGETERS R IY0 T AA1 R G AH0 T ER0 Z
+RETARGETING R IY0 T AA1 R G AH0 T IH0 NG
+RETELL R IY0 T EH1 L
+RETELLING R IY0 T EH1 L IH0 NG
+RETEMEYER R EH1 T AH0 M AY2 R
+RETENTION R IY0 T EH1 N SH AH0 N
+RETEST R IY1 T EH1 S T
+RETESTED R IY0 T EH1 S T IH0 D
+RETESTING R IY0 T EH1 S T IH0 NG
+RETESTS R IY1 T EH1 S T S
+RETHERFORD R IH0 TH ER1 F ER0 D
+RETHINK R IY0 TH IH1 NG K
+RETHINKING R IY0 TH IH1 NG K IH0 NG
+RETHOUGHT R IY0 TH AO1 T
+RETI R EH1 T IY0
+RETICENCE R EH1 T IH0 S AH0 N S
+RETICENT R EH1 T IH0 S AH0 N T
+RETIN R EH1 T IH0 N
+RETINA R EH1 T AH0 N AH0
+RETINAL R EH1 T AH0 N AH0 L
+RETINOBLASTOMA R EH2 T IH0 N OW2 B L AE2 S T OW1 M AH0
+RETINOID R EH1 T IH0 N OY0 D
+RETINOIDS R EH1 T IH0 N OY0 D Z
+RETINOL R EH1 T IH0 N AO2 L
+RETINUE R EH1 T AH0 N UW2
+RETINYL R EH1 T IH0 N AH0 L
+RETIRE R IH0 T AY1 R
+RETIRE(2) R IY0 T AY1 R
+RETIRE(3) R IY2 T AY1 ER0
+RETIRED R IH0 T AY1 R D
+RETIRED(2) R IY0 T AY1 ER0 D
+RETIRED(3) R IY0 T AY1 R D
+RETIREE R IY0 T AY1 R IY1
+RETIREE'S R IH0 T AY2 R IY1 Z
+RETIREES R IY0 T AY1 R IY1 Z
+RETIREES' R IH0 T AY2 R IY1 Z
+RETIREMENT R IY0 T AY1 ER0 M AH0 N T
+RETIREMENT(2) R IH0 T AY1 ER0 M AH0 N T
+RETIREMENT'S R IH0 T AY1 R M AH0 N T S
+RETIREMENT'S(2) R IY0 T AY1 R M AH0 N T S
+RETIREMENTS R IH0 T AY1 R M AH0 N T S
+RETIREMENTS(2) R IY0 T AY1 R M AH0 N T S
+RETIRES R IH0 T AY1 R Z
+RETIRES(2) R IY0 T AY1 ER0 Z
+RETIRES(3) R IY0 T AY1 R Z
+RETIRING R IH0 T AY1 R IH0 NG
+RETIRING(2) R IY0 T AY1 ER0 IH0 NG
+RETIRING(3) R IY0 T AY1 R IH0 NG
+RETLIN R EH1 T L IH0 N
+RETO R IY1 T UW1
+RETOOK R IY0 T UH1 K
+RETOOL R IY0 T UW1 L
+RETOOLED R IY0 T UW1 L D
+RETOOLING R IY0 T UW1 L IH0 NG
+RETORT R IY1 T AO2 R T
+RETORTED R IY0 T AO1 R T IH0 D
+RETORTS R IH0 T AO1 R T S
+RETORTS(2) R IY0 T AO1 R T S
+RETOUCHING R IY0 T AH1 CH IH0 NG
+RETRACE R IY0 T R EY1 S
+RETRACED R IY0 T R EY1 S T
+RETRACEMENT R IY0 T R EY1 S M AH0 N T
+RETRACING R IY0 T R EY1 S IH0 NG
+RETRACT R IY0 T R AE1 K T
+RETRACTABLE R IY0 T R AE1 K T AH0 B AH0 L
+RETRACTED R IY0 T R AE1 K T AH0 D
+RETRACTING R IY0 T R AE1 K T IH0 NG
+RETRACTION R IY0 T R AE1 K SH AH0 N
+RETRACTS R IY0 T R AE1 K T S
+RETRAIN R IY0 T R EY1 N
+RETRAINED R IY0 T R EY1 N D
+RETRAINING R IY0 T R EY1 N IH0 NG
+RETRANSMISSION R IY2 T R AE0 N Z M IH1 SH AH0 N
+RETREAD R IY0 T R EH1 D
+RETREADING R IY0 T R EH1 D IH0 NG
+RETREADS R IY0 T R EH1 D Z
+RETREAT R IY0 T R IY1 T
+RETREATED R IY0 T R IY1 T AH0 D
+RETREATED(2) R IY0 T R IY1 T IH0 D
+RETREATING R IY0 T R IY1 T IH0 NG
+RETREATS R IY0 T R IY1 T S
+RETRENCH R IY0 T R EH1 N CH
+RETRENCHED R IY0 T R EH1 N CH T
+RETRENCHING R IY0 T R EH1 N CH IH0 NG
+RETRENCHMENT R IY0 T R EH1 N CH M AH0 N T
+RETRENCHMENTS R IY0 T R EH1 N CH M AH0 N T S
+RETRIAL R IY0 T R AY1 AH0 L
+RETRIBUTION R EH2 T R AH0 B Y UW1 SH AH0 N
+RETRIED R IY0 T R AY1 D
+RETRIEVAL R IH0 T R IY1 V AH0 L
+RETRIEVAL(2) R IY0 T R IY1 V AH0 L
+RETRIEVE R IH0 T R IY1 V
+RETRIEVE(2) R IY0 T R IY1 V
+RETRIEVED R IY0 T R IY1 V D
+RETRIEVER R IY0 T R IY1 V ER0
+RETRIEVERS R IY0 T R IY1 V ER0 Z
+RETRIEVES R IH0 T R IY1 V Z
+RETRIEVES(2) R IY0 T R IY1 V Z
+RETRIEVING R IY0 T R IY1 V IH0 NG
+RETRO R EH1 T R OW0
+RETROACTIVE R EH2 T R OW0 AE1 K T IH0 V
+RETROACTIVELY R EH2 T R OW0 AE1 K T IH0 V L IY0
+RETROACTIVITY R EH2 T R OW0 AE0 K T IH1 V IH0 T IY0
+RETROCESSION R EH2 T R OW0 S EH1 SH AH0 N
+RETROCESSIONARIES R EH2 T R OW0 S EH1 SH AH0 N EH0 R IY0 Z
+RETROCESSIONARY R EH2 T R OW0 S EH1 SH AH0 N EH0 R IY0
+RETROFIT R EH1 T R OW0 F IH2 T
+RETROFITS R EH1 T R OW0 F IH2 T S
+RETROFITTED R EH1 T R OW2 F IH2 T IH0 D
+RETROFITTER R EH1 T R OW2 F IH2 T ER0
+RETROFITTING R EH1 T R OW0 F IH2 T IH0 NG
+RETROGRADE R EH1 T R AH0 G R EY2 D
+RETROSPECT R EH1 T R AH0 S P EH2 K T
+RETROSPECTIVE R EH2 T R AH0 S P EH1 K T IH0 V
+RETROSPECTIVELY R EH2 T R OW0 S P EH1 K T IH0 V L IY0
+RETROVIR R EH1 T R OW0 V IH2 R
+RETROVIRUS R EH2 T R OW0 V AY1 R AH0 S
+RETROVIRUSES R EH2 T R OW0 V AY1 R AH0 S IH0 Z
+RETRY R IY0 T R AY1
+RETRYING R IY0 T R AY1 IH0 NG
+RETTBERG R EH1 T B ER0 G
+RETTER R EH1 T ER0
+RETTEW R EH1 CH UW0
+RETTIG R EH1 T IH0 G
+RETTINGER R EH1 T IH0 N JH ER0
+RETTINGER(2) R EH1 T IH0 NG ER0
+RETTKE R EH1 T K IY0
+RETTON R EH1 T AH0 N
+RETURN R IH0 T ER1 N
+RETURN(2) R IY0 T ER1 N
+RETURNABLE R IY0 T ER1 N AH0 B AH0 L
+RETURNED R IH0 T ER1 N D
+RETURNED(2) R IY0 T ER1 N D
+RETURNEE R IH0 T ER0 N IY1
+RETURNEES R IH0 T ER0 N IY1 Z
+RETURNING R IH0 T ER1 N IH0 NG
+RETURNING(2) R IY0 T ER1 N IH0 NG
+RETURNS R IH0 T ER1 N Z
+RETURNS(2) R IY0 T ER1 N Z
+RETURNS' R AH0 T ER1 N Z
+RETURNS'(2) R IY0 T ER1 N Z
+RETZ R EH1 T S
+RETZER R EH1 T Z ER0
+RETZLAFF R EH1 T Z L AH0 F
+RETZLOFF R EH1 T Z L AO0 F
+REUBEN R UW1 B AH0 N
+REUBER R OY1 B ER0
+REUL R UW1 L
+REULAND R OY1 L AH0 N D
+REULE R UW1 L
+REUM R IY1 AH0 M
+REUNIFICATION R IY0 UW2 N AH0 F AH0 K EY1 SH AH0 N
+REUNIFIED R IY0 UW1 N AH0 F AY2 D
+REUNIFY R IY0 UW1 N AH0 F AY2
+REUNION R IY0 UW1 N Y AH0 N
+REUNIONS R IY0 UW1 N Y AH0 N Z
+REUNITE R IY2 UW0 N AY1 T
+REUNITED R IY2 UW0 N AY1 T IH0 D
+REUNITES R IY2 UW0 N AY1 T S
+REUNITING R IY2 UW0 N AY1 T IH0 NG
+REUSABLE R IY0 UW1 Z AH0 B AH0 L
+REUSCH R OY1 SH
+REUSE R IY0 Y UW1 S
+REUSE(2) R IY0 Y UW1 Z
+REUSED R IY0 UW1 Z D
+REUSING R IY0 Y UW1 Z IH0 NG
+REUSS R UW1 S
+REUSSER R OY1 S ER0
+REUST R UW1 S T
+REUTER R OY1 T ER0
+REUTER'S R OY1 T ER0 Z
+REUTERS R OY1 T ER0 Z
+REUTERS' R OY1 T ER0 Z
+REUTERS'S R OY1 T ER0 Z IH0 Z
+REUTERS'S(2) R OY1 T ER0 Z
+REUTGERS R OY1 T G ER0 Z
+REUTHER R OY1 DH ER0
+REUTTER R OY1 T ER0
+REUTZEL R OY1 T Z AH0 L
+REV R EH1 V
+REVA R EY1 V AH0
+REVAK R EH1 V AH0 K
+REVALUATION R IY0 V AE1 L Y UW0 EY1 SH AH0 N
+REVALUATIONS R IY0 IH0 V AE2 L Y UW0 EY1 SH AH0 N Z
+REVALUE R IY0 V AE1 L Y UW2
+REVALUED R IY0 V AE1 L Y UW0 D
+REVALUING R IY0 V AE1 L Y UW0 IH0 NG
+REVAMP R IY0 V AE1 M P
+REVAMPED R IY0 V AE1 M P T
+REVAMPING R IY0 V AE1 M P IH0 NG
+REVAMPS R IY0 V AE1 M P S
+REVARD R IH0 V AA1 R D
+REVCO R EH1 V K OW0
+REVCO'S R EH1 V K OW0 Z
+REVEAL R IH0 V IY1 L
+REVEAL(2) R IY0 V IY1 L
+REVEALED R IH0 V IY1 L D
+REVEALED(2) R IY0 V IY1 L D
+REVEALING R IH0 V IY1 L IH0 NG
+REVEALING(2) R IY0 V IY1 L IH0 NG
+REVEALS R IH0 V IY1 L Z
+REVEALS(2) R IY0 V IY1 L Z
+REVEILLE R EH1 V AH0 L IY2
+REVEL R EH1 V AH0 L
+REVELATION R EH2 V AH0 L EY1 SH AH0 N
+REVELATIONS R EH2 V AH0 L EY1 SH AH0 N Z
+REVELATORY R IH0 V EH1 L AH0 T AO2 R IY0
+REVELED R EH1 V AH0 L D
+REVELER R EH1 V AH0 L ER0
+REVELERS R EH1 V AH0 L ER0 Z
+REVELES R EY0 V EY1 L EH0 S
+REVELING R EH1 V AH0 L IH0 NG
+REVELL R EY0 V EY1 L
+REVELLE R IH0 V EH1 L
+REVELLER R EH1 V AH0 L ER0
+REVELLERS R EH1 V AH0 L ER0 Z
+REVELLO R EH0 V EH1 L OW0
+REVELRY R EH1 V AH0 L R IY0
+REVELS R EH1 V AH0 L Z
+REVENGE R IY0 V EH1 N JH
+REVENUE R EH1 V AH0 N UW2
+REVENUE(2) R EH1 V AH0 N Y UW2
+REVENUER R EH1 V AH0 N UW2 ER0
+REVENUER(2) R EH1 V AH0 N Y UW2 ER0
+REVENUERS R EH1 V AH0 N UW2 ER0 Z
+REVENUERS(2) R EH1 V AH0 N Y UW2 ER0 Z
+REVENUES R EH1 V AH0 N UW2 Z
+REVENUES(2) R EH1 V AH0 N Y UW2 Z
+REVERB R IY0 V ER1 B
+REVERBED R IY0 V ER1 B D
+REVERBERATE R IH0 V ER1 B ER0 AH0 T
+REVERBERATE(2) R IH0 V ER1 B ER0 EY0 T
+REVERBERATED R IH0 V ER1 B ER0 EY2 T IH0 D
+REVERBERATED(2) R IY0 V ER1 B ER0 EY2 T AH0 D
+REVERBERATED(3) R IY0 V ER1 B ER0 EY2 T IH0 D
+REVERBERATES R IH0 V ER1 B ER0 EY2 T S
+REVERBERATING R IH0 V ER1 B ER0 EY2 T IH0 NG
+REVERBERATING(2) R IY0 V ER1 B ER0 EY2 T IH0 NG
+REVERBERATION R IY0 V ER2 B ER0 EY1 SH AH0 N
+REVERBERATIONS R IH0 V ER2 B ER0 EY1 SH AH0 N Z
+REVERCOMB R EH1 V ER0 K AH0 M
+REVERE R IH0 V IH1 R
+REVERED R IH0 V IH1 R D
+REVERENCE R EH1 V ER0 AH0 N S
+REVEREND R EH1 V ER0 AH0 N D
+REVEREND(2) R EH1 V R AH0 N D
+REVERENT R EH1 V ER0 AH0 N T
+REVERENTIAL R EH2 V ER0 EH1 N SH AH0 L
+REVERENTLY R EH1 V ER0 AH0 N T L IY0
+REVERES R IH0 V IH1 R Z
+REVERIE R EH1 V ER0 IY0
+REVERIES R EH1 V ER0 IY0 Z
+REVERING R IH0 V IH1 R IH0 NG
+REVERON R EH1 V ER0 AA2 N
+REVERON'S R EH1 V ER0 AA2 N Z
+REVERSAL R IH0 V ER1 S AH0 L
+REVERSAL(2) R IY0 V ER1 S AH0 L
+REVERSALS R IH0 V ER1 S AH0 L Z
+REVERSALS(2) R IY0 V ER1 S AH0 L Z
+REVERSE R IH0 V ER1 S
+REVERSE(2) R IY0 V ER1 S
+REVERSED R IH0 V ER1 S T
+REVERSED(2) R IY0 V ER1 S T
+REVERSER R IH0 V ER1 S ER0
+REVERSERS R IH0 V ER1 S ER0 Z
+REVERSES R IH0 V ER1 S IH0 Z
+REVERSES(2) R IY0 V ER1 S AH0 Z
+REVERSES(3) R IY0 V ER1 S IH0 Z
+REVERSIBLE R IH0 V ER1 S AH0 B AH0 L
+REVERSIBLE(2) R IY0 V ER1 S AH0 B AH0 L
+REVERSING R IH0 V ER1 S IH0 NG
+REVERSING(2) R IY0 V ER1 S IH0 NG
+REVERSION R IH0 V ER1 ZH AH0 N
+REVERT R IH0 V ER1 T
+REVERT(2) R IY0 V ER1 T
+REVERTED R IH0 V ER1 T IH0 D
+REVERTED(2) R IY0 V ER1 T IH0 D
+REVERTING R IH0 V ER1 T IH0 NG
+REVERTS R IH0 V ER1 T S
+REVES R IY1 V Z
+REVETTE R IH0 V EH1 T
+REVIER R IY1 V IY0 ER0
+REVIEW R IY2 V Y UW1
+REVIEW'S R IY0 V Y UW1 Z
+REVIEWED R IY0 V Y UW1 D
+REVIEWER R IY0 V Y UW1 ER0
+REVIEWER'S R IY0 V Y UW1 ER0 Z
+REVIEWERS R IY0 V Y UW1 ER0 Z
+REVIEWING R IY0 V Y UW1 IH0 NG
+REVIEWS R IY0 V Y UW1 Z
+REVIEWS(2) R AH0 V Y UW1 Z
+REVIGLIO R AH0 V IH1 G L IY0 OW0
+REVILE R IY0 V AY1 L
+REVILED R IY0 V AY1 L D
+REVILLA R EH0 V IH1 L AH0
+REVILLE R EH1 V IH2 L
+REVIS R EH1 V IH0 S
+REVISE R IH0 V AY1 Z
+REVISE(2) R IY0 V AY1 Z
+REVISED R IH0 V AY1 Z D
+REVISED(2) R IY0 V AY1 Z D
+REVISES R IH0 V AY1 Z IH0 Z
+REVISES(2) R IY0 V AY1 Z IH0 Z
+REVISING R IH0 V AY1 Z IH0 NG
+REVISING(2) R IY0 V AY1 Z IH0 NG
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diff --git a/utils/tts/gpt_sovits/text/cmudict.rep b/utils/tts/gpt_sovits/text/cmudict.rep
new file mode 100644
index 0000000000000000000000000000000000000000..eb4ee9e4c837e23580593929ae04abb37c100e39
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/cmudict.rep
@@ -0,0 +1,134429 @@
+;;; # CMUdict  --  Major Version: 0.07
+;;; 
+;;; # $HeadURL: https://svn.code.sf.net/p/cmusphinx/code/branches/cmudict/cmudict-0.7b $
+;;; # $Date:: 2015-07-15 12:34:30 -0400 (Wed, 15 Jul 2015)      $:
+;;; # $Id:: cmudict-0.7b 13083 2015-07-15 16:34:30Z air         $:
+;;; # $Rev:: 13083                                              $: 
+;;; # $Author:: air                                             $:
+;;;
+;;; #
+;;; # ========================================================================
+;;; # Copyright (C) 1993-2015 Carnegie Mellon University. All rights reserved.
+;;; #
+;;; # Redistribution and use in source and binary forms, with or without
+;;; # modification, are permitted provided that the following conditions
+;;; # are met:
+;;; #
+;;; # 1. Redistributions of source code must retain the above copyright
+;;; #    notice, this list of conditions and the following disclaimer.
+;;; #    The contents of this file are deemed to be source code.
+;;; #
+;;; # 2. Redistributions in binary form must reproduce the above copyright
+;;; #    notice, this list of conditions and the following disclaimer in
+;;; #    the documentation and/or other materials provided with the
+;;; #    distribution.
+;;; #
+;;; # This work was supported in part by funding from the Defense Advanced
+;;; # Research Projects Agency, the Office of Naval Research and the National
+;;; # Science Foundation of the United States of America, and by member
+;;; # companies of the Carnegie Mellon Sphinx Speech Consortium. We acknowledge
+;;; # the contributions of many volunteers to the expansion and improvement of
+;;; # this dictionary.
+;;; #
+;;; # THIS SOFTWARE IS PROVIDED BY CARNEGIE MELLON UNIVERSITY ``AS IS'' AND
+;;; # ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
+;;; # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+;;; # PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL CARNEGIE MELLON UNIVERSITY
+;;; # NOR ITS EMPLOYEES BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+;;; # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+;;; # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+;;; # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+;;; # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+;;; # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+;;; # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+;;; #
+;;; # ========================================================================
+;;; #
+;;;
+;;;  NOTES  ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
+;;; 
+;;;  [20080401] (air)  New dict file format introduced 
+;;;   - comments (like this section) are allowed
+;;;   - file name is major version; vers/rev information is now in the header
+;;;
+;;; 
+;;; ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
+;;; 
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+CONFUSION  K AH0 N F Y UW1 ZH AH0 N
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+CONG  K AO1 NG
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+CONGRESSPERSON  K AA1 NG G R AH0 S P ER2 S AH0 N
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+CONGRESSWOMAN  K AA1 NG G R AH0 S W UH2 M AH0 N
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+CONICS(1)  K OW1 N IH0 K S
+CONIFER  K AA1 N AH0 F ER0
+CONIFER'S  K AA1 N AH0 F ER0 Z
+CONIFEROUS  K AH0 N IH1 F ER0 AH0 S
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+CONISTON  K AA1 N AH0 S T AH0 N
+CONJECTURE  K AH0 N JH EH1 K CH ER0
+CONJECTURE(1)  K AH0 N JH EH1 K SH ER0
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+CONJECTURES(1)  K AH0 N JH EH1 K SH ER0 Z
+CONJOIN  K AA2 N JH OY1 N
+CONJOINED  K AA2 N JH OY1 N D
+CONJUGAL  K AA1 N JH AH0 G AH0 L
+CONJUGATE  K AA2 N JH AH0 G EY1 T
+CONJUGATE(1)  K AA1 N JH AH0 G EY2 T
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+CONJUGATION  K AA2 N JH AH0 G EY1 SH AH0 N
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+CONJUL  K AA1 N JH AH0 L
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+CONJUNCTIVA  K AA2 N JH AH0 NG K T AY1 V AH0
+CONJURE  K AA1 N JH ER0
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+CONJUROR  K AA1 N JH ER0 ER0
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+CONKEY  K AA1 N K IY0
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+CONKLIN  K AA1 NG K L IH0 N
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+CONNOISSEUR  K AA2 N AH0 S ER1
+CONNOISSEURS  K AA2 N AH0 S ER1 Z
+CONNOLE  K AA1 N AH0 L
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+CONNOTE  K AH0 N OW1 T
+CONNOTES  K AH0 N OW1 T S
+CONNY  K AA1 N IY0
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+CONOCO(1)  K AH0 N AA1 K OW0
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+CONOLY  K AA1 N OW0 L IY0
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+CONQUER  K AA1 NG K ER0
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+CONQUERORS  K AA1 NG K ER0 ER0 Z
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+CONSEQUENT  K AA1 N S AH0 K W AH0 N T
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+CONSERVATIVELY  K AH0 N S ER1 V AH0 T IH0 V L IY0
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+CONSIDERATION  K AH0 N S IH2 D ER0 EY1 SH AH0 N
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+CONSIDINE  K AA1 N S IH0 D AY2 N
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+CONSISTENT  K AH0 N S IH1 S T AH0 N T
+CONSISTENTLY  K AH0 N S IH1 S T AH0 N T L IY0
+CONSISTING  K AH0 N S IH1 S T IH0 NG
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+CONSOLIDATED  K AH0 N S AA1 L AH0 D EY2 T AH0 D
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+CONSOLIDATING  K AH0 N S AA1 L AH0 D EY2 T IH0 NG
+CONSOLIDATION  K AH0 N S AA2 L AH0 D EY1 SH AH0 N
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+CONSOLING  K AH0 N S OW1 L IH0 NG
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+CONSORTIUM  K AH0 N S AO1 R SH IY0 AH0 M
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+CONSORTIUM'S(1)  K AH0 N S AO1 R SH Y AH0 M Z
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+CONSORTIUMS(1)  K AH0 N S AO1 R SH Y AH0 M Z
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+CONSPICUOUSLY  K AH0 N S P IH1 K Y UW0 AH0 S L IY0
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+CONSPIRATORIALLY  K AH0 N S P IH2 R AH0 T AO1 R IY0 AH0 L IY0
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+CONSTANTINA  K AA0 N S T AA0 N T IY1 N AH0
+CONSTANTINE  K AA1 N S T AH0 N T IY2 N
+CONSTANTINE(1)  K AA1 N S T AH0 N T AY2 N
+CONSTANTINIDES  K AA0 N S T AH0 N T AH0 N IY1 D AH0 S
+CONSTANTINIDES(1)  K AA2 N S T AE0 N T AH0 N IY1 D IY0 Z
+CONSTANTINO  K AA2 N S T AH0 N T IY1 N OW0
+CONSTANTINOPLE  K AA2 N S T AE0 N T AH0 N OW1 P AH0 L
+CONSTANTINOS  K AA2 N S T AH0 N T IY1 N OW0 S
+CONSTANTLY  K AA1 N S T AH0 N T L IY0
+CONSTANTS  K AA1 N S T AH0 N T S
+CONSTAR  K AA1 N S T AA2 R
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+CONSTELLATION'S  K AA2 N S T AH0 L EY1 SH AH0 N Z
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+CONSTERNATION  K AA2 N S T ER0 N EY1 SH AH0 N
+CONSTIPATE  K AA1 N S T AH0 P EY2 T
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+CONSTIPATION  K AA2 N S T AH0 P EY1 SH AH0 N
+CONSTITUENCIES  K AH0 N S T IH1 CH UW0 AH0 N S IY0 Z
+CONSTITUENCY  K AH0 N S T IH1 CH UW0 AH0 N S IY0
+CONSTITUENT  K AH0 N S T IH1 CH UW0 AH0 N T
+CONSTITUENTS  K AH0 N S T IH1 CH UW0 AH0 N T S
+CONSTITUENTS'  K AH0 N S T IH1 CH UW0 AH0 N T S
+CONSTITUTE  K AA1 N S T AH0 T UW2 T
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+CONSTITUTION  K AA2 N S T AH0 T UW1 SH AH0 N
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+CONSTITUTIONAL  K AA2 N S T AH0 T UW1 SH AH0 N AH0 L
+CONSTITUTIONALITY  K AA2 N S T IH0 T UW2 SH AH0 N AE1 L IH0 T IY0
+CONSTITUTIONALLY  K AA2 N S T AH0 T UW1 SH AH0 N AH0 L IY0
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+CONSTITUTIONS  K AA2 N S T IH0 T UW1 SH AH0 N Z
+CONSTITUTIVE  K AA2 N S T IH1 T UW0 T IH2 V
+CONSTRAIN  K AH0 N S T R EY1 N
+CONSTRAINED  K AH0 N S T R EY1 N D
+CONSTRAINING  K AH0 N S T R EY1 N IH0 NG
+CONSTRAINS  K AH0 N S T R EY1 N Z
+CONSTRAINT  K AH0 N S T R EY1 N T
+CONSTRAINTS  K AH0 N S T R EY1 N T S
+CONSTRICT  K AH0 N S T R IH1 K T
+CONSTRICTED  K AH0 N S T R IH1 K T AH0 D
+CONSTRICTING  K AH0 N S T R IH1 K T IH0 NG
+CONSTRICTION  K AH0 N S T R IH1 K SH AH0 N
+CONSTRICTIONS  K AH0 N S T R IH1 K SH AH0 N Z
+CONSTRICTOR  K AH0 N S T R IH1 K T ER0
+CONSTRICTORS  K AH0 N S T R IH1 K T ER0 Z
+CONSTRUCCIONES  K AH0 N S T R UW1 CH IY0 OW2 N EY0 Z
+CONSTRUCT  K AH0 N S T R AH1 K T
+CONSTRUCT(1)  K AA1 N S T R AH0 K T
+CONSTRUCTED  K AH0 N S T R AH1 K T AH0 D
+CONSTRUCTED(1)  K AH0 N S T R AH1 K T IH0 D
+CONSTRUCTING  K AH0 N S T R AH1 K T IH0 NG
+CONSTRUCTION  K AH0 N S T R AH1 K SH AH0 N
+CONSTRUCTIONIST  K AH0 N S T R AH1 K SH AH0 N IH0 S T
+CONSTRUCTIONS  K AH0 N S T R AH1 K SH AH0 N Z
+CONSTRUCTIVE  K AH0 N S T R AH1 K T IH0 V
+CONSTRUCTIVELY  K AH0 N S T R AH1 K T IH0 V L IY0
+CONSTRUCTOR  K AH0 N S T R AH1 K T ER0
+CONSTRUCTORS  K AH0 N S T R AH1 K T ER0 Z
+CONSTRUCTS  K AH0 N S T R AH1 K T S
+CONSTRUCTS(1)  K AA1 N S T R AH0 K T S
+CONSTRUE  K AH0 N S T R UW1
+CONSTRUED  K AH0 N S T R UW1 D
+CONSUELA  K AH0 N S W EY1 L AH0
+CONSUL  K AA1 N S AH0 L
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+CONSULATE  K AA1 N S AH0 L AH0 T
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+CONSULS  K AA1 N S AH0 L Z
+CONSULSHIP  K AA1 N S AH0 L SH IH2 P
+CONSULT  K AH0 N S AH1 L T
+CONSULTANCY  K AH0 N S AH1 L T AH0 N S IY0
+CONSULTANT  K AH0 N S AH1 L T AH0 N T
+CONSULTANT'S  K AH0 N S AH1 L T AH0 N T S
+CONSULTANTS  K AH0 N S AH1 L T AH0 N T S
+CONSULTANTS'  K AH0 N S AH1 L T AH2 N T S
+CONSULTATION  K AA2 N S AH0 L T EY1 SH AH0 N
+CONSULTATIONS  K AA2 N S AH0 L T EY1 SH AH0 N Z
+CONSULTATIVE  K AH0 N S AH1 L T AH0 T IH0 V
+CONSULTED  K AH0 N S AH1 L T AH0 D
+CONSULTED(1)  K AH0 N S AH1 L T IH0 D
+CONSULTING  K AH0 N S AH1 L T IH0 NG
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+CONSUMABLE  K AH0 N S UW1 M AH0 B AH0 L
+CONSUME  K AH0 N S UW1 M
+CONSUMED  K AH0 N S UW1 M D
+CONSUMER  K AH0 N S UW1 M ER0
+CONSUMER'S  K AH0 N S UW1 M ER0 Z
+CONSUMERISM  K AH0 N S UW1 M ER0 IH2 Z AH0 M
+CONSUMERIST  K AH0 N S UW1 M ER0 IH0 S T
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+CONSUMERS  K AH0 N S UW1 M ER0 Z
+CONSUMERS'  K AH0 N S UW1 M ER0 Z
+CONSUMES  K AH0 N S UW1 M Z
+CONSUMING  K AH0 N S UW1 M IH0 NG
+CONSUMMATE  K AA1 N S AH0 M AH0 T
+CONSUMMATE(1)  K AA1 N S AH0 M EY2 T
+CONSUMMATED  K AA1 N S AH0 M EY2 T AH0 D
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+CONSUMMATING  K AA1 N S AH0 M EY2 T IH0 NG
+CONSUMMATION  K AA2 N S AH0 M EY1 SH AH0 N
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+CRAZY  K R EY1 Z IY0
+CREA  K R IY1
+CREACH  K R IY1 CH
+CREAGER  K R IY1 IH0 JH ER0
+CREAGH  K R IY1 G
+CREAK  K R IY1 K
+CREAKED  K R IY1 K T
+CREAKING  K R IY1 K IH0 NG
+CREAKY  K R IY1 K IY0
+CREAL  K R IY1 L
+CREAM  K R IY1 M
+CREAMED  K R IY1 M D
+CREAMER  K R IY1 M ER0
+CREAMERY  K R IY1 M ER0 IY0
+CREAMIER  K R IY1 M IY0 ER0
+CREAMIEST  K R IY1 M IY0 IH0 S T
+CREAMS  K R IY1 M Z
+CREAMY  K R IY1 M IY0
+CREAN  K R IY1 N
+CREAR  K R IH1 R
+CREASE  K R IY1 S
+CREASES  K R IY1 S IH0 Z
+CREASEY  K R IY1 S IY0
+CREASMAN  K R IY1 Z M AH0 N
+CREASON  K R IY1 S AH0 N
+CREASY  K R IY1 S IY0
+CREATE  K R IY0 EY1 T
+CREATE-A-BOOK  K R IY0 EY2 T AH0 B UH1 K
+CREATED  K R IY0 EY1 T AH0 D
+CREATED(1)  K R IY0 EY1 T IH0 D
+CREATES  K R IY0 EY1 T S
+CREATH  K R EH1 TH
+CREATING  K R IY0 EY1 T IH0 NG
+CREATION  K R IY0 EY1 SH AH0 N
+CREATIONISM  K R IY0 EY1 SH AH0 N IH2 Z AH0 M
+CREATIONS  K R IY0 EY1 SH AH0 N Z
+CREATIVE  K R IY0 EY1 T IH0 V
+CREATIVELY  K R IY0 EY1 T IH0 V L IY0
+CREATIVENESS  K R IY0 EY1 T IH0 V N AH0 S
+CREATIVITY  K R IY2 EY0 T IH1 V AH0 T IY0
+CREATOLOGIST  K R IY0 EY2 T AO1 L AH0 JH IH0 S T
+CREATOLOGISTS  K R IY0 EY2 T AO1 L AH0 JH IH0 S T S
+CREATOR  K R IY0 EY1 T ER0
+CREATORS  K R IY0 EY1 T ER0 Z
+CREATURE  K R IY1 CH ER0
+CREATURES  K R IY1 CH ER0 Z
+CRECELIUS  K R IH0 S IY1 L IY0 IH0 S
+CRECHE  K R EH1 SH
+CRECHES  K R EH1 SH AH0 Z
+CREDENCE  K R IY1 D AH0 N S
+CREDENTIAL  K R IH0 D EH1 N SH AH0 L
+CREDENTIAL(1)  K R AH0 D EH1 N SH AH0 L
+CREDENTIALED  K R AH0 D EH1 N SH AH0 L D
+CREDENTIALS  K R AH0 D EH1 N SH AH0 L Z
+CREDENZA  K R AH0 D EH1 N Z AH0
+CREDEUR  K R IH0 D ER1
+CREDIBILITY  K R EH2 D AH0 B IH1 L IH0 T IY0
+CREDIBLE  K R EH1 D AH0 B AH0 L
+CREDIBLY  K R EH1 D AH0 B L IY0
+CREDIT  K R EH1 D AH0 T
+CREDIT(1)  K R EH1 D IH0 T
+CREDIT'S  K R EH1 D IH0 T S
+CREDITABLE  K R EH1 D AH0 T AH0 B AH0 L
+CREDITABLY  K R EH1 D AH0 T AH0 B L IY0
+CREDITANSTALT  K R EH2 D IH1 T AH0 N SH T AO2 L T
+CREDITBANK  K R EH1 D IH0 T B AE2 NG K
+CREDITED  K R EH1 D AH0 T AH0 D
+CREDITED(1)  K R EH1 D IH0 T IH0 D
+CREDITHRIFT  K R EH2 D IH0 TH R IH1 F T
+CREDITING  K R EH1 D AH0 T IH0 NG
+CREDITO  K R EH0 D IY1 T OW0
+CREDITOR  K R EH1 D AH0 T ER0
+CREDITOR(1)  K R EH1 D IH0 T ER0
+CREDITORS  K R EH1 D IH0 T ER0 Z
+CREDITORS'  K R EH1 D IH0 T ER0 Z
+CREDITS  K R EH1 D IH0 T S
+CREDITWATCH  K R EH1 D IH0 T W AA2 CH
+CREDITWORTHINESS  K R EH1 D IH0 T W ER2 DH IY0 N AH0 S
+CREDITWORTHY  K R EH1 D IH0 T W ER2 DH IY0
+CREDLE  K R EH1 D AH0 L
+CREDO  K R EY1 D OW0
+CREDO(1)  K R IY1 D OW0
+CREDULITY  K R IH0 D UW1 L AH0 T IY0
+CREDULOUS  K R EH1 JH AH0 L AH0 S
+CREE  K R IY1
+CREECH  K R IY1 CH
+CREECY  K R IY1 S IY0
+CREED  K R IY1 D
+CREEDEN  K R IY1 D AH0 N
+CREEDON  K R IY1 D AH0 N
+CREEDS  K R IY1 D Z
+CREEGAN  K R IY1 G AH0 N
+CREEK  K R IY1 K
+CREEK'S  K R IY1 K S
+CREEKMORE  K R IY1 K M AO0 R
+CREEKMUR  K R IY1 K M ER0
+CREEKS  K R IY1 K S
+CREEL  K R IY1 L
+CREELMAN  K R IY1 L M AH0 N
+CREELY  K R IY1 L IY0
+CREEP  K R IY1 P
+CREEPINESS  K R IY1 P IY0 N IH0 S
+CREEPING  K R IY1 P IH0 NG
+CREEPS  K R IY1 P S
+CREEPY  K R IY1 P IY0
+CREER  K R IH1 R
+CREES  K R IY1 Z
+CREF  K R EH1 F
+CREF'S  K R EH1 F S
+CREGAN  K R IY1 G AH0 N
+CREGAR  K R IY1 G ER0
+CREGER  K R IY1 JH ER0
+CREGG  K R EH1 G
+CREGGER  K R EH1 G ER0
+CREGO  K R EH1 G OW0
+CREHAN  K R EH1 HH AH0 N
+CREIGHTON  K R EY1 T AH0 N
+CREKO  K R EH1 K OW0
+CRELLIN  K R EH1 L IH0 N
+CREMATE  K R IY1 M EY0 T
+CREMATED  K R IY1 M EY0 T IH0 D
+CREMATION  K R IY0 M EY1 SH AH0 N
+CREMATORIA  K R IY0 M AH0 T AO1 R IY0 AH0
+CREMATORIUM  K R IY0 M AH0 T AO1 R IY0 AH0 M
+CREME  K R IY1 M
+CREMEANS  K R EH1 M AH0 N Z
+CREMEENS  K R IH0 M IY1 N Z
+CREMER  K R IY1 M ER0
+CREMIN  K R EH1 M IH0 N
+CRENELATE  K R EH1 N AH0 L EY2 T
+CRENELATED  K R EH1 N AH0 L EY2 T AH0 D
+CRENSHAW  K R EH1 N SH AO2
+CRENWELGE  K R EH1 N W IH0 L JH
+CREOLE  K R IY1 OW0 L
+CREOLES  K R IY0 OW1 L Z
+CREOLIZE  K R IY1 OW2 L AY2 Z
+CREOLIZED  K R IY1 OW2 L AY2 Z D
+CREOSOTE  K R IY1 AH0 S OW2 T
+CREPE  K R EY1 P
+CREPEAU  K R IH0 P OW1
+CREPES  K R EY1 P S
+CREPS  K R EH1 P S
+CREPT  K R EH1 P T
+CREQUE  K R EH1 K
+CRESAP  K R EH1 S AH0 P
+CRESAP(1)  K R IY1 S AH0 P
+CRESCENDO  K R IH0 SH EH1 N D OW0
+CRESCENT  K R EH1 S AH0 N T
+CRESCENTS  K R EH1 S AH0 N T S
+CRESCENZI  K R EH0 S CH EH1 N Z IY0
+CRESCENZO  K R EH0 S CH EH1 N Z OW0
+CRESCI  K R EH1 S IY0
+CRESCOTT  K R EH1 S K AA0 T
+CRESON  K R EH1 S AH0 N
+CRESPI  K R EH1 S P IY0
+CRESPIN  K R EH1 S P IH0 N
+CRESPO  K R EH1 S P OW0
+CRESS  K R EH1 S
+CRESSES  K R EH1 S AH0 Z
+CRESSES(1)  K R EH1 S IH0 Z
+CRESSEY  K R EH1 S IY0
+CRESSLER  K R EH1 S L ER0
+CRESSMAN  K R EH1 S M AH0 N
+CRESSON  K R EH1 S AH0 N
+CRESSWELL  K R EH1 S W EH2 L
+CRESSY  K R EH1 S IY0
+CREST  K R EH1 S T
+CRESTAR  K R EH1 S T AA2 R
+CRESTED  K R EH1 S T IH0 D
+CRESTFALLEN  K R EH1 S T F AO2 L AH0 N
+CRESTING  K R EH1 S T IH0 NG
+CRESTMONT  K R EH1 S T M AA2 N T
+CRESTS  K R EH1 S T S
+CRESWELL  K R EH1 S W EH2 L
+CRETACEOUS  K R IH0 T EY1 SH IH0 S
+CRETCHEN  K R EH1 CH AH0 N
+CRETE  K R IY1 T
+CRETELLA  K R EH0 T EH1 L AH0
+CRETIEN  K R IY1 SH Y AH0 N
+CRETIN  K R IY1 T AH0 N
+CRETSINGER  K R EH1 T S IH0 N JH ER0
+CREUTZFELDT  K R UH1 T S F EH0 L D
+CREUTZFELDT(1)  K R UH1 T S F EH0 L T
+CREVASSE  K R AH0 V AE1 S
+CREVELING  K R EH1 V AH0 L IH0 NG
+CREVELING(1)  K R EH1 V L IH0 NG
+CREVICE  K R EH1 V AH0 S
+CREVICES  K R EH1 V AH0 S IH0 Z
+CREVIER  K R IY1 V IY0 ER0
+CREVISTON  K R EH1 V IH0 S T AA0 N
+CREW  K R UW1
+CREW'S  K R UW1 Z
+CREWE  K R UW1
+CREWEL  K R UW1 AH0 L
+CREWES  K R UW1 Z
+CREWMAN  K R UW1 M AH0 N
+CREWMEN  K R UW1 M IH0 N
+CREWS  K R UW1 Z
+CRIB  K R IH1 B
+CRIBARI  K R IY0 B AA1 R IY0
+CRIBB  K R IH1 B
+CRIBBAGE  K R IH1 B AH0 JH
+CRIBBING  K R IH1 B IH0 NG
+CRIBBS  K R IH1 B Z
+CRIBS  K R IH1 B Z
+CRICHLOW  K R IH1 K L OW0
+CRICHTON  K R IH1 CH T AH0 N
+CRICK  K R IH1 K
+CRICKET  K R IH1 K IH0 T
+CRICKET'S  K R IH1 K IH0 T S
+CRICKETS  K R IH1 K IH0 T S
+CRIDDLE  K R IH1 D AH0 L
+CRIDER  K R AY1 D ER0
+CRIED  K R AY1 D
+CRIER  K R AY1 ER0
+CRIES  K R AY1 Z
+CRIGER  K R AY1 JH ER0
+CRIGGER  K R IH1 G ER0
+CRIGLER  K R IH1 G L ER0
+CRIHFIELD  K R IH1 F IY0 L D
+CRILL  K R IH1 L
+CRILLEY  K R IH1 L IY0
+CRILLY  K R IH1 L IY0
+CRIM  K R IH1 M
+CRIME  K R AY1 M
+CRIME'S  K R AY1 M Z
+CRIMEA  K R AY0 M IY1 AH0
+CRIMEAN  K R IH0 M IY1 N
+CRIMEAN(1)  K R AY2 M IY1 AH0 N
+CRIMES  K R AY1 M Z
+CRIMI  K R IY1 M IY0
+CRIMINAL  K R IH1 M AH0 N AH0 L
+CRIMINAL'S  K R IH1 M AH0 N AH0 L Z
+CRIMINALIST  K R IH1 M AH0 N AH0 L IH0 S T
+CRIMINALIST'S  K R IH1 M AH0 N AH0 L IH0 S T S
+CRIMINALISTICS  K R IH2 M AH0 N AH0 L IH1 S T IH0 K S
+CRIMINALISTS  K R IH1 M AH0 N AH0 L IH0 S T S
+CRIMINALITY  K R IH2 M AH0 N AE1 L IH0 T IY0
+CRIMINALIZATION  K R IH2 M AH0 N AH0 L AH0 Z EY1 SH AH0 N
+CRIMINALIZE  K R IH1 M AH0 N AH0 L AY2 Z
+CRIMINALIZED  K R IH1 M AH0 N AH0 L AY2 Z D
+CRIMINALIZING  K R IH1 M AH0 N AH0 L AY2 Z IH0 NG
+CRIMINALLY  K R IH1 M AH0 N AH0 L IY0
+CRIMINALS  K R IH1 M AH0 N AH0 L Z
+CRIMINALS'  K R IH1 M AH0 N AH0 L Z
+CRIMINOLOGIST  K R IH2 M IH0 N AA1 L AH0 JH IH0 S T
+CRIMINOLOGISTS  K R IH2 M IH0 N AA1 L AH0 JH IH0 S T S
+CRIMINOLOGY  K R IH2 M IH0 N AA1 L AH0 JH IY0
+CRIMM  K R IH1 M
+CRIMMINS  K R IH1 M IH0 N Z
+CRIMP  K R IH1 M P
+CRIMPED  K R IH1 M P T
+CRIMPING  K R IH1 M P IH0 NG
+CRIMPS  K R IH1 M P S
+CRIMSON  K R IH1 M Z AH0 N
+CRINER  K R AY1 N ER0
+CRINGE  K R IH1 N JH
+CRINGED  K R IH1 N JH D
+CRINGES  K R IH1 N JH IH0 Z
+CRINGING  K R IH1 N JH IH0 NG
+CRIP  K R IH1 P
+CRIPE  K R AY1 P
+CRIPPEN  K R IH1 P AH0 N
+CRIPPLE  K R IH1 P AH0 L
+CRIPPLED  K R IH1 P AH0 L D
+CRIPPLES  K R IH1 P AH0 L Z
+CRIPPLING  K R IH1 P AH0 L IH0 NG
+CRIPPLING(1)  K R IH1 P L IH0 NG
+CRIPPS  K R IH1 P S
+CRIPS  K R IH1 P S
+CRIS  K R IH1 S
+CRISAFI  K R IY0 S AA1 F IY0
+CRISAFULLI  K R IY0 S AA0 F UW1 L IY0
+CRISANTI  K R IH0 S AE1 N T IY0
+CRISCI  K R IH1 S IY0
+CRISCIONE  K R IY0 S CH OW1 N IY0
+CRISCO  K R IY1 S K OW0
+CRISCUOLO  K R IY0 S K W OW1 L OW0
+CRISE  K R AY1 Z
+CRISES  K R AY1 S IY0 Z
+CRISHMAN  K R IH1 SH M AH0 N
+CRISHMAN'S  K R IH1 SH M AH0 N Z
+CRISIS  K R AY1 S AH0 S
+CRISLER  K R IH1 S AH0 L ER0
+CRISLER(1)  K R IH1 S L ER0
+CRISLIP  K R IH1 S L IH0 P
+CRISMAN  K R IH1 S M AH0 N
+CRISMON  K R IH1 Z M AH0 N
+CRISOSTOMO  K R IY0 S OW0 S T OW1 M OW0
+CRISP  K R IH1 S P
+CRISPELL  K R IH1 S P AH0 L
+CRISPEN  K R IH1 S P AH0 N
+CRISPER  K R IH1 S P ER0
+CRISPIN  K R IH1 S P IH0 N
+CRISPINA  K R IY0 S P IY1 N AH0
+CRISPINO  K R IY0 S P IY1 N OW0
+CRISPLY  K R IH1 S P L IY0
+CRISPNESS  K R IH1 S P N AH0 S
+CRISPO  K R IY1 S P OW0
+CRISPY  K R IH1 S P IY0
+CRISS  K R IH1 S
+CRISS-CROSS  K R IH1 S K R AO1 S
+CRISS-CROSSED  K R IH1 S K R AO1 S T
+CRISSCROSS  K R IH1 S K R AO2 S
+CRISSCROSSED  K R IH1 S K R AO2 S T
+CRISSCROSSING  K R IH1 S K R AO2 S IH0 NG
+CRISSEY  K R IH1 S IY0
+CRISSINGER  K R IH1 S IH0 N JH ER0
+CRISSLOW  K R IH1 S L OW0
+CRISSMAN  K R IH1 S M AH0 N
+CRIST  K R IH1 S T
+CRISTA  K R IH1 S T AH0
+CRISTABEL  K R IH1 S T AH0 B EH0 L
+CRISTAL  K R IH1 S T AH0 L
+CRISTIANI  K R IH2 S T IY0 AA1 N IY0
+CRISTIANI(1)  K R IH2 S CH IY0 AA1 N IY0
+CRISTIANI'S  K R IH2 S T IY0 AA1 N IY0 Z
+CRISTIANI'S(1)  K R IH2 S CH IY0 AA1 N IY0 Z
+CRISTIANO  K R IY0 S T IY0 AA1 N OW0
+CRISTINA  K R IH0 S T IY1 N AH0
+CRISTO  K R IH1 S T OW2
+CRISTO'S  K R IH1 S T OW2 Z
+CRISTOBAL  K R IH1 S T AH0 B AH0 L
+CRISTOBEL  K R IH1 S T AH0 B EH0 L
+CRISTOBEL'S  K R IH1 S T AH0 B EH0 L Z
+CRISWELL  K R IH1 S W EH0 L
+CRITCHER  K R IH1 CH ER0
+CRITCHFIELD  K R IH1 CH F IY0 L D
+CRITCHLEY  K R IH1 CH L IY0
+CRITCHLOW  K R IH1 CH L OW0
+CRITELLI  K R IH0 T EH1 L IY0
+CRITERIA  K R AY0 T IH1 R IY0 AH0
+CRITERION  K R AY0 T IH1 R IY0 AH0 N
+CRITES  K R AY1 T S
+CRITIC  K R IH1 T IH0 K
+CRITIC'S  K R IH1 T IH0 K S
+CRITICAL  K R IH1 T IH0 K AH0 L
+CRITICALITY  K R IH2 T AH0 K AE1 L AH0 T IY0
+CRITICALLY  K R IH1 T IH0 K AH0 L IY0
+CRITICALLY(1)  K R IH1 T IH0 K L IY0
+CRITICISM  K R IH1 T IH0 S IH2 Z AH0 M
+CRITICISMS  K R IH1 T IH0 S IH2 Z AH0 M Z
+CRITICIZE  K R IH1 T IH0 S AY2 Z
+CRITICIZED  K R IH1 T AH0 S AY2 Z D
+CRITICIZES  K R IH1 T IH0 S AY2 Z IH0 Z
+CRITICIZING  K R IH1 T IH0 S AY2 Z IH0 NG
+CRITICS  K R IH1 T IH0 K S
+CRITICS'  K R IH1 T IH0 K S
+CRITIQUE  K R AH0 T IY1 K
+CRITIQUE(1)  K R IH0 T IY1 K
+CRITIQUED  K R IH0 T IY1 K T
+CRITIQUES  K R IH0 T IY1 K S
+CRITIQUING  K R IH0 T IY1 K IH0 NG
+CRITON  K R AY1 T AH0 N
+CRITSER  K R IH1 T S ER0
+CRITTENDEN  K R IH0 T EH1 N D AH0 N
+CRITTENDON  K R IH1 T AH0 N D IH0 N
+CRITTER  K R IH1 T ER0
+CRITTERS  K R IH1 T ER0 Z
+CRITZ  K R IH1 T S
+CRITZER  K R IH1 T Z ER0
+CRIVARO  K R IH0 V AE1 R OW0
+CRIVELLI  K R IH0 V EH1 L IY0
+CRIVELLO  K R IH0 V EH1 L OW0
+CRIVEN  K R IH1 V AH0 N
+CRNKOVICH  S ER1 N AH0 V IH2 CH
+CRO  K R OW1
+CROAK  K R OW1 K
+CROAKER  K R OW1 K ER0
+CROAKING  K R OW1 K IH0 NG
+CROAT  K R OW1 AA2 T
+CROAT'S  K R OW1 AA2 T S
+CROATIA  K R OW0 EY1 SH AH0
+CROATIA'S  K R OW0 EY1 SH AH0 Z
+CROATIAN  K R OW0 EY1 SH AH0 N
+CROATIAN'S  K R OW0 EY1 SH AH0 N Z
+CROATIANS  K R OW0 EY1 SH AH0 N Z
+CROATIANS'  K R OW0 EY1 SH AH0 N Z
+CROATS  K R OW1 AA2 T S
+CROC  K R AA1 K
+CROCCO  K R AA1 K OW0
+CROCE  K R OW1 CH IY0
+CROCHET  K R OW0 SH EY1
+CROCHETED  K R OW0 SH EY1 D
+CROCHETIERE  K R OW2 SH AH0 T IH1 R
+CROCHETING  K R OW0 SH EY1 IH0 NG
+CROCK  K R AA1 K
+CROCKER  K R AA1 K ER0
+CROCKER'S  K R AA1 K ER0 Z
+CROCKERY  K R AA1 K ER0 IY0
+CROCKETT  K R AA1 K IH0 T
+CROCKFORD  K R AA1 K F ER0 D
+CROCODILE  K R AA1 K AH0 D AY2 L
+CROCODILES  K R AA1 K AH0 D AY2 L Z
+CROCODILIAN  K R AA2 K AH0 D IH1 L Y AH0 N
+CROCUS  K R OW1 K AH0 S
+CROCUSES  K R OW1 K Y UW0 S IH0 Z
+CROFF  K R AO1 F
+CROFFORD  K R AA1 F ER0 D
+CROFOOT  K R OW1 F UH0 T
+CROFT  K R AO1 F T
+CROFTON  K R AA1 F T AH0 N
+CROFTS  K R AO1 F T S
+CROGAN  K R OW1 G AH0 N
+CROGHAN  K R AA1 G AH0 N
+CROISSANT  K W AA2 S AA1 N T
+CROISSANT(1)  K R AH0 S AA1 N T
+CROISSANTS  K W AA2 S AA1 N T S
+CROISSANTS(1)  K R AH0 S AA1 N T S
+CROITZER  K R OY1 T S ER0
+CROIX  K R OY1
+CROIX'S  K R OY1 Z
+CROKE  K R OW1 K
+CROKER  K R OW1 K ER0
+CROLEY  K R OW1 L IY0
+CROLL  K R OW1 L
+CROM  K R AA1 M
+CROMARTIE  K R AA1 M AA0 R T IY0
+CROMBIE  K R AA1 M B IY0
+CROMER  K R OW1 M ER0
+CROMER'S  K R OW1 M ER0 Z
+CROMIE  K R AA1 M IY0
+CROMLEY  K R AA1 M L IY0
+CROMPTON  K R AA1 M P T AH0 N
+CROMWELL  K R AA1 M W AH0 L
+CROMWELL'S  K R AA1 M W IH0 L Z
+CRON  K R AA1 N
+CRONAN  K R OW1 N AH0 N
+CRONAUER  K R AA1 N AW0 R
+CRONCE  K R AA1 N S
+CRONE  K R OW1 N
+CRONEN  K R OW1 N AH0 N
+CRONER  K R OW1 N ER0
+CRONEY  K R OW1 N IY0
+CRONIES  K R OW1 N IY0 Z
+CRONIN  K R OW1 N IH0 N
+CRONK  K R AA1 NG K
+CRONKHITE  K R AA1 NG K HH AY2 T
+CRONKITE  K R AA1 NG K AY2 T
+CRONKRIGHT  K R AA1 NG K R AY2 T
+CRONQUIST  K R AA1 N K W IH2 S T
+CRONUS  K R OW1 N AH0 S
+CRONY  K R OW1 N IY0
+CRONYISM  K R OW1 N IY0 IH2 Z AH0 M
+CROOK  K R UH1 K
+CROOKE  K R UH1 K
+CROOKED  K R UH1 K AH0 D
+CROOKED-LEG  K R UH1 K AH0 D L EH2 G
+CROOKEDNESS  K R UH1 K AH0 D N AH0 S
+CROOKER  K R UH1 K ER0
+CROOKLYN  K R UH1 K L IH2 N
+CROOKLYN'S  K R UH1 K L IH2 N Z
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+DESPERATE(1)  D EH1 S P ER0 IH0 T
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+DESPERATELY(1)  D EH1 S P R AH0 T L IY0
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+DESTABILIZE  D IH0 S T EY1 B AH0 L AY2 Z
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+DESTEC  D EH1 S T EH2 K
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+DESTRUCTED  D IH0 S T R AH1 K T IH0 D
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+DETACH(1)  D IY0 T AE1 CH
+DETACHABLE  D IH0 T AE1 CH AH0 B AH0 L
+DETACHABLE(1)  D IY0 T AE1 CH AH0 B AH0 L
+DETACHED  D IH0 T AE1 CH T
+DETACHED(1)  D IY0 T AE1 CH T
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+DETACHES(1)  D IY0 T AE1 CH AH0 Z
+DETACHMENT  D IH0 T AE1 CH M AH0 N T
+DETACHMENT(1)  D IY0 T AE1 CH M AH0 N T
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+DETAIL(1)  D IY1 T EY0 L
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+DETAILER  D IY1 T EY0 L ER0
+DETAILING  D IH0 T EY1 L IH0 NG
+DETAILS  D IH0 T EY1 L Z
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+DETAINED  D IH0 T EY1 N D
+DETAINEE  D IY2 T EY0 N IY1
+DETAINEES  D IH0 T EY2 N IY1 Z
+DETAINING  D IH0 T EY1 N IH0 NG
+DETAMORE  D IH0 T AA1 M AO0 R
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+DETAR(1)  D IY0 T AA0 R
+DETECT  D IH0 T EH1 K T
+DETECTABLE  D IH0 T EH1 K T AH0 B AH0 L
+DETECTED  D IH0 T EH1 K T AH0 D
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+DETECTING  D IH0 T EH1 K T IH0 NG
+DETECTION  D IH0 T EH1 K SH AH0 N
+DETECTIVE  D IH0 T EH1 K T IH0 V
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+DETECTOR  D IH0 T EH1 K T ER0
+DETECTORS  D IH0 T EH1 K T ER0 Z
+DETECTS  D IH0 T EH1 K T S
+DETEMPLE  D EH1 T IH0 M P AH0 L
+DETENTE  D EY0 T AA1 N T
+DETENTION  D IH0 T EH1 N SH AH0 N
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+DETER  D IH0 T ER1
+DETERDING  D EH1 T ER0 D IH0 NG
+DETERGENT  D IH0 T ER1 JH AH0 N T
+DETERGENTS  D IH0 T ER1 JH AH0 N T S
+DETERIORATE  D IH0 T IH1 R IY0 ER0 EY2 T
+DETERIORATED  D IH0 T IH1 R IY0 ER0 EY2 T IH0 D
+DETERIORATES  D IH0 T IH1 R IY0 ER0 EY2 T S
+DETERIORATING  D IH0 T IH1 R IY0 ER0 EY2 T IH0 NG
+DETERIORATION  D IH0 T IH1 R IY0 ER0 EY2 SH AH0 N
+DETERMAN  D IY1 T ER0 M AH0 N
+DETERMENT  D IH0 T ER1 M AH0 N T
+DETERMINABLE  D IH0 T ER1 M AH0 N AH0 B AH0 L
+DETERMINANT  D IH0 T ER1 M AH0 N AH0 N T
+DETERMINANTS  D IH0 T ER1 M AH0 N AH0 N T S
+DETERMINATE  D IH0 T ER1 M AH0 N EY2 T
+DETERMINATION  D IH0 T ER2 M AH0 N EY1 SH AH0 N
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+DETERMINATIVE  D IH0 T ER1 M IH0 N AH0 T IH2 V
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+DETERMINEDLY(1)  D AH0 T ER1 M AH0 N D L IY0
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+DETERMINES(1)  D IH0 T ER1 M AH0 N Z
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+DETERMINISM  D IH0 T ER1 M AH0 N IH2 Z AH0 M
+DETERMINIST  D IH0 T ER1 M AH0 N AH0 S T
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+DETONATION  D EH2 T AH0 N EY1 SH AH0 N
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+DETOXICATION  D IH0 T AA2 K S AH0 K EY1 SH AH0 N
+DETOXIFICATION  D IH0 T AA2 K S IH0 F IH0 K EY1 SH AH0 N
+DETOXIFY  D IH0 T AA1 K S AH0 F AY2
+DETRACT  D IH0 T R AE1 K T
+DETRACTED  D IH0 T R AE1 K T IH0 D
+DETRACTING  D IH0 T R AE1 K T IH0 NG
+DETRACTOR  D IY0 T R AE1 K T ER0
+DETRACTORS  D IH0 T R AE1 K T ER0 Z
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+DETRICK  D EH1 T R IH0 K
+DETRIMENT  D EH1 T R AH0 M AH0 N T
+DETRIMENTAL  D EH2 T R AH0 M EH1 N T AH0 L
+DETRIMENTAL(1)  D EH2 T R AH0 M EH1 N AH0 L
+DETRITUS  D IH0 T R AY1 T AH0 S
+DETRITUS(1)  D EH1 T R AH0 T AH0 S
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+DETROIT  D IH0 T R OY1 T
+DETROIT(1)  D IY1 T R OY2 T
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+DETTINGER  D EH1 T IH0 N JH ER0
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+DETWEILER  D EH1 T W AY2 L ER0
+DETWILER  D EH1 T W AY2 L ER0
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+DINIUS  D AY1 N IY0 IH0 S
+DINK  D IH1 NG K
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+DINO  D IY1 N OW0
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+DIPLOMATIC  D IH2 P L AH0 M AE1 T IH0 K
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+DIRCKS  D ER1 K S
+DIRE  D AY1 R
+DIRE(1)  D AY1 ER0
+DIRECT  D ER0 EH1 K T
+DIRECT(1)  D AY0 R EH1 K T
+DIRECT(2)  D IH0 R EH1 K T
+DIRECTED  D ER0 EH1 K T AH0 D
+DIRECTED(1)  D ER0 EH1 K T IH0 D
+DIRECTED(2)  D AY0 R EH1 K T IH0 D
+DIRECTED(3)  D IH0 R EH1 K T IH0 D
+DIRECTING  D ER0 EH1 K T IH0 NG
+DIRECTING(1)  D IY0 R EH1 K T IH0 NG
+DIRECTING(2)  D AY0 R EH1 K T IH0 NG
+DIRECTING(3)  D IH0 R EH1 K T IH0 NG
+DIRECTION  D ER0 EH1 K SH AH0 N
+DIRECTION(1)  D IY0 R EH1 K SH IH0 N
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+DIRECTIONLESS(1)  D IY0 R EH1 K SH IH0 N L AH0 S
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+DIRECTIONS(1)  D IY0 R EH1 K SH IH0 N Z
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+DIRECTIVE  D ER0 EH1 K T IH0 V
+DIRECTIVE(1)  D IY0 R EH1 K T IH0 V
+DIRECTIVE(2)  D AY0 R EH1 K T IH0 V
+DIRECTIVE(3)  D IH0 R EH1 K T IH0 V
+DIRECTIVES  D AY0 R EH1 K T IH0 V Z
+DIRECTIVES(1)  D IY0 R EH1 K T IH0 V Z
+DIRECTIVES(2)  D ER0 EH1 K T IH0 V Z
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+DIRECTLY  D ER0 EH1 K T L IY0
+DIRECTLY(1)  D IY0 R EH1 K L IY0
+DIRECTLY(2)  D AY0 R EH1 K L IY0
+DIRECTLY(3)  D IH0 R EH1 K L IY0
+DIRECTNESS  D ER0 EH1 K T N AH0 S
+DIRECTNESS(1)  D IY0 R EH1 K N AH0 S
+DIRECTNESS(2)  D AY0 R EH1 K N AH0 S
+DIRECTNESS(3)  D IH0 R EH1 K N AH0 S
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+DIRECTOR(1)  D AY0 R EH1 K T ER0
+DIRECTOR(2)  D IY0 R EH1 K T ER0
+DIRECTOR(3)  D IH0 R EH1 K T ER0
+DIRECTOR'S  D AY0 R EH1 K T ER0 Z
+DIRECTOR'S(1)  D ER0 EH1 K T ER0 Z
+DIRECTOR'S(2)  D IY0 R EH1 K T ER0 Z
+DIRECTOR'S(3)  D IH0 R EH1 K T ER0 Z
+DIRECTORAL  D ER0 EH1 K T ER0 AH0 L
+DIRECTORATE  D ER0 EH1 K T ER0 AH0 T
+DIRECTORATE(1)  D AY0 R EH1 K T ER0 AH0 T
+DIRECTORATE(2)  D IY0 R EH1 K T ER0 AH0 T
+DIRECTORATE(3)  D IH0 R EH1 K T ER0 AH0 T
+DIRECTORIAL  D ER0 EH0 K T AO1 R IY0 AH0 L
+DIRECTORIAL(1)  D AY0 R EH0 K T AO1 R IY0 AH0 L
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+DIRECTORIES(1)  D ER0 EH1 K T ER0 IY0 Z
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+DIRECTORS(1)  D AY0 R EH1 K T ER0 Z
+DIRECTORS(2)  D IY0 R EH1 K T ER0 Z
+DIRECTORS(3)  D IH0 R EH1 K T ER0 Z
+DIRECTORS'  D IH0 R EH1 K T ER0 Z
+DIRECTORS'(1)  D ER0 EH1 K T ER0 Z
+DIRECTORS'(2)  D IY0 R EH1 K T ER0 Z
+DIRECTORSHIP  D ER0 EH1 K T ER0 SH IH2 P
+DIRECTORSHIP(1)  D AY0 R EH1 K T ER0 SH IH2 P
+DIRECTORSHIP(2)  D IY0 R EH1 K T ER0 SH IH2 P
+DIRECTORSHIP(3)  D IH0 R EH1 K T ER0 SH IH2 P
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+DIRECTORSHIPS(1)  D AY0 R EH1 K T ER0 SH IH2 P S
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+DIRECTORY  D ER0 EH1 K T ER0 IY0
+DIRECTORY(1)  D AY0 R EH1 K T ER0 IY0
+DIRECTORY(2)  D IY0 R EH1 K T ER0 IY0
+DIRECTORY(3)  D IH0 R EH1 K T ER0 IY0
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+DIRECTS(1)  D AY0 R EH1 K T S
+DIRECTS(2)  D IY0 R EH1 K T S
+DIRECTS(3)  D IH0 R EH1 K T S
+DIRECTV  D ER0 EH1 K T IY1 V IY1
+DIRECTV(1)  D AY0 R EH1 K T IY1 V IY1
+DIRECTV(2)  D IY0 R EH1 K T IY1 V IY1
+DIRECTV(3)  D IH0 R EH1 K T IY1 V IY1
+DIRENZO  D IH0 R EH1 N Z OW2
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+DIRGE  D ER1 JH
+DIRHAMS  D ER1 AH0 M Z
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+DIRIENZO  D IH0 R IY1 N Z OW2
+DIRK  D ER1 K
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+DIRKSE  D ER1 K S
+DIRKSEN  D ER1 K S AH0 N
+DIRLAM  D ER0 L AE1 M
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+DIRR  D ER1
+DIRT  D ER1 T
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+DIRUSSO  D IH0 R UW1 S OW2
+DIS  D IH1 S
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+DISABATO  D IH0 S AA0 B AA1 T OW0
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+DISADVANTAGE  D IH2 S AH0 D V AE1 N T IH0 JH
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+DISALLOWED  D IH2 S AH0 L AW1 D
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+DISASSEMBLE  D IH2 S AH0 S EH1 M B AH0 L
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+DISAVOW  D IH2 S AH0 V AW1
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+DISBAND  D IH0 S B AE1 N D
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+DISC  D IH1 S K
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+DISCARDING  D IH0 S K AA1 R D IH0 NG
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+DISCHARGING  D IH0 S CH AA1 R JH IH0 NG
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+DISCIPLE  D IH0 S AY1 P AH0 L
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+DISCIPLINARIANS  D IH2 S IH0 P L IH0 N EH1 R IY0 AH0 N Z
+DISCIPLINARY  D IH1 S AH0 P L AH0 N EH2 R IY0
+DISCIPLINE  D IH1 S AH0 P L AH0 N
+DISCIPLINED  D IH1 S AH0 P L AH0 N D
+DISCIPLINES  D IH1 S AH0 P L AH0 N Z
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+DISCLOSED  D IH0 S K L OW1 Z D
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+DISCOLORS  D IH0 S K AH1 L ER0 Z
+DISCOMBOBULATE  D IH2 S K AH0 M B AO1 B Y UW0 L EY0 T
+DISCOMBOBULATED  D IH2 S K AH0 M B AO1 B Y UW0 L EY0 T AH0 D
+DISCOMBOBULATES  D IH2 S K AH0 M B AO1 B Y UW0 L EY0 T S
+DISCOMBOBULATING  D IH2 S K AH0 M B AO1 B Y UW0 L EY0 T IH0 NG
+DISCOMFORT  D IH0 S K AH1 M F ER0 T
+DISCONCERT  D IH2 S K AH0 N S ER1 T
+DISCONCERTING  D IH2 S K AH0 N S ER1 T IH0 NG
+DISCONNECT  D IH0 S K AH0 N EH1 K T
+DISCONNECTED  D IH2 S K AH0 N EH1 K T IH0 D
+DISCONNECTING  D IH2 S K AH0 N EH1 K T IH0 NG
+DISCONNECTION  D IH2 S K AH0 N EH1 K SH AH0 N
+DISCONNECTS  D IH0 S K AH0 N EH1 K T S
+DISCONTENT  D IH0 S K AH0 N T EH1 N T
+DISCONTENTED  D IH2 S K AH0 N T EH1 N T IH0 D
+DISCONTENTEDLY  D IH2 S K AH0 N T EH1 N T IH0 D L IY0
+DISCONTENTEDNESS  D IH2 S K AH0 N T EH1 N T IH0 D N AH0 S
+DISCONTENTS  D IH2 S K AH0 N T EH1 N T S
+DISCONTINUANCE  D IH2 S K AH0 N T IH1 N Y UW0 AH0 N S
+DISCONTINUATION  D IH2 S K AH0 N T IH2 N Y UW0 EY1 SH AH0 N
+DISCONTINUE  D IH0 S K AH0 N T IH1 N Y UW0
+DISCONTINUED  D IH0 S K AH0 N T IH1 N Y UW0 D
+DISCONTINUING  D IH0 S K AH0 N T IH1 N Y UW0 IH0 NG
+DISCONTINUITY  D IH0 S K AA2 N T IH0 N UW1 IH0 T IY0
+DISCORD  D IH1 S K AO0 R D
+DISCORDANT  D IH0 S K AO1 R D AH0 N T
+DISCOS  D IH1 S K OW0 Z
+DISCOTHEQUE  D IH1 S K OW0 T EH2 K
+DISCOTHEQUES  D IH1 S K OW0 T EH2 K S
+DISCOUNT  D IH0 S K AW1 N T
+DISCOUNT(1)  D IH1 S K AW0 N T
+DISCOUNTABLE  D IH1 S K AW2 N T AH0 B AH0 L
+DISCOUNTED  D IH1 S K AW2 N T IH0 D
+DISCOUNTED(1)  D IH1 S K AW2 N IH0 D
+DISCOUNTER  D IH1 S K AW2 N T ER0
+DISCOUNTERS  D IH0 S K AW1 N T ER0 Z
+DISCOUNTERS(1)  D IH1 S K AW2 N ER0 R Z
+DISCOUNTING  D IH1 S K AW2 N T IH0 NG
+DISCOUNTING(1)  D IH1 S K AW2 N IH0 NG
+DISCOUNTS  D IH0 S K AW1 N T S
+DISCOUNTS(1)  D IH1 S K AW2 N T S
+DISCOURAGE  D IH0 S K ER1 IH0 JH
+DISCOURAGED  D IH0 S K ER1 IH0 JH D
+DISCOURAGEMENT  D IH0 S K ER1 IH0 JH M AH0 N T
+DISCOURAGES  D IH0 S K ER1 IH0 JH IH0 Z
+DISCOURAGING  D IH0 S K ER1 IH0 JH IH0 NG
+DISCOURSE  D IH1 S K AO0 R S
+DISCOURSES  D IH0 S K AO1 R S IH0 Z
+DISCOURSES(1)  D IH1 S K AO0 R S IH0 Z
+DISCOVER  D IH0 S K AH1 V ER0
+DISCOVERABLE  D IH0 S K AH1 V ER0 AH0 B AH0 L
+DISCOVERABLE(1)  D IH0 S K AH1 V R AH0 B AH0 L
+DISCOVERED  D IH0 S K AH1 V ER0 D
+DISCOVERER  D IH0 S K AH1 V AH0 R ER0
+DISCOVERERS  D IH0 S K AH1 V AH0 R ER0 Z
+DISCOVERIES  D IH0 S K AH1 V ER0 IY0 Z
+DISCOVERING  D IH0 S K AH1 V ER0 IH0 NG
+DISCOVERS  D IH0 S K AH1 V ER0 Z
+DISCOVERY  D IH0 S K AH1 V ER0 IY0
+DISCOVERY(1)  D IH0 S K AH1 V R IY0
+DISCOVERY(2)  D IH2 S K AH1 V R IY0
+DISCOVERY'S  D IH0 S K AH1 V ER0 IY0 Z
+DISCOVERY'S(1)  D IH0 S K AH1 V R IY0 Z
+DISCREDIT  D IH0 S K R EH1 D AH0 T
+DISCREDITED  D IH0 S K R EH1 D IH0 T IH0 D
+DISCREDITING  D IH0 S K R EH1 D AH0 T IH0 NG
+DISCREDITS  D IH0 S K R EH1 D AH0 T S
+DISCREET  D IH0 S K R IY1 T
+DISCREETLY  D IH0 S K R IY1 T L IY0
+DISCREPANCIES  D IH0 S K R EH1 P AH0 N S IY0 Z
+DISCREPANCY  D IH0 S K R EH1 P AH0 N S IY0
+DISCRETE  D IH0 S K R IY1 T
+DISCRETION  D IH0 S K R EH1 SH AH0 N
+DISCRETIONARY  D IH0 S K R EH1 SH AH0 N EH2 R IY0
+DISCRETIONS  D IH0 S K R EH1 SH AH0 N Z
+DISCRIMINATE  D IH0 S K R IH1 M AH0 N EY2 T
+DISCRIMINATED  D IH0 S K R IH1 M AH0 N EY2 T AH0 D
+DISCRIMINATED(1)  D IH0 S K R IH1 M AH0 N EY2 T IH0 D
+DISCRIMINATES  D IH0 S K R IH1 M AH0 N EY2 T S
+DISCRIMINATING  D IH0 S K R IH1 M AH0 N EY2 T IH0 NG
+DISCRIMINATION  D IH0 S K R IH2 M AH0 N EY1 SH AH0 N
+DISCRIMINATIVE  D IH0 S K R IH1 M AH0 N AH0 T IH0 V
+DISCRIMINATORY  D IH0 S K R IH1 M AH0 N AH0 T AO2 R IY0
+DISCS  D IH1 S K S
+DISCURSIVE  D IH0 S K ER1 S IH0 V
+DISCUS  D IH1 S K AH0 S
+DISCUSS  D IH0 S K AH1 S
+DISCUSSED  D IH0 S K AH1 S T
+DISCUSSES  D IH0 S K AH1 S AH0 Z
+DISCUSSES(1)  D IH0 S K AH1 S IH0 Z
+DISCUSSING  D IH0 S K AH1 S IH0 NG
+DISCUSSION  D IH0 S K AH1 SH AH0 N
+DISCUSSIONS  D IH0 S K AH1 SH AH0 N Z
+DISDAIN  D IH0 S D EY1 N
+DISDAINED  D IH0 S D EY1 N D
+DISDAINFUL  D IH0 S D EY1 N F AH0 L
+DISDAINING  D IH0 S D EY1 N IH0 NG
+DISDAINS  D IH0 S D EY1 N Z
+DISE  D AY1 S
+DISE(1)  D AY1 Z
+DISEASE  D IH0 Z IY1 Z
+DISEASE'S  D IH0 Z IY1 Z IH0 Z
+DISEASED  D IH0 Z IY1 Z D
+DISEASES  D IH0 Z IY1 Z AH0 Z
+DISEASES(1)  D IH0 Z IY1 Z IH0 Z
+DISEMBARK  D IH0 S EH0 M B AA1 R K
+DISEMBARKATION  D IH0 S EH2 M B AA0 R K EY1 SH AH0 N
+DISEMBODIED  D IH0 S IH0 B AA1 D IY0 D
+DISEMBODY  D IH0 S IH0 B AA1 D IY0
+DISENCHANTED  D IH0 S IH0 N CH AE1 N T IH0 D
+DISENCHANTED(1)  D IH0 S IH0 N CH AE1 N IH0 D
+DISENCHANTMENT  D IH0 S IH0 N CH AE1 N T M AH0 N T
+DISENFRANCHISE  D IH0 S IH0 N F R AE1 N CH AY2 Z
+DISENFRANCHISED  D IH0 S IH0 N F R AE1 N CH AY2 Z D
+DISENFRANCHISEMENT  D IH0 S IH0 N F R AE1 N CH AY2 Z M AH0 N T
+DISENGAGE  D IH0 S IH0 N G EY1 JH
+DISENGAGED  D IH0 S IH0 N G EY1 JH D
+DISENGAGEMENT  D IH0 S IH0 N G EY1 JH M AH0 N T
+DISENGAGING  D IH0 S IH0 N G EY1 JH IH0 NG
+DISENTANGLE  D IH2 S AH0 N T AE1 NG G AH0 L
+DISEQUILIBRIUM  D IH0 S IY2 K W AH0 L IH1 B R IY0 AH0 M
+DISESTABLISHMENT  D IH0 S IH0 S T AE1 B L IH0 SH M AH0 N T
+DISFAVOR  D IH0 S F EY1 V ER0
+DISFAVORING  D IH0 S F EY1 V ER0 IH0 NG
+DISFIGURE  D IH0 S F IH1 G Y ER0
+DISFIGURED  D IH0 S F IH1 G Y ER0 D
+DISFIGUREMENT  D IH0 S F IH1 G Y ER0 M EH0 N T
+DISFIGURING  D IH0 S F IH1 G Y ER0 IH0 NG
+DISGORGE  D IH0 S G AO1 R JH
+DISGORGED  D IH0 S G AO1 R JH D
+DISGORGEMENT  D IH0 S G AO1 R JH M AH0 N T
+DISGRACE  D IH0 S G R EY1 S
+DISGRACED  D IH0 S G R EY1 S T
+DISGRACEFUL  D IH0 S G R EY1 S F AH0 L
+DISGRUNTLED  D IH0 S G R AH1 N T AH0 L D
+DISGRUNTLED(1)  D IH0 S G R AH1 N AH0 L D
+DISGRUNTLEMENT  D IH0 S G R AH1 N T AH0 L M AH0 N T
+DISGRUNTLING  D IH0 S G R AH1 N T AH0 L IH0 NG
+DISGRUNTLING(1)  D IH0 S G R AH1 N T L IH0 NG
+DISGUISE  D IH0 S G AY1 Z
+DISGUISED  D IH0 S G AY1 Z D
+DISGUISES  D IH0 S G AY1 Z IH0 Z
+DISGUISING  D IH0 S G AY1 Z IH0 NG
+DISGUST  D IH0 S G AH1 S T
+DISGUSTED  D IH0 S G AH1 S T AH0 D
+DISGUSTED(1)  D IH0 S G AH1 S T IH0 D
+DISGUSTING  D IH0 S G AH1 S T IH0 NG
+DISGUSTS  D IH0 S G AH1 S T S
+DISH  D IH1 SH
+DISHARMONY  D IH0 S HH AA1 R M AH0 N IY0
+DISHAROON  D IH0 SH ER0 UW1 N
+DISHAW  D IH1 SH AO2
+DISHEARTENED  D IH0 S HH AA1 R T AH0 N D
+DISHEARTENING  D IH0 S HH AA1 R T AH0 N IH0 NG
+DISHEARTENING(1)  D IH2 S HH AA1 R T N IH0 NG
+DISHED  D IH1 SH T
+DISHER  D IH1 SH ER0
+DISHES  D IH1 SH AH0 Z
+DISHES(1)  D IH1 SH IH0 Z
+DISHEVEL  D IH0 SH EH1 V AH0 L
+DISHEVELED  D IH0 SH EH1 V AH0 L D
+DISHING  D IH1 SH IH0 NG
+DISHMAN  D IH1 SH M AH0 N
+DISHNER  D IH1 SH N ER0
+DISHON  D IH1 S AH0 N
+DISHONEST  D IH0 S AA1 N AH0 S T
+DISHONESTLY  D IH0 S AO1 N AH0 S T L IY0
+DISHONESTY  D IH0 S AA1 N AH0 S T IY0
+DISHONG  D IH1 S AO0 NG
+DISHONOR  D IH0 S AA1 N ER0
+DISHONORABLE  D IH0 S AA1 N ER0 AH0 B AH0 L
+DISHONORED  D IH0 S AA1 N ER0 D
+DISHWASHER  D IH1 SH W AA2 SH ER0
+DISHWASHERS  D IH1 SH W AA2 SH ER0 Z
+DISHWASHING  D IH1 SH W AA2 SH IH0 NG
+DISILLUSION  D IH2 S IH0 L UW1 ZH AH0 N
+DISILLUSIONED  D IH2 S IH0 L UW1 ZH AH0 N D
+DISILLUSIONING  D IH2 S IH0 L UW1 ZH AH0 N IH0 NG
+DISILLUSIONMENT  D IH2 S IH0 L UW1 ZH AH0 N M AH0 N T
+DISILVESTRO  D IH0 S IY0 L V EH1 S T R OW0
+DISIMONE  D IH0 S IY0 M OW1 N IY0
+DISINCENTIVE  D IH2 S IH0 N S EH1 N T IH0 V
+DISINCENTIVES  D IH2 S IH0 N S EH1 N T IH0 V Z
+DISINCLINATION  D IH0 S IH0 N K L AH0 N EY1 SH AH0 N
+DISINCLINE  D IH2 S IH0 N K L AY1 N
+DISINCLINED  D IH2 S IH0 N K L AY1 N D
+DISINFECT  D IH0 S IH0 N F EH1 K T
+DISINFECTANT  D IH0 S IH0 N F EH1 K T AH0 N T
+DISINFECTANTS  D IH0 S IH0 N F EH1 K T AH0 N T S
+DISINFECTION  D IH0 S IH0 N F EH1 K SH AH0 N
+DISINFLATE  D IH2 S IH0 N F L EY1 T
+DISINFLATION  D IH2 S IH0 N F L EY1 SH AH0 N
+DISINFLATIONARY  D IH2 S IH0 N F L EY1 SH AH0 N EH2 R IY0
+DISINFORM  D IH0 S IH0 N F AO1 R M
+DISINFORMATION  D IH0 Z IH2 N F ER0 M EY1 SH AH0 N
+DISINGENUOUS  D IH0 S IH0 N JH EH1 N Y UW0 AH0 S
+DISINGENUOUSNESS  D IH2 S IH0 N JH EH1 N Y UW0 AH0 S N AH0 S
+DISINTEGRATE  D IH0 S IH1 N T AH0 G R EY2 T
+DISINTEGRATED  D IH0 S IH1 N T AH0 G R EY2 T IH0 D
+DISINTEGRATES  D IH0 S IH1 N T AH0 G R EY2 T S
+DISINTEGRATING  D IH0 S IH1 N T AH0 G R EY2 T IH0 NG
+DISINTEGRATION  D IH0 S IH2 N T AH0 G R EY1 SH AH0 N
+DISINTEGRATION(1)  D IH0 S IH2 N AH0 G R EY1 SH AH0 N
+DISINTEREST  D IH0 S IH1 N T ER0 AH0 S T
+DISINTERESTED  D IH0 S IH1 N T R IH0 S T IH0 D
+DISINTERESTED(1)  D IH0 S IH1 N R IH0 S T IH0 D
+DISINVESTMENT  D IH0 S IH0 N V EH1 S T M AH0 N T
+DISINVESTMENT(1)  D IH0 S IH0 N V EH1 S M AH0 N T
+DISINVITE  D IH0 S IH0 N V AY1 T
+DISINVITED  D IH2 S IH2 N V AY1 T IH0 D
+DISJOINT  D IH0 S JH OY1 N T
+DISJOINTED  D IH0 S JH OY1 N T IH0 D
+DISK  D IH1 S K
+DISKETTE  D IH0 S K EH1 T
+DISKETTES  D IH0 S K EH1 T S
+DISKIN  D IH1 S K IH0 N
+DISKLESS  D IH1 S K L AH0 S
+DISKS  D IH1 S K S
+DISLIKE  D IH0 S L AY1 K
+DISLIKED  D IH0 S L AY1 K T
+DISLIKES  D IH0 S L AY1 K S
+DISLIKING  D IH0 S L AY1 K IH0 NG
+DISLOCATE  D IH1 S L OW0 K EY0 T
+DISLOCATED  D IH1 S L OW0 K EY0 T IH0 D
+DISLOCATION  D IH0 S L OW1 K EY1 SH AH0 N
+DISLOCATIONS  D IH0 S L OW1 K EY1 SH AH0 N Z
+DISLODGE  D IH0 S L AA1 JH
+DISLODGED  D IH0 S L AA1 JH D
+DISLODGING  D IH0 S L AA1 JH IH0 NG
+DISLOYAL  D IH0 S L OY1 AH0 L
+DISLOYALTY  D IH0 S L OY1 AH0 L T IY0
+DISMAL  D IH1 Z M AH0 L
+DISMALLY  D IH1 Z M AH0 L IY0
+DISMANTLE  D IH0 S M AE1 N T AH0 L
+DISMANTLE(1)  D IH0 S M AE1 N AH0 L
+DISMANTLED  D IH0 S M AE1 N T AH0 L D
+DISMANTLED(1)  D IH0 S M AE1 N AH0 L D
+DISMANTLEMENT  D IH0 S M AE1 N T AH0 L M AH0 N T
+DISMANTLES  D IH0 S M AE1 N T AH0 L Z
+DISMANTLING  D IH0 S M AE1 N T AH0 L IH0 NG
+DISMANTLING(1)  D IH0 S M AE1 N AH0 L IH0 NG
+DISMANTLING(2)  D IH2 S M AE1 N T L IH0 NG
+DISMAY  D IH0 S M EY1
+DISMAYED  D IH0 S M EY1 D
+DISMAYING  D IH0 S M EY1 IH0 NG
+DISMAYS  D IH0 S M EY1 Z
+DISMEMBER  D IH0 S M EH1 M B ER0
+DISMEMBERED  D IH0 S M EH1 M B ER0 D
+DISMEMBERING  D IH0 S M EH1 M B ER0 IH0 NG
+DISMEMBERMENT  D IH0 S M EH1 M B ER0 M AH0 N T
+DISMISS  D IH0 S M IH1 S
+DISMISSAL  D IH0 S M IH1 S AH0 L
+DISMISSALS  D IH0 S M IH1 S AH0 L Z
+DISMISSED  D IH0 S M IH1 S T
+DISMISSES  D IH0 S M IH1 S IH0 Z
+DISMISSING  D IH0 S M IH1 S IH0 NG
+DISMISSIVE  D IH0 S M IH1 S IH0 V
+DISMORE  D IH1 S M AO0 R
+DISMUKE  D IH1 S M Y UW0 K
+DISMUKES  D IH1 S M Y UW0 K S
+DISMUTASE  D IH2 S M Y UW0 T EY1 Z
+DISNEY  D IH1 Z N IY0
+DISNEY'S  D IH1 Z N IY0 Z
+DISNEYLAND  D IH1 Z N IY0 L AE2 N D
+DISNEYWORLD  D IH1 Z N IY0 W ER0 L D
+DISOBEDIENCE  D IH2 S AH0 B IY1 D IY0 AH0 N S
+DISOBEDIENCE(1)  D IH2 S OW0 B IY1 D IY0 AH0 N S
+DISOBEDIENT  D IH2 S AH0 B IY1 D IY0 AH0 N T
+DISOBEDIENT(1)  D IH2 S OW0 B IY1 D IY0 AH0 N T
+DISOBEY  D IH2 S AH0 B EY1
+DISOBEYED  D IH2 S OW0 B EY1 D
+DISOBEYING  D IH2 S OW0 B EY1 IH0 NG
+DISON  D IH1 S AH0 N
+DISORDER  D IH0 S AO1 R D ER0
+DISORDERED  D IH0 S AO1 R D ER0 D
+DISORDERLY  D IH0 S AO1 R D ER0 L IY0
+DISORDERS  D IH0 S AO1 R D ER0 Z
+DISORGANIZATION  D IH0 S AO2 R G AH0 N AH0 Z EY1 SH AH0 N
+DISORGANIZE  D IH0 S AO1 R G AH0 N AY2 Z
+DISORGANIZED  D IH0 S AO1 R G AH0 N AY2 Z D
+DISORIENT  D IH0 S AO1 R IY0 EH2 N T
+DISORIENTATION  D IH0 S AO2 R IY0 AH0 N T EY1 SH AH0 N
+DISORIENTED  D IH0 S AO1 R IY0 EH2 N T IH0 D
+DISORIENTING  D IH0 S AO1 R IY0 EH2 N T IH0 NG
+DISOWN  D IH0 S OW1 N
+DISOWNED  D IH0 S OW1 N D
+DISPAIR  D IH0 S P EH1 R
+DISPARAGE  D IH0 S P EH1 R IH0 JH
+DISPARAGED  D IH0 S P EH1 R IH0 JH D
+DISPARAGES  D IH0 S P EH1 R IH0 JH IH0 Z
+DISPARAGING  D IH0 S P EH1 R IH0 JH IH0 NG
+DISPARAGINGLY  D IH0 S P EH1 R IH0 JH IH0 NG L IY0
+DISPARATE  D IH1 S P ER0 IH0 T
+DISPARATE(1)  D IH0 S P EH1 R IH0 T
+DISPARITIES  D IH0 S P EH1 R AH0 T IY0 Z
+DISPARITY  D IH0 S P EH1 R AH0 T IY0
+DISPASSIONATE  D IH0 S P AE1 SH AH0 N AH0 T
+DISPASSIONATELY  D IH0 S P AE1 SH AH0 N AH0 T L IY0
+DISPATCH  D IH0 S P AE1 CH
+DISPATCHED  D IH0 S P AE1 CH T
+DISPATCHER  D IH0 S P AE1 CH ER0
+DISPATCHER(1)  D IH1 S P AE2 CH ER0
+DISPATCHER'S  D IH0 S P AE1 CH ER0 Z
+DISPATCHER'S(1)  D IH1 S P AE2 CH ER0 Z
+DISPATCHERS  D IH0 S P AE1 CH ER0 Z
+DISPATCHES  D IH0 S P AE1 CH IH0 Z
+DISPATCHING  D IH0 S P AE1 CH IH0 NG
+DISPEL  D IH0 S P EH1 L
+DISPELL  D IH0 S P EH1 L
+DISPELLED  D IH0 S P EH1 L D
+DISPELLING  D IH0 S P EH1 L IH0 NG
+DISPELS  D IH0 S P EH1 L Z
+DISPENSABLE  D IH0 S P EH1 N S AH0 B AH0 L
+DISPENSARY  D IH0 S P EH1 N S ER0 IY0
+DISPENSARY(1)  D IH1 S P EH0 N S EH2 R IY0
+DISPENSATION  D IH2 S P AH0 N S EY1 SH AH0 N
+DISPENSE  D IH0 S P EH1 N S
+DISPENSED  D IH0 S P EH1 N S T
+DISPENSER  D IH0 S P EH1 N S ER0
+DISPENSERS  D IH0 S P EH1 N S ER0 Z
+DISPENSES  D IH0 S P EH1 N S IH0 Z
+DISPENSING  D IH0 S P EH1 N S IH0 NG
+DISPENZA  D IH0 S P EH1 N Z AH0
+DISPERSAL  D IH0 S P ER1 S AH0 L
+DISPERSANT  D IH2 S P ER1 S AH0 N T
+DISPERSANTS  D IH2 S P ER1 S AH0 N T S
+DISPERSE  D IH0 S P ER1 S
+DISPERSED  D IH0 S P ER1 S T
+DISPERSING  D IH0 S P ER1 S IH0 NG
+DISPERSION  D IH0 S P ER1 ZH AH0 N
+DISPERSIVE  D IH0 S P ER1 S IH0 V
+DISPIRITED  D IH0 S P IH1 R AH0 T IH0 D
+DISPIRITING  D IH0 S P IH1 R IH0 T IH0 NG
+DISPLACE  D IH0 S P L EY1 S
+DISPLACED  D IH0 S P L EY1 S T
+DISPLACEMENT  D IH0 S P L EY1 S M AH0 N T
+DISPLACEMENTS  D IH0 S P L EY1 S M AH0 N T S
+DISPLACES  D IH0 S P L EY1 S IH0 Z
+DISPLACING  D IH0 S P L EY1 S IH0 NG
+DISPLAY  D IH0 S P L EY1
+DISPLAYED  D IH0 S P L EY1 D
+DISPLAYING  D IH0 S P L EY1 IH0 NG
+DISPLAYS  D IH0 S P L EY1 Z
+DISPLAYWRITE  D IH0 S P L EY1 R AY2 T
+DISPLAYWRITER  D IH0 S P L EY1 R AY2 T ER0
+DISPLEASE  D IH0 S P L IY1 Z
+DISPLEASED  D IH0 S P L IY1 Z D
+DISPLEASURE  D IH0 S P L EH1 ZH ER0
+DISPOSABLE  D IH0 S P OW1 Z AH0 B AH0 L
+DISPOSABLES  D IH0 S P OW1 Z AH0 B AH0 L Z
+DISPOSAL  D IH0 S P OW1 Z AH0 L
+DISPOSALS  D IH0 S P OW1 Z AH0 L Z
+DISPOSE  D IH0 S P OW1 Z
+DISPOSED  D IH0 S P OW1 Z D
+DISPOSER  D IH0 S P OW1 Z ER0
+DISPOSES  D IH0 S P OW1 Z IH0 Z
+DISPOSING  D IH0 S P OW1 Z IH0 NG
+DISPOSITION  D IH2 S P AH0 Z IH1 SH AH0 N
+DISPOSITIONS  D IH2 S P AH0 Z IH1 SH AH0 N Z
+DISPOSITIVE  D IH2 S P AA1 Z AH0 T IH0 V
+DISPOSSESS  D IH2 S P AH0 Z EH1 S
+DISPOSSESSED  D IH2 S P AH0 Z EH1 S T
+DISPROPORTIONATE  D IH2 S P R AH0 P AO1 R SH AH0 N IH0 T
+DISPROPORTIONATELY  D IH2 S P R AH0 P AO1 R SH AH0 N AH0 T L IY0
+DISPROVE  D IH0 S P R UW1 V
+DISPROVED  D IH0 S P R UW1 V D
+DISPROVEN  D IH0 S P R UW1 V IH0 N
+DISPROVES  D IH0 S P R UW1 V Z
+DISPUTABLE  D IH0 S P Y UW1 T AH0 B AH0 L
+DISPUTATION  D IH0 S P Y UW1 T EY1 SH AH0 N
+DISPUTE  D IH0 S P Y UW1 T
+DISPUTED  D IH0 S P Y UW1 T AH0 D
+DISPUTED(1)  D IH0 S P Y UW1 T IH0 D
+DISPUTES  D IH0 S P Y UW1 T S
+DISPUTING  D IH0 S P Y UW1 T IH0 NG
+DISQUALIFICATION  D IH0 S K W AA2 L AH0 F AH0 K EY1 SH AH0 N
+DISQUALIFIED  D IH0 S K W AA1 L AH0 F AY2 D
+DISQUALIFIES  D IH0 S K W AA1 L AH0 F AY2 Z
+DISQUALIFY  D IH0 S K W AA1 L AH0 F AY2
+DISQUALIFYING  D IH0 S K W AA1 L AH0 F AY2 IH0 NG
+DISQUE  D IH1 S K
+DISQUIET  D IH0 S K W AY1 AH0 T
+DISQUIETING  D IH0 S K W AY1 AH0 T IH0 NG
+DISRAELI  D IH0 Z R EY1 L IY0
+DISREGARD  D IH2 S R IH0 G AA1 R D
+DISREGARDED  D IH2 S R IH0 G AA1 R D IH0 D
+DISREGARDING  D IH2 S R IH0 G AA1 R D IH0 NG
+DISREGARDS  D IH2 S R IH0 G AA1 R D Z
+DISREPAIR  D IH2 S R IH0 P EH1 R
+DISREPUTABLE  D IH0 S R EH1 P Y AH0 T AH0 B AH0 L
+DISREPUTE  D IH2 S R IH0 P Y UW1 T
+DISRESPECT  D IH2 S R IH0 S P EH1 K T
+DISRESPECTED  D IH2 S R IH0 S P EH1 K T IH0 D
+DISRESPECTFUL  D IH2 S R IH0 S P EH1 K T F AH0 L
+DISRESPECTING  D IH2 S R IH0 S P EH1 K T IH0 NG
+DISRESPECTS  D IH2 S R IH0 S P EH1 K T S
+DISRUPT  D IH0 S R AH1 P T
+DISRUPTED  D IH0 S R AH1 P T IH0 D
+DISRUPTING  D IH0 S R AH1 P T IH0 NG
+DISRUPTION  D IH0 S R AH1 P SH AH0 N
+DISRUPTIONS  D IH0 S R AH1 P SH AH0 N Z
+DISRUPTIVE  D IH0 S R AH1 P T IH0 V
+DISRUPTS  D IH0 S R AH1 P T S
+DISS  D IH1 S
+DISSATISFACTION  D IH2 S AE0 T IH0 S F AE1 K SH AH0 N
+DISSATISFIED  D IH0 S AE1 T AH0 S F AY2 D
+DISSATISFY  D IH0 S AE1 T AH0 S F AY2
+DISSECT  D AY0 S EH1 K T
+DISSECTED  D AY0 S EH1 K T AH0 D
+DISSECTING  D AY0 S EH1 K T IH0 NG
+DISSECTION  D AY0 S EH1 K SH AH0 N
+DISSECTION(1)  D AY1 S EH0 K SH AH0 N
+DISSECTIONS  D AY0 S EH1 K SH AH0 N Z
+DISSECTIONS(1)  D AY1 S EH0 K SH AH0 N Z
+DISSECTS  D AY0 S EH1 K T S
+DISSECTS(1)  D AY0 S EH1 K S
+DISSEMBLE  D IH0 S EH1 M B AH0 L
+DISSEMBLING  D IH0 S EH1 M B L IH0 NG
+DISSEMINATE  D IH0 S EH1 M AH0 N EY2 T
+DISSEMINATED  D IH0 S EH1 M AH0 N EY2 T AH0 D
+DISSEMINATES  D IH0 S EH1 M AH0 N EY2 T S
+DISSEMINATING  D IH0 S EH1 M AH0 N EY2 T IH0 NG
+DISSEMINATION  D IH0 S EH2 M AH0 N EY1 SH AH0 N
+DISSENSION  D IH0 S EH1 N SH AH0 N
+DISSENT  D IH0 S EH1 N T
+DISSENTED  D IH0 S EH1 N T IH0 D
+DISSENTED(1)  D IH0 S EH1 N IH0 D
+DISSENTER  D IH0 S EH1 N T ER0
+DISSENTERS  D IH0 S EH1 N T ER0 Z
+DISSENTERS(1)  D IH0 S EH1 N ER0 Z
+DISSENTING  D IH0 S EH1 N T IH0 NG
+DISSENTING(1)  D IH0 S EH1 N IH0 NG
+DISSENTS  D IH0 S EH1 N T S
+DISSERTATION  D IH2 S ER0 T EY1 SH AH0 N
+DISSERVICE  D IH0 S ER1 V AH0 S
+DISSIDENCE  D IH1 S AH0 D IH0 N S
+DISSIDENT  D IH1 S AH0 D IH0 N T
+DISSIDENTS  D IH1 S AH0 D AH0 N T S
+DISSIDENTS'  D IH1 S AH0 D AH0 N T S
+DISSIMILAR  D IH0 S IH1 M AH0 L ER0
+DISSIMILARITY  D IH2 S IH2 M AH0 L AE1 R AH0 T IY0
+DISSING  D IH1 S IH0 NG
+DISSINGER  D IH1 S IH0 N JH ER0
+DISSIPATE  D IH1 S AH0 P EY2 T
+DISSIPATED  D IH1 S AH0 P EY2 T IH0 D
+DISSIPATES  D IH1 S AH0 P EY2 T S
+DISSIPATING  D IH1 S AH0 P EY2 T IH0 NG
+DISSIPATION  D IH2 S IH0 P EY1 SH AH0 N
+DISSIPATIVE  D IH1 S AH0 P EY2 T IH0 V
+DISSOCIATE  D IH0 S OW1 S IY0 EY0 T
+DISSOCIATION  D IH0 S OW2 S IY0 EY1 SH AH0 N
+DISSOLUTION  D IH2 S AH0 L UW1 SH AH0 N
+DISSOLVE  D IH0 Z AA1 L V
+DISSOLVED  D IH0 Z AA1 L V D
+DISSOLVER  D IH0 Z AA1 L V ER0
+DISSOLVERS  D IH0 Z AA1 L V ER0 Z
+DISSOLVES  D IH0 Z AA1 L V Z
+DISSOLVING  D IH0 Z AO1 L V IH0 NG
+DISSONANCE  D IH1 S AH0 N AH0 N S
+DISSONANT  D IH1 S AH0 N AH0 N T
+DISSUADE  D IH0 S W EY1 D
+DISSUADED  D IH0 S W EY1 D IH0 D
+DISSYMMETRIC  D IH2 S IH0 M EH1 T R IH0 K
+DISSYMMETRY  D IH0 S IH1 M AH0 T R IY0
+DISTAD  D IH1 S T AH0 D
+DISTAL  D IH1 S T AH0 L
+DISTANCE  D IH1 S T AH0 N S
+DISTANCED  D IH1 S T AH0 N S T
+DISTANCES  D IH1 S T AH0 N S AH0 Z
+DISTANCES(1)  D IH1 S T AH0 N S IH0 Z
+DISTANCING  D IH1 S T AH0 N S IH0 NG
+DISTANT  D IH1 S T AH0 N T
+DISTASI  D IH0 S T AA1 S IY0
+DISTASIO  D IH0 S T AA1 S IY0 OW0
+DISTASTE  D IH0 S T EY1 S T
+DISTASTEFUL  D IH0 S T EY1 S T F AH0 L
+DISTEFANO  D IH0 S T EH0 F AA1 N OW0
+DISTEL  D IH1 S T AH0 L
+DISTEMPER  D IH0 S T EH1 M P ER0
+DISTEND  D IH0 S T EH1 N D
+DISTENDED  D IH0 S T EH1 N D IH0 D
+DISTIL  D IH0 S T IH1 L
+DISTILL  D IH0 S T IH1 L
+DISTILLATE  D IH1 S T AH0 L EY2 T
+DISTILLATE(1)  D IH1 S T AH0 L AH0 T
+DISTILLATES  D IH1 S T AH0 L EY2 T S
+DISTILLATION  D IH2 S T AH0 L EY1 SH AH0 N
+DISTILLED  D IH0 S T IH1 L D
+DISTILLER  D IH0 S T IH1 L ER0
+DISTILLER'S  D IH0 S T IH1 L ER0 Z
+DISTILLERIES  D IH0 S T IH1 L ER0 IY0 Z
+DISTILLERS  D IH0 S T IH1 L ER0 Z
+DISTILLERS'  D IH0 S T IH1 L ER0 Z
+DISTILLERS'S  D IH0 S T IH1 L ER0 Z IH0 Z
+DISTILLERY  D IH0 S T IH1 L ER0 IY0
+DISTILLING  D IH0 S T IH1 L IH0 NG
+DISTILLS  D IH0 S T IH1 L Z
+DISTINCT  D IH0 S T IH1 NG K T
+DISTINCTION  D IH0 S T IH1 NG K SH AH0 N
+DISTINCTIONS  D IH0 S T IH1 NG K SH AH0 N Z
+DISTINCTIVE  D IH0 S T IH1 NG K T IH0 V
+DISTINCTIVELY  D IH0 S T IH1 NG K T IH0 V L IY0
+DISTINCTIVENESS  D IH0 S T IH1 NG K T IH0 V N AH0 S
+DISTINCTLY  D IH0 S T IH1 NG K T L IY0
+DISTINGUISH  D IH0 S T IH1 NG G W IH0 SH
+DISTINGUISHABLE  D IH0 S T IH1 NG G W IH0 SH AH0 B AH0 L
+DISTINGUISHED  D IH0 S T IH1 NG G W IH0 SH T
+DISTINGUISHES  D IH0 S T IH1 NG G W IH0 SH IH0 Z
+DISTINGUISHING  D IH0 S T IH1 NG G W IH0 SH IH0 NG
+DISTLER  D IH1 S T L ER0
+DISTORT  D IH0 S T AO1 R T
+DISTORTED  D IH0 S T AO1 R T AH0 D
+DISTORTED(1)  D IH0 S T AO1 R T IH0 D
+DISTORTING  D IH0 S T AO1 R T IH0 NG
+DISTORTION  D IH0 S T AO1 R SH AH0 N
+DISTORTIONS  D IH0 S T AO1 R SH AH0 N Z
+DISTORTS  D IH0 S T AO1 R T S
+DISTRACT  D IH0 S T R AE1 K T
+DISTRACTED  D IH0 S T R AE1 K T AH0 D
+DISTRACTED(1)  D IH0 S T R AE1 K T IH0 D
+DISTRACTING  D IH0 S T R AE1 K T IH0 NG
+DISTRACTION  D IH0 S T R AE1 K SH AH0 N
+DISTRACTIONS  D IH0 S T R AE1 K SH AH0 N Z
+DISTRACTS  D IH0 S T R AE1 K T S
+DISTRAUGHT  D IH0 S T R AO1 T
+DISTRESS  D IH0 S T R EH1 S
+DISTRESSED  D IH0 S T R EH1 S T
+DISTRESSES  D IH0 S T R EH1 S IH0 Z
+DISTRESSING  D IH0 S T R EH1 S IH0 NG
+DISTRESSINGLY  D IH0 S T R EH1 S IH0 NG L IY0
+DISTRIBUTE  D IH0 S T R IH1 B Y UW0 T
+DISTRIBUTED  D IH0 S T R IH1 B Y AH0 T AH0 D
+DISTRIBUTES  D IH0 S T R IH1 B Y UW0 T S
+DISTRIBUTING  D IH0 S T R IH1 B Y UW0 T IH0 NG
+DISTRIBUTION  D IH2 S T R AH0 B Y UW1 SH AH0 N
+DISTRIBUTIONS  D IH2 S T R AH0 B Y UW1 SH AH0 N Z
+DISTRIBUTIVE  D IH0 S T R IH1 B Y UW0 T IH0 V
+DISTRIBUTOR  D IH0 S T R IH1 B Y AH0 T ER0
+DISTRIBUTOR'S  D IH0 S T R IH1 B Y UW0 T ER0 Z
+DISTRIBUTORS  D IH0 S T R IH1 B Y AH0 T ER0 Z
+DISTRIBUTORS'  D IH0 S T R IH1 B Y AH0 T ER0 Z
+DISTRIBUTORSHIP  D IH0 S T R IH1 B Y UW0 T ER0 SH IH2 P
+DISTRIBUTORSHIPS  D IH0 S T R IH1 B Y UW0 T ER0 SH IH2 P S
+DISTRICT  D IH1 S T R IH0 K T
+DISTRICT'S  D IH1 S T R IH0 K T S
+DISTRICTING  D IH1 S T R IH0 K T IH0 N G
+DISTRICTS  D IH1 S T R IH0 K T S
+DISTRIGAS  D IH0 S T R IY1 G AH0 S
+DISTRON  D IH1 S T R AA2 N
+DISTRUST  D IH0 S T R AH1 S T
+DISTRUSTED  D IH0 S T R AH1 S T AH0 D
+DISTRUSTED(1)  D IH0 S T R AH1 S T IH0 D
+DISTRUSTFUL  D IH0 S T R AH1 S T F AH0 L
+DISTRUSTS  D IH0 S T R AH1 S T S
+DISTURB  D IH0 S T ER1 B
+DISTURBANCE  D IH0 S T ER1 B AH0 N S
+DISTURBANCES  D IH0 S T ER1 B AH0 N S AH0 Z
+DISTURBANCES(1)  D IH0 S T ER1 B AH0 N S IH0 Z
+DISTURBED  D IH0 S T ER1 B D
+DISTURBING  D IH0 S T ER1 B IH0 NG
+DISTURBINGLY  D IH0 S T ER1 B IH0 NG L IY0
+DISTURBS  D IH0 S T ER1 B Z
+DISUNION  D IH0 S Y UW1 N Y AH0 N
+DISUNITY  D IH0 S Y UW1 N AH0 T IY0
+DISUSE  D IH0 S Y UW1 S
+DITCH  D IH1 CH
+DITCHED  D IH1 CH T
+DITCHES  D IH1 CH AH0 Z
+DITCHES(1)  D IH1 CH IH0 Z
+DITCHING  D IH1 CH IH0 NG
+DITH  D IH1 TH
+DITHER  D IH1 DH ER0
+DITHERING  D IH1 DH ER0 IH0 NG
+DITHERS  D IH1 DH ER0 Z
+DITHYRAMB  D IH1 TH ER0 AE2 M
+DITKA  D IH1 T K AH0
+DITKA'S  D IH1 T K AH0 Z
+DITLOW  D IH1 T L OW0
+DITMARS  D IH1 T M ER0 Z
+DITMER  D IH1 T M ER0
+DITMORE  D IH1 T M AO0 R
+DITOMASSO  D IH0 T OW0 M AA1 S OW2
+DITOMMASO  D IH0 T OW0 M AA1 S OW2
+DITSY  D IH1 T S IY0
+DITTBERNER  D IH1 T B ER0 N ER0
+DITTEMORE  D IH0 T EH1 M AO0 R
+DITTER  D IH1 T ER0
+DITTIES  D IH1 T IY0 Z
+DITTMAN  D IH1 T M AH0 N
+DITTMANN  D IH1 T M AH0 N
+DITTMAR  D IH1 T M ER0
+DITTMER  D IH1 T M ER0
+DITTO  D IH1 T OW0
+DITTOHEAD  D IH1 T OW0 HH EH2 D
+DITTON  D IH1 T AH0 N
+DITTRICH  D IH1 T R IH0 K
+DITTUS  D IH1 T AH0 S
+DITTY  D IH1 T IY0
+DITULLIO  D IH0 T AH1 L IY0 OW2
+DITZEL  D IH1 T Z AH0 L
+DITZLER  D IH1 T S L ER0
+DIURETIC  D AY2 UW0 R EH1 T IH0 K
+DIURETICS  D AY2 UW0 R EH1 T IH0 K S
+DIURNAL  D AY0 ER1 N AH0 L
+DIURNALLY  D AY0 ER1 N AH0 L IY0
+DIVA  D IY1 V AH0
+DIVAD  D IH1 V AE0 D
+DIVALENT  D AY0 V EY1 L AH0 N T
+DIVALL  D IH1 V AH0 L
+DIVAN  D IH0 V AE1 N
+DIVAS  D IY1 V AH0 Z
+DIVE  D AY1 V
+DIVED  D AY1 V D
+DIVELBISS  D IH0 V EH1 L B IH0 S
+DIVELEY  D IH1 V IH0 L IY0
+DIVELY  D AY1 V L IY0
+DIVEN  D AY1 V AH0 N
+DIVENS  D AY1 V AH0 N Z
+DIVER  D AY1 V ER0
+DIVER'S  D AY1 V ER0 Z
+DIVERGE  D IH0 V ER1 JH
+DIVERGED  D AY0 V ER1 JH D
+DIVERGENCE  D AY0 V ER1 JH AH0 N S
+DIVERGENCE(1)  D IH0 V ER1 JH AH0 N S
+DIVERGENCES  D AY0 V ER1 JH AH0 N S IH0 Z
+DIVERGENT  D AY0 V ER1 JH AH0 N T
+DIVERGENT(1)  D IH0 V ER1 JH AH0 N T
+DIVERGES  D AY0 V ER1 JH IH0 Z
+DIVERGING  D AY0 V ER1 JH IH0 NG
+DIVERS  D AY1 V ER0 Z
+DIVERSE  D AY0 V ER1 S
+DIVERSE(1)  D IH0 V ER1 S
+DIVERSICARE  D IH1 V ER0 S IH0 K EH2 R
+DIVERSIFICATION  D AY0 V ER2 S AH0 F AH0 K EY1 SH AH0 N
+DIVERSIFICATION(1)  D IH0 V ER2 S AH0 F AH0 K EY1 SH AH0 N
+DIVERSIFICATIONS  D AY0 V ER2 S AH0 F AH0 K EY1 SH AH0 N Z
+DIVERSIFICATIONS(1)  D IH0 V ER2 S AH0 F AH0 K EY1 SH AH0 N Z
+DIVERSIFIED  D AY0 V ER1 S AH0 F AY2 D
+DIVERSIFIED(1)  D IH0 V ER1 S AH0 F AY2 D
+DIVERSIFY  D AY0 V ER1 S AH0 F AY2
+DIVERSIFY(1)  D IH0 V ER1 S AH0 F AY2
+DIVERSIFYING  D AY0 V ER1 S AH0 F AY2 IH0 NG
+DIVERSIFYING(1)  D IH0 V ER1 S AH0 F AY2 IH0 NG
+DIVERSION  D AY0 V ER1 ZH AH0 N
+DIVERSION(1)  D IH0 V ER1 ZH AH0 N
+DIVERSIONARY  D AY0 V ER1 ZH AH0 N EH2 R IY0
+DIVERSIONARY(1)  D IH0 V ER1 ZH AH0 N EH2 R IY0
+DIVERSIONS  D IH0 V ER1 ZH AH0 N Z
+DIVERSIONS(1)  D AY0 V ER1 ZH AH0 N Z
+DIVERSITY  D IH0 V ER1 S IH0 T IY0
+DIVERSITY(1)  D AY0 V ER1 S IH0 T IY0
+DIVERT  D AY0 V ER1 T
+DIVERT(1)  D IH0 V ER1 T
+DIVERTED  D AY0 V ER1 T IH0 D
+DIVERTED(1)  D IH0 V ER1 T IH0 D
+DIVERTICULA  D AY2 V ER0 T IH1 K Y AH0 L AH0
+DIVERTICULUM  D AY2 V ER0 T IH1 K Y AH0 L AH0 M
+DIVERTIMENTO  D IH0 V ER2 T AH0 M EH1 N T OW2
+DIVERTING  D AY0 V ER1 T IH0 NG
+DIVERTING(1)  D IH0 V ER1 T IH0 NG
+DIVERTS  D AY0 V ER1 T S
+DIVERTS(1)  D IH0 V ER1 T S
+DIVES  D AY1 V Z
+DIVEST  D AY0 V EH1 S T
+DIVEST(1)  D IH0 V EH1 S T
+DIVESTED  D AY0 V EH1 S T IH0 D
+DIVESTING  D AY0 V EH1 S T IH0 NG
+DIVESTITURE  D IH0 V EH1 S T IH0 CH ER0
+DIVESTITURE(1)  D AY0 V EH1 S T IH0 CH ER0
+DIVESTITURES  D IH0 V EH1 S T IH0 CH ER0 Z
+DIVESTITURES(1)  D AY0 V EH1 S T IH0 CH ER0 Z
+DIVESTMENT  D AY0 V EH1 S T M AH0 N T
+DIVESTMENT(1)  D AY0 V EH1 S M AH0 N T
+DIVESTMENT(2)  D IH0 V EH1 S M AH0 N T
+DIVESTMENTS  D AY0 V EH1 S T M AH0 N T S
+DIVESTMENTS(1)  D AY0 V EH1 S M AH0 N T S
+DIVESTMENTS(2)  D IH0 V EH1 S M AH0 N T S
+DIVESTS  D AY0 V EH1 S T S
+DIVI  D IY1 V IY0
+DIVIDE  D IH0 V AY1 D
+DIVIDED  D IH0 V AY1 D IH0 D
+DIVIDEND  D IH1 V IH0 D EH2 N D
+DIVIDEND'S  D IH1 V IH0 D EH2 N D Z
+DIVIDENDS  D IH1 V IH0 D EH2 N D Z
+DIVIDER  D IH0 V AY1 D ER0
+DIVIDES  D IH0 V AY1 D Z
+DIVIDING  D IH0 V AY1 D IH0 NG
+DIVINATION  D IH2 V AH0 N EY1 SH AH0 N
+DIVINCENZO  D IH0 V IY0 N CH EH1 N Z OW0
+DIVINE  D IH0 V AY1 N
+DIVINELY  D IH0 V AY1 N L IY0
+DIVINEY  D IH1 V IH0 N IY0
+DIVING  D AY1 V IH0 NG
+DIVINING  D AH0 V AY1 N IH0 NG
+DIVINITAS  D IH2 V IH0 N IY1 T AH0 S
+DIVINITIES  D IH0 V IH1 N AH0 T IY0 Z
+DIVINITY  D IH0 V IH1 N AH0 T IY0
+DIVIRGILIO  D IH0 V IH0 R JH IY1 L IY0 OW2
+DIVIS  D IY1 V IH0 S
+DIVISIBLE  D IH0 V IH1 Z AH0 B AH0 L
+DIVISION  D IH0 V IH1 ZH AH0 N
+DIVISION'S  D IH0 V IH1 ZH AH0 N Z
+DIVISIONAL  D IH0 V IH1 ZH AH0 N AH0 L
+DIVISIONS  D IH0 V IH1 ZH AH0 N Z
+DIVISIONS'  D IH0 V IH1 ZH AH0 N Z
+DIVISIVE  D IH0 V AY1 S IH0 V
+DIVISIVENESS  D IH0 V AY1 S IH0 V N AH0 S
+DIVISON  D IH0 V IH1 ZH AH0 N
+DIVISON(1)  D IH0 V IH1 S AH0 N
+DIVISOR  D IH0 V AY1 Z ER0
+DIVITA  D IH0 V IY1 T AH0
+DIVITO  D IH0 V IY1 T OW0
+DIVORCE  D IH0 V AO1 R S
+DIVORCED  D IH0 V AO1 R S T
+DIVORCEE  D AH0 V AO1 R S IY2
+DIVORCEE(1)  D AH0 V AO1 R S EY2
+DIVORCES  D IH0 V AO1 R S IH0 Z
+DIVORCING  D IH0 V AO1 R S IH0 NG
+DIVULGE  D IH0 V AH1 L JH
+DIVULGE(1)  D AY0 V AH1 L JH
+DIVULGED  D IH0 V AH1 L JH D
+DIVULGED(1)  D AY0 V AH1 L JH D
+DIVULGING  D IH0 V AH1 L JH IH0 NG
+DIVULGING(1)  D AY0 V AH1 L JH IH0 NG
+DIVVIED  D IH1 V IY0 D
+DIVVY  D IH1 V IY0
+DIWA  D IY1 W AH0
+DIWALI  D IH0 W AA1 L IY0
+DIX  D IH1 K S
+DIXIE  D IH1 K S IY0
+DIXIELAND  D IH1 K S IY0 L AE2 N D
+DIXON  D IH1 K S AH0 N
+DIXON'S  D IH1 K S AH0 N Z
+DIXONS  D IH1 K S AH0 N Z
+DIXSON  D IH1 K S AH0 N
+DIXVILLE  D IH1 K S V IH0 L
+DIXY  D IH1 K S IY0
+DIZON  D IH1 Z AH0 N
+DIZZINESS  D IH1 Z IY0 N AH0 S
+DIZZY  D IH1 Z IY0
+DIZZYING  D IH1 Z IY0 IH0 NG
+DJAKARTA  JH AH0 K AA1 R T AH0
+DJAKARTA'S  JH AH0 K AA1 R T AH0 Z
+DJIBOUTI  JH IH0 B UW1 T IY2
+DJUKANOVIC  JH UW0 K AE1 N AH0 V IH0 K
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+DOWNSIZES  D AW1 N S AY2 Z IH0 Z
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+DOWNSIZINGS  D AW1 N S AY2 Z IH0 NG Z
+DOWNSTAGE  D AW1 N S T EY2 JH
+DOWNSTAIRS  D AW1 N S T EH1 R Z
+DOWNSTATE  D AW1 N S T EY2 T
+DOWNSTREAM  D AW1 N S T R IY1 M
+DOWNSWING  D AW1 N S W IH2 NG
+DOWNTIME  D AW1 N T AY2 M
+DOWNTOWN  D AW1 N T AW1 N
+DOWNTOWN'S  D AW1 N T AW1 N Z
+DOWNTOWNS  D AW1 N T AW1 N Z
+DOWNTREND  D AW1 N T R EH2 N D
+DOWNTRODDEN  D AW2 N T R AA1 D AH0 N
+DOWNTURN  D AW1 N T ER2 N
+DOWNTURNS  D AW1 N T ER2 N Z
+DOWNUM  D AW1 N AH0 M
+DOWNWARD  D AW1 N W ER0 D
+DOWNWARDLY  D AW1 N W ER0 D L IY0
+DOWNWARDS  D AW1 N W ER0 D Z
+DOWNWIND  D AW0 N W IH1 N D
+DOWNY  D AW1 N IY0
+DOWRY  D AW1 R IY0
+DOWSE  D AW1 S
+DOWSING  D AW1 S IH0 NG
+DOWSMAN  D AW1 Z M AH0 N
+DOWTY  D AW1 T IY0
+DOXEY  D AA1 K S IY0
+DOXIE  D AA1 K S IY0
+DOXOLOGIES  D AA0 K S AA1 L AH0 JH IY0 Z
+DOXOLOGY  D AA0 K S AA1 L AH0 JH IY0
+DOXTATER  D AA1 K S T EY0 T ER0
+DOXY  D AA1 K S IY0
+DOYAL  D OY0 AA1 L
+DOYEL  D OY1 AH0 L
+DOYEN  D OY1 IH0 N
+DOYENNE  D OY2 EH1 N
+DOYLE  D OY1 L
+DOYLE'S  D OY1 L Z
+DOYON  D OY1 AH0 N
+DOZE  D OW1 Z
+DOZED  D OW1 Z D
+DOZEN  D AH1 Z AH0 N
+DOZENS  D AH1 Z AH0 N Z
+DOZES  D OW1 Z IH0 Z
+DOZIER  D OW1 Z IY0 ER0
+DOZING  D OW1 Z IH0 NG
+DQALPHA  D IY1 K Y UW1 AE1 L F AH0
+DR  D R AY1 V
+DR(1)  D AA1 K T ER0
+DR.  D R AY1 V
+DR.(1)  D AA1 K T ER0
+DRAB  D R AE1 B
+DRABBED  D R AE1 B D
+DRABBLE  D R AE1 B AH0 L
+DRABEK  D R AE1 B IH0 K
+DRABENSTOTT  D R AH0 B EH1 N S T AH0 T
+DRABIK  D R AA1 B IH0 K
+DRABINSKY  D R AH0 B IH1 N S K IY0
+DRABKIN  D R AE1 B K IH2 N
+DRABS  D R AE1 B Z
+DRACE  D R EY1 S
+DRACH  D R AE1 CH
+DRACHENBERG  D R AE1 K AH0 N B ER0 G
+DRACHMA  D R AE1 K M AH0
+DRACHMAS  D R AA1 K M AH0 Z
+DRACKETT  D R AE1 K IH0 T
+DRACO  D R EY1 K OW0
+DRACO(1)  D R AE1 K OW0
+DRACONIAN  D R EY0 K OW1 N IY0 AH0 N
+DRACONIAN(1)  D R AH0 K OW1 N IY0 AH0 N
+DRACULA  D R AE1 K Y UW0 L AH0
+DRAEGER  D R EH1 G ER0
+DRAFFEN  D R AE1 F AH0 N
+DRAFT  D R AE1 F T
+DRAFT'S  D R AE1 F T S
+DRAFTED  D R AE1 F T IH0 D
+DRAFTEE  D R AE1 F T IY1
+DRAFTEES  D R AE1 F T IY1 Z
+DRAFTER  D R AE1 F T ER0
+DRAFTERS  D R AE1 F T ER0 Z
+DRAFTING  D R AE1 F T IH0 NG
+DRAFTS  D R AE1 F T S
+DRAFTSMAN  D R AE1 F T S M AH0 N
+DRAFTSMANSHIP  D R AE1 F T S M AH0 N SH IH2 P
+DRAFTSMEN  D R AE1 F T S M AH0 N
+DRAFTY  D R AE1 F T IY0
+DRAG  D R AE1 G
+DRAGAN  D R AA1 G AH0 N
+DRAGE  D R EY1 JH
+DRAGER  D R EY1 G ER0
+DRAGGED  D R AE1 G D
+DRAGGING  D R AE1 G IH0 NG
+DRAGGY  D R AE1 G IY0
+DRAGLINE  D R AE1 G L AY2 N
+DRAGNET  D R AE1 G N EH2 T
+DRAGNETS  D R AE1 G N EH2 T S
+DRAGO  D R AA1 G OW0
+DRAGON  D R AE1 G AH0 N
+DRAGON'S  D R AE1 G AH0 N Z
+DRAGONAIR  D R AE1 G AH0 N EH2 R
+DRAGONE  D R AH0 G OW1 N
+DRAGONHEAD  D R AE1 G AH0 N HH EH2 D
+DRAGONHEART  D R AE1 G AH0 N HH AA2 R T
+DRAGONS  D R AE1 G AH0 N Z
+DRAGOO  D R AA1 G UW0
+DRAGOVICH  D R AE1 G AH0 V IH0 CH
+DRAGS  D R AE1 G Z
+DRAHEIM  D R AE1 HH AY0 M
+DRAHOS  D R EY1 HH OW0 Z
+DRAHUSCHAK  D R AE1 HH AH0 SH AE0 K
+DRAHUSCHAK(1)  D R AH0 HH UW1 SH AH0 K
+DRAIN  D R EY1 N
+DRAINAGE  D R EY1 N AH0 JH
+DRAINAGE(1)  D R EY1 N IH0 JH
+DRAINE  D R EY1 N
+DRAINED  D R EY1 N D
+DRAINER  D R EY1 N ER0
+DRAINERS  D R EY1 N ER0 Z
+DRAINING  D R EY1 N IH0 NG
+DRAINS  D R EY1 N Z
+DRAKE  D R EY1 K
+DRAKEFORD  D R AE1 K F AO0 R D
+DRAKES  D R EY1 K S
+DRAKOS  D R EY1 K OW0 Z
+DRALLE  D R EY1 L
+DRAM  D R AE1 M
+DRAM(1)  D IY1 R AE2 M
+DRAMA  D R AA1 M AH0
+DRAMAMINE  D R AE1 M AH0 M IY2 N
+DRAMAS  D R AA1 M AH0 Z
+DRAMATIC  D R AH0 M AE1 T IH0 K
+DRAMATICALLY  D R AH0 M AE1 T IH0 K L IY0
+DRAMATICALLY(1)  D R AH0 M AE1 T IH0 K AH0 L IY0
+DRAMATICS  D R AH0 M AE1 T IH0 K S
+DRAMATIST  D R AA1 M AH0 T IH0 S T
+DRAMATIZATION  D R AE2 M AH0 T AH0 Z EY1 SH AH0 N
+DRAMATIZATIONS  D R AE2 M AH0 T AH0 Z EY1 SH AH0 N Z
+DRAMATIZE  D R AA1 M AH0 T AY2 Z
+DRAMATIZE(1)  D R AE1 M AH0 T AY2 Z
+DRAMATIZED  D R AE1 M AH0 T AY2 Z D
+DRAMATIZES  D R AE1 M AH0 T AY2 Z IH0 Z
+DRAMATIZING  D R AE1 M AH0 T AY2 Z IH0 NG
+DRAMATURGY  D R AA1 M AH0 T ER0 JH IY0
+DRAMS  D R AE1 M Z
+DRAMS(1)  D IY1 R AE2 M Z
+DRANE  D R EY1 N
+DRANEY  D R EY1 N IY0
+DRANG  D R AE1 NG
+DRANK  D R AE1 NG K
+DRANSFIELD  D R AE1 N S F IY2 L D
+DRAPE  D R EY1 P
+DRAPEAU  D R AH0 P OW1
+DRAPED  D R EY1 P T
+DRAPER  D R EY1 P ER0
+DRAPERIES  D R EY1 P ER0 IY0 Z
+DRAPERY  D R EY1 P ER0 IY0
+DRAPES  D R EY1 P S
+DRAPING  D R EY1 P IH0 NG
+DRAPKIN  D R AE1 P K IH0 N
+DRASNER  D R AE1 S N ER0
+DRASTIC  D R AE1 S T IH0 K
+DRASTICALLY  D R AE1 S T IH0 K L IY0
+DRAUGHN  D R AO1 N
+DRAUGHON  D R AO1 AH0 N
+DRAUGHT  D R AE1 F T
+DRAUGHTS  D R AE1 F T S
+DRAUS  D R AO1 Z
+DRAVECKY  D R AH0 V EH1 K IY0
+DRAVES  D R EY1 V Z
+DRAVIS  D R AE1 V IH0 S
+DRAVO  D R AE1 V OW0
+DRAVO'S  D R AE1 V OW0 Z
+DRAW  D R AO1
+DRAWBACK  D R AO1 B AE2 K
+DRAWBACKS  D R AO1 B AE2 K S
+DRAWBAUGH  D R AO1 B AO2
+DRAWBRIDGE  D R AO1 B R IH2 JH
+DRAWDOWN  D R AO1 D AW2 N
+DRAWDOWNS  D R AO1 D AW2 N Z
+DRAWDY  D R AO1 D IY0
+DRAWER  D R AO1 R
+DRAWERS  D R AO1 R Z
+DRAWING  D R AO1 IH0 NG
+DRAWINGS  D R AO1 IH0 NG Z
+DRAWL  D R AO1 L
+DRAWLED  D R AO1 L D
+DRAWLS  D R AO1 L Z
+DRAWN  D R AO1 N
+DRAWS  D R AO1 Z
+DRAY  D R EY1
+DRAYER  D R EY1 ER0
+DRAYTON  D R EY1 T AH0 N
+DRAYTON'S  D R EY1 T AH0 N Z
+DREAD  D R EH1 D
+DREADED  D R EH1 D IH0 D
+DREADFUL  D R EH1 D F AH0 L
+DREADFULLY  D R EH1 D F AH0 L IY0
+DREADING  D R EH1 D IH0 NG
+DREADNOUGHT  D R EH1 D N AO2 T
+DREADS  D R EH1 D Z
+DREAM  D R IY1 M
+DREAMED  D R IY1 M D
+DREAMER  D R IY1 M ER0
+DREAMERS  D R IY1 M ER0 Z
+DREAMING  D R IY1 M IH0 NG
+DREAMLAND  D R IY1 M L AE2 N D
+DREAMLIKE  D R IY1 M L AY2 K
+DREAMS  D R IY1 M Z
+DREAMT  D R EH1 M T
+DREAMWORKS  D R IY1 M W ER2 K S
+DREAMWORKS'  D R IY1 M W ER2 K S
+DREAMWORLD  D R IY1 M W ER2 L D
+DREAMY  D R IY1 M IY0
+DREARINESS  D R IY1 R IY0 N AH0 S
+DREARY  D R IH1 R IY0
+DREBSKY  D R EH1 B S K IY0
+DRECHSEL  D R EH1 K S AH0 L
+DRECHSLER  D R EH1 K S AH0 L ER0
+DRECHSLER(1)  D R EH1 K S L ER0
+DRED  D R EH1 D
+DREDD  D R EH1 D
+DREDGE  D R EH1 JH
+DREDGED  D R EH1 JH D
+DREDGES  D R EH1 JH AH0 Z
+DREDGES(1)  D R EH1 JH IH0 Z
+DREDGING  D R EH1 JH IH0 NG
+DREES  D R IY1 Z
+DREESE  D R IY1 Z
+DREESSEN  D R IY1 S AH0 N
+DREGER  D R EH1 G ER0
+DREGS  D R EH1 G Z
+DREHER  D R EH1 R
+DREIBELBIS  D R AY1 B IH0 L B IH0 S
+DREIER  D R AY1 ER0
+DREIGHTON  D R AY1 T IH0 N
+DREILING  D R AY1 L IH0 NG
+DREIS  D R IY1 Z
+DREISBACH  D R AY1 S B AA2 K
+DREMAN  D R IY1 M AH0 N
+DRENCH  D R EH1 N CH
+DRENCHED  D R EH1 N CH T
+DRENCHES  D R EH1 N CH AH0 Z
+DRENCHING  D R EH1 N CH IH0 NG
+DRENNAN  D R EH1 N AH0 N
+DRENNEN  D R EH1 N AH0 N
+DRENNING  D R EH1 N IH0 NG
+DRENNON  D R EH1 N AH0 N
+DRENTH  D R EH1 N TH
+DREPUNG  D R EY1 P AH2 NG
+DREPUNG(1)  D R EY2 P AO1 NG
+DRESBACH  D R EH1 S B AA2 K
+DRESCH  D R EH1 SH
+DRESCHER  D R EH1 SH ER0
+DRESDEN  D R EH1 Z D IH0 N
+DRESDNER  D R EH1 Z D N ER0
+DRESDNER'S  D R EH1 Z D N ER0 Z
+DRESEN  D R IY1 Z AH0 N
+DRESHER  D R EH1 SH ER0
+DRESNER  D R EH1 Z N ER0
+DRESS  D R EH1 S
+DRESSAGE  D R EH0 S AA1 ZH
+DRESSED  D R EH1 S T
+DRESSEL  D R EH1 S AH0 L
+DRESSEN  D R EH1 S AH0 N
+DRESSER  D R EH1 S ER0
+DRESSER'S  D R EH1 S ER0 Z
+DRESSERS  D R EH1 S ER0 Z
+DRESSES  D R EH1 S AH0 Z
+DRESSES(1)  D R EH1 S IH0 Z
+DRESSIER  D R EH1 S IY0 ER0
+DRESSING  D R EH1 S IH0 NG
+DRESSINGS  D R EH1 S IH0 NG Z
+DRESSLER  D R EH1 S L ER0
+DRESSMAKER  D R EH1 S M EY2 K ER0
+DRESSMAKERS  D R EH1 S M EY2 K ER0 Z
+DRESSMAKING  D R EH1 S M EY2 K IH0 NG
+DRESSY  D R EH1 S IY0
+DREW  D R UW1
+DREWERY  D R UW1 ER0 IY0
+DREWES  D R UW1 Z
+DREWETT  D R UW1 IH0 T
+DREWRY  D R UW1 R IY0
+DREWS  D R UW1 Z
+DREXEL  D R EH1 K S AH0 L
+DREXEL'S  D R EH1 K S AH0 L Z
+DREXLER  D R EH1 K S L ER0
+DREY  D R EY1
+DREYER  D R EY1 ER0
+DREYFUS  D R AY1 F AH0 S
+DREYFUS(1)  D R EY1 F AH0 S
+DREYFUS'S  D R EY1 F AH0 S IH0 Z
+DREYFUSS  D R EY1 F AH0 S
+DRIBBED  D R IH1 B D
+DRIBBLE  D R IH1 B AH0 L
+DRIBBLED  D R IH1 B AH0 L D
+DRIBBLES  D R IH1 B AH0 L Z
+DRIBBLING  D R IH1 B AH0 L IH0 NG
+DRIBBLING(1)  D R IH1 B L IH0 NG
+DRIBS  D R IH1 B Z
+DRIED  D R AY1 D
+DRIEHAUS  D R IY1 HH AW2 S
+DRIER  D R AY1 ER0
+DRIES  D R AY1 Z
+DRIESSEN  D R IY1 S AH0 N
+DRIEST  D R AY1 AH0 S T
+DRIEVER  D R IY1 V ER0
+DRIFT  D R IH1 F T
+DRIFTA  D R IH1 F T AH0
+DRIFTED  D R IH1 F T AH0 D
+DRIFTED(1)  D R IH1 F T IH0 D
+DRIFTER  D R IH1 F T ER0
+DRIFTERS  D R IH1 F T ER0 Z
+DRIFTING  D R IH1 F T IH0 NG
+DRIFTNET  D R IH1 F T N EH2 T
+DRIFTS  D R IH1 F T S
+DRIFTWOOD  D R IH1 F T W UH2 D
+DRIGGERS  D R IH1 G ER0 Z
+DRIGGS  D R IH1 G Z
+DRILL  D R IH1 L
+DRILLBIT  D R IH1 L B IH2 T
+DRILLED  D R IH1 L D
+DRILLER  D R IH1 L ER0
+DRILLERS  D R IH1 L ER0 Z
+DRILLING  D R IH1 L IH0 NG
+DRILLING'S  D R IH1 L IH0 NG Z
+DRILLS  D R IH1 L Z
+DRINA  D IY1 N AH0
+DRINA'S  D IY1 N AH0 Z
+DRINAS  D IY1 N AH0 Z
+DRING  D R IH1 NG
+DRINK  D R IH1 NG K
+DRINKABLE  D R IH1 N K AH0 B AH0 L
+DRINKARD  D R IH1 NG K ER0 D
+DRINKER  D R IH1 NG K ER0
+DRINKER'S  D R IH1 NG K ER0 Z
+DRINKERS  D R IH1 NG K ER0 Z
+DRINKING  D R IH1 NG K IH0 NG
+DRINKS  D R IH1 NG K S
+DRINKWATER  D R IH1 NG K W AO2 T ER0
+DRINKWINE  D R IH1 NG K W AY2 N
+DRINNON  D R IH1 N AH0 N
+DRIP  D R IH1 P
+DRIP-DRY  D R IH1 P D R AY1
+DRIPPED  D R IH1 P T
+DRIPPING  D R IH1 P IH0 NG
+DRIPPS  D R IH1 P S
+DRIPS  D R IH1 P S
+DRISCOLL  D R IH1 S K AH0 L
+DRISKELL  D R IH1 S K AH0 L
+DRISKILL  D R IH1 S K IH0 L
+DRIVABLE  D R AY1 V AH0 B AH0 L
+DRIVE  D R AY1 V
+DRIVE'S  D R AY1 V Z
+DRIVE-BY  D R AY1 V B AY1
+DRIVE-THRU  D R AY1 V TH R UW1
+DRIVE-THRUS  D R AY1 V TH R UW1 Z
+DRIVEL  D R IH1 V AH0 L
+DRIVEN  D R IH1 V AH0 N
+DRIVER  D R AY1 V ER0
+DRIVER'S  D R AY1 V ER0 Z
+DRIVERLESS  D R AY1 V ER0 L IH0 S
+DRIVERS  D R AY1 V ER0 Z
+DRIVERS'  D R AY1 V ER0 Z
+DRIVES  D R AY1 V Z
+DRIVEWAY  D R AY1 V W EY2
+DRIVEWAYS  D R AY1 V W EY2 Z
+DRIVING  D R AY1 V IH0 NG
+DRIZZLE  D R IH1 Z AH0 L
+DRIZZLING  D R IH1 Z AH0 L IH0 NG
+DRIZZLING(1)  D R IH1 Z L IH0 NG
+DRIZZLY  D R IH1 Z L IY0
+DROBKOV  D R AO1 B K AO0 V
+DROBKOV(1)  D R AO1 B K AO0 F
+DROBNY  D R AA1 B N IY0
+DRODDY  D R AA1 D IY0
+DROEGE  D R OW1 JH
+DROESSLER  D R OW1 S AH0 L ER0
+DROESSLER(1)  D R OW1 S L ER0
+DROGE  D R OW1 JH
+DROGOUL  D R OW0 G UW1 L
+DROGOUL'S  D R OW0 G UW1 L Z
+DROGUE  D R OW1 G
+DROHAN  D R OW1 AH0 N
+DROID  D R OY1 D
+DROIDS  D R OY1 D Z
+DROKE  D R OW1 K
+DROLET  D R OW1 L IH0 T
+DROLL  D R OW1 L
+DROLLINGER  D R OW1 L IH0 NG ER0
+DROMEDARY  D R AA1 M AH0 D EH2 R IY0
+DROMER  D R OW1 M ER0
+DROMEY  D R OW1 M IY0
+DROMGOOLE  D R AA1 M G UW2 L
+DROMGOOLES  D R AA0 M G UW1 L Z
+DROMI  D R AA1 M IY0
+DROMOMANIA  D R OW2 M OW0 M EY1 N IY0 AH0
+DROMOMANIA(1)  D R OW2 M OW0 M EY1 N Y AH0
+DROMON  D R OW1 M AH0 N
+DRONE  D R OW1 N
+DRONED  D R OW1 N D
+DRONES  D R OW1 N Z
+DRONET  D R OW1 N IH0 T
+DRONEY  D R OW1 N IY0
+DRONING  D R OW1 N IH0 NG
+DROOL  D R UW1 L
+DROOLED  D R UW1 L D
+DROOLING  D R UW1 L IH0 NG
+DROOP  D R UW1 P
+DROOPED  D R UW1 P T
+DROOPING  D R UW1 P IH0 NG
+DROOPY  D R UW1 P IY0
+DROP  D R AA1 P
+DROP(1)  D R AO1 P
+DROP-OFF  D R AA1 P AO2 F
+DROP-OFFS  D R AA1 P AO2 F S
+DROPKIN  D R AA1 P K IH0 N
+DROPLET  D R AA1 P L AH0 T
+DROPLETS  D R AA1 P L AH0 T S
+DROPOFF  D R AA1 P AO2 F
+DROPOFFS  D R AA1 P AO2 F S
+DROPOUT  D R AA1 P AW2 T
+DROPOUTS  D R AA1 P AW2 T S
+DROPPED  D R AA1 P T
+DROPPER  D R AA1 P ER0
+DROPPERS  D R AA1 P ER0 Z
+DROPPING  D R AA1 P IH0 NG
+DROPPINGS  D R AA1 P IH0 NG Z
+DROPS  D R AA1 P S
+DROPSY  D R AA1 P S IY0
+DROSER  D R OW1 Z ER0
+DROSS  D R AO1 S
+DROST  D R AA1 S T
+DROSTE  D R OW1 S T
+DROSTE(1)  D R AA1 S T
+DROSTE(2)  D R AA1 S T AH0
+DROTAR  D R OW1 T ER0
+DROUGHT  D R AW1 T
+DROUGHT'S  D R AW1 T S
+DROUGHTS  D R AW1 T S
+DROUILLARD  D R W IY0 L AA1 R D
+DROUIN  D R W IY1 N
+DROVE  D R OW1 V
+DROVER  D R OW1 V ER0
+DROVES  D R OW1 V Z
+DROWN  D R AW1 N
+DROWNED  D R AW1 N D
+DROWNING  D R AW1 N IH0 NG
+DROWNINGS  D R AW1 N IH0 NG Z
+DROWNS  D R AW1 N Z
+DROWSINESS  D R AW1 Z IY0 N AH0 S
+DROWSY  D R AW1 Z IY0
+DROZ  D R AA1 Z
+DROZD  D R AA1 Z D
+DROZDA  D R AA1 Z D AH0
+DROZDOWSKI  D R AH0 Z D AO1 F S K IY0
+DRU  D R UW1
+DRUB  D R AH1 B
+DRUBBED  D R AH1 B D
+DRUBBING  D R AH1 B IH0 NG
+DRUCE  D R UW1 S
+DRUCIE  D R AH1 K IY0
+DRUCK  D R AH1 K
+DRUCKENMILLER  D R AH1 K IH0 N M IH2 L ER0
+DRUCKER  D R AH1 K ER0
+DRUCKMAN  D R AH1 K M AH0 N
+DRUDGE  D R AH1 JH
+DRUDGERY  D R AH1 JH ER0 IY0
+DRUELLA  D R UW2 EH1 L AH0
+DRUG  D R AH1 G
+DRUG'S  D R AH1 G Z
+DRUGAN  D R UW1 G AH0 N
+DRUGGED  D R AH1 G D
+DRUGGING  D R AH1 G IH0 NG
+DRUGGIST  D R AH1 G IH0 S T
+DRUGGIST'S  D R AH1 G AH0 S T S
+DRUGGIST'S(1)  D R AH1 G IH0 S T S
+DRUGGISTS  D R AH1 G AH0 S T S
+DRUGGISTS(1)  D R AH1 G IH0 S T S
+DRUGMAKER  D R AH1 G M EY2 K ER0
+DRUGMAKERS  D R AH1 G M EY2 K ER0 Z
+DRUGS  D R AH1 G Z
+DRUGS'  D R AH1 G Z
+DRUGSTORE  D R AH1 G S T AO2 R
+DRUGSTORES  D R AH1 G S T AO2 R Z
+DRUID  D R UW1 IH0 D
+DRUIDISM  D R UW1 AH0 D IH2 Z AH0 M
+DRUIDS  D R UW1 IH0 D Z
+DRUISILLA  D R IH0 S IH1 L AH0
+DRUM  D R AH1 M
+DRUMBEAT  D R AH1 M B IY2 T
+DRUMHEAD  D R AH1 M HH EH2 D
+DRUMHELLER  D R AH1 M HH EH2 L ER0
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+HAMBRECHT  HH AE1 M B R EH2 K T
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+HAMBURGER  HH AE1 M B ER0 G ER0
+HAMBURGERS  HH AE1 M B ER0 G ER0 Z
+HAMBY  HH AE1 M B IY0
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+HAMDOON  HH AE0 M D UW1 N
+HAMDUN  HH AE1 M D AH0 N
+HAMED  HH AE1 M D
+HAMEISTER  HH AE1 M AY0 S T ER0
+HAMEL  HH AE1 M AH0 L
+HAMELIN  HH AE1 M AH0 L IH0 N
+HAMELIN(1)  HH AE1 M L IH2 N
+HAMER  HH AE1 M ER0
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+HAMIEL  HH AE1 M IY0 L
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+HAMILTON  HH AE1 M AH0 L T AH0 N
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+HAMILTONIAN  HH AE1 M AH0 L T OW2 N Y AH0 N
+HAMILTONS  HH AE1 M AH0 L T AH0 N Z
+HAMISH  HH AE1 M IH0 SH
+HAMITER  HH AE1 M AY0 T ER0
+HAMITIC  HH AE0 M IH1 T IH0 K
+HAMLER  HH AE1 M AH0 L ER0
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+HAMLET  HH AE1 M L AH0 T
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+HAMLETT  HH AE1 M L IH0 T
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+HAMMAKER  HH AE1 M EY2 K ER0
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+HAMMERMAN  HH AE1 M ER0 M AH0 N
+HAMMERMEISTER  HH AE1 M ER0 M AY2 S T ER0
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+HAMMERSLEY  HH AE1 M ER0 S L IY0
+HAMMERSMITH  HH AE1 M ER0 S M IH2 TH
+HAMMERSON  HH AE1 M ER0 S AH0 N
+HAMMERSTEIN  HH AE1 M ER0 S T IY2 N
+HAMMERSTEIN(1)  HH AE1 M ER0 S T AY2 N
+HAMMERSTEIN'S  HH AE1 M ER0 S T IY2 N Z
+HAMMERSTEIN'S(1)  HH AE1 M ER0 S T AY2 N Z
+HAMMERSTROM  HH AE1 M ER0 S T R AH0 M
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+HAMMETT  HH AE1 M IH0 T
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+HAMNER  HH AE1 M N ER0
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+HAMPSHIRE  HH AE1 M P SH ER0
+HAMPSHIRE(1)  HH AE1 M SH ER0
+HAMPSHIRE(2)  HH AE1 M P SH AY0 ER0
+HAMPSHIRE(3)  HH AE1 M SH AY0 ER0
+HAMPSHIRE'S  HH AE1 M P SH ER0 Z
+HAMPSHIRE'S(1)  HH AE1 M SH ER0 Z
+HAMPSHIRE'S(2)  HH AE1 M P SH AY0 ER0 Z
+HAMPSHIRE'S(3)  HH AE1 M SH AY0 ER0 Z
+HAMPSHIRITES  HH AE1 M P SH ER0 AY2 T S
+HAMPSON  HH AE1 M P S AH0 N
+HAMPSTEAD  HH AE1 M P S T EH2 D
+HAMPTON  HH AE1 M P T AH0 N
+HAMPTON'S  HH AE1 M P T AH0 N Z
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+HAMPTONSHIRE  HH AE1 M P T AH0 N SH ER0
+HAMPTONSHIRE'S  HH AE1 M P T AH0 N SH ER0 Z
+HAMRE  HH AE1 M ER0
+HAMRIC  HH AE1 M R IH0 K
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+HAMROCK  HH AE1 M R AA2 K
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+HAMSPHIRE  HH AE1 M S F AY2 R
+HAMSTER  HH AE1 M S T ER0
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+HAMSTRA  HH AE1 M S T R AH0
+HAMSTRING  HH AE1 M S T R IH2 NG
+HAMSTRINGS  HH AE1 M S T R IH2 NG Z
+HAMSTRUNG  HH AE1 M S T R AH0 NG
+HAMTRAMCK  HH AE0 M T R AE1 M IH0 K
+HAN  HH AA1 N
+HAN(1)  HH AE1 N
+HAN'S  HH AA1 N Z
+HAN'S(1)  HH AE1 N Z
+HANA  HH AE1 N AH0
+HANAFIN  HH AE1 N AH0 F IH0 N
+HANAGAN  HH AA0 N AA1 G AA0 N
+HANAHAN  HH AE1 N AH0 HH AE0 N
+HANAK  HH AA1 N AH0 K
+HANAN  HH EY1 N AH0 N
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+HANAUER  HH AE1 N AW0 ER0
+HANAWALT  HH AE1 N AH0 W AO2 L T
+HANAWAY  HH AE1 N AH0 W EY0
+HANBACK  HH AE1 N B AE2 K
+HANBERRY  HH AE1 N B EH2 R IY0
+HANBO  HH AE1 N B OW0
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+HANCHETT  HH AE1 N CH IH0 T
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+HANCOCK  HH AE1 N K AA2 K
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+HANCOX  HH AE1 N K AA0 K S
+HAND  HH AE1 N D
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+HAND-HELD  HH AE1 N D HH EH2 L D
+HANDA  HH AE1 N D AH0
+HANDBAG  HH AE1 N D B AE2 G
+HANDBAGS  HH AE1 N D B AE2 G Z
+HANDBALL  HH AE1 N D B AO2 L
+HANDBALLS  HH AE1 N D B AO2 L Z
+HANDBILL  HH AE1 N D B IH2 L
+HANDBILLS  HH AE1 N D B IH2 L Z
+HANDBOOK  HH AE1 N D B UH2 K
+HANDBOOKS  HH AE1 N D B UH2 K S
+HANDCLASP  HH AE1 N D K L AE2 S P
+HANDCRAFT  HH AE1 N D K R AE2 F T
+HANDCRAFTED  HH AE1 N D K R AE2 F T IH0 D
+HANDCRAFTS  HH AE1 N D K R AE2 F T S
+HANDCUFF  HH AE1 N D K AH2 F
+HANDCUFFED  HH AE1 N D K AH2 F T
+HANDCUFFING  HH AE1 N D K AH2 F IH0 NG
+HANDCUFFS  HH AE1 N D K AH2 F S
+HANDED  HH AE1 N D AH0 D
+HANDED(1)  HH AE1 N D IH0 D
+HANDEDLY  HH AE1 N D IH0 D L IY0
+HANDEDNESS  HH AE1 N D AH0 D N AH0 S
+HANDEL  HH AE1 N D AH0 L
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+HANDELAND  HH AE1 N D IH0 L AH0 N D
+HANDELMAN  HH AE1 N D AH0 L M AH0 N
+HANDELS  HH AE1 N D AH0 L Z
+HANDELSBANK  HH AE1 N D AH0 L Z B AE2 NG K
+HANDELSBANKEN  HH AE2 N D AH0 L S B AE1 NG K AH0 N
+HANDELSMAN  HH AE1 N D IH0 L S M AH0 N
+HANDER  HH AE1 N D ER0
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+HANDFORD  HH AE1 N D F ER0 D
+HANDFUL  HH AE1 N D F UH2 L
+HANDFULS  HH AE1 N D F UH2 L Z
+HANDGUN  HH AE1 N D G AH2 N
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+HANDHELD  HH AE1 N D HH EH1 L D
+HANDHOLD  HH AE1 N D HH OW2 L D
+HANDHOLDING  HH AE1 N D HH OW2 L D IH0 NG
+HANDICAP  HH AE1 N D IY0 K AE2 P
+HANDICAPPED  HH AE1 N D IY0 K AE2 P T
+HANDICAPPER  HH AE1 N D IY0 K AE2 P ER0
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+HANDICAPPING  HH AE1 N D IY0 K AE2 P IH0 NG
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+HANDICRAFT  HH AE1 N D IY0 K R AE2 F T
+HANDICRAFTS  HH AE1 N D IY0 K R AE2 F T S
+HANDIER  HH AE1 N D IY0 ER0
+HANDIEST  HH AE1 N D IY0 AH0 S T
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+HANDING  HH AE1 N D IH0 NG
+HANDIWORK  HH AE1 N D IY0 W ER2 K
+HANDKE  HH AE1 N D K IY0
+HANDKERCHIEF  HH AE1 NG K ER0 CH IH0 F
+HANDKERCHIEF(1)  HH AE1 NG K ER0 CH IY0 F
+HANDKERCHIEFS  HH AE1 NG K ER0 CH AH0 F S
+HANDKERCHIEFS(1)  HH AE1 NG K ER0 CH IY0 F S
+HANDLE  HH AE1 N D AH0 L
+HANDLEBAR  HH AE1 N D AH0 L B AA2 R
+HANDLEBARS  HH AE1 N D AH0 L B AA2 R Z
+HANDLED  HH AE1 N D AH0 L D
+HANDLER  HH AE1 N D L ER0
+HANDLER(1)  HH AE1 N D AH0 L ER0
+HANDLERS  HH AE1 N D L ER0 Z
+HANDLERS(1)  HH AE1 N D AH0 L ER0 Z
+HANDLES  HH AE1 N D AH0 L Z
+HANDLEY  HH AE1 N D L IY0
+HANDLIN  HH AE1 N D L IH0 N
+HANDLING  HH AE1 N D L IH0 NG
+HANDLING(1)  HH AE1 N D AH0 L IH0 NG
+HANDLON  HH AE1 N D L AH0 N
+HANDLOOM  HH AE1 N D L UW2 M
+HANDLOOMS  HH AE1 N D L UW2 M Z
+HANDLY  HH AE1 N D L IY0
+HANDMADE  HH AE1 N D M EY1 D
+HANDMADE(1)  HH AE1 N M EY1 D
+HANDOUT  HH AE1 N D AW2 T
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+HANDOVER  HH AE1 N D OW0 V ER0
+HANDPICK  HH AE1 N D P IH1 K
+HANDPICKED  HH AE1 N D P IH1 K T
+HANDRAIL  HH AE1 N D R EY2 L
+HANDRAILS  HH AE1 N D R EY2 L Z
+HANDRICH  HH AE1 N D R IH0 K
+HANDROS  HH AE1 N D R OW0 S
+HANDS  HH AE1 N D Z
+HANDS(1)  HH AE1 N Z
+HANDS-ON  HH AE2 N Z AO1 N
+HANDSAW  HH AE1 N D S AO2
+HANDSAWS  HH AE1 N D S AO2 Z
+HANDSET  HH AE1 N D S EH2 T
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+HANDSHAKE  HH AE1 N D SH EY2 K
+HANDSHAKES  HH AE1 N D SH EY2 K S
+HANDSHAKING  HH AE1 N D SH EY2 K IH0 NG
+HANDSOME  HH AE1 N S AH0 M
+HANDSOMELY  HH AE1 N S AH0 M L IY0
+HANDSTAND  HH AE1 N D S T AE2 N D
+HANDSTANDS  HH AE1 N D S T AE2 N D Z
+HANDWERK  HH AE1 N D W ER0 K
+HANDWERKER  HH AE1 N D W ER0 K ER0
+HANDWOVEN  HH AE1 N D W OW1 V AH0 N
+HANDWOVEN(1)  HH AE1 N W OW1 V AH0 N
+HANDWRITING  HH AE1 N D R AY2 T IH0 NG
+HANDWRITTEN  HH AE1 N D R IH2 T AH0 N
+HANDY  HH AE1 N D IY0
+HANDYMAN  HH AE1 N D IY0 M AE2 N
+HANDYMEN  HH AE1 N D IY0 M EH1 N
+HANE  HH EY1 N
+HANEDA  HH AH0 N EY1 D AH0
+HANEL  HH AE1 N AH0 L
+HANELINE  HH AE1 N IH0 L AY2 N
+HANEMANN  HH EY1 N M AH0 N
+HANER  HH EY1 N ER0
+HANES  HH EY1 N Z
+HANEY  HH EY1 N IY0
+HANF  HH AE1 N F
+HANFORD  HH AE1 N F ER0 D
+HANFT  HH AE1 N F T
+HANG  HH AE1 NG
+HANGAR  HH AE1 NG ER0
+HANGARS  HH AE1 NG ER0 Z
+HANGARTNER  HH AE1 NG G AA0 R T N ER0
+HANGED  HH AE1 NG D
+HANGEN  HH AE1 NG AH0 N
+HANGER  HH AE1 NG ER0
+HANGERS  HH AE1 NG ER0 Z
+HANGIN'  HH AE1 NG G IH0 N
+HANGING  HH AE1 NG IH0 NG
+HANGING(1)  HH AE1 NG G IH0 NG
+HANGINGS  HH AE1 NG G IH0 NG Z
+HANGMAN  HH AE1 NG M AH0 N
+HANGOUT  HH AE1 NG AW2 T
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+HANGOVER  HH AE1 NG OW2 V ER0
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+HANGUP  HH AE1 NG G AH2 P
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+HANI  HH AE1 N IY0
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+HANIFEN  HH AE1 N IH0 F AH0 N
+HANIFIN  HH AE1 N IH0 F IH0 N
+HANIGAN  HH AE1 N IH0 G AH0 N
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+HANISCH  HH AE1 N IH0 SH
+HANISEE  HH AE1 N IH0 S IY0
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+HANKIN  HH AE1 NG K IH0 N
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+HANOVERIAN  HH AE2 N OW0 V IH1 R IY0 AH0 N
+HANRAHAN  HH AE1 N R AH0 HH AE0 N
+HANRATTY  HH AE1 N R AH0 T IY0
+HANS  HH AA1 N S
+HANS(1)  HH AE1 N Z
+HANSA  HH AE1 N S AH0
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+HANSHAW  HH AE1 N SH AO2
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+HANSMA  HH AE1 N S M AH0
+HANSMAN  HH AE1 N S M AH0 N
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+HANSOM  HH AE1 N S AH0 M
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+HANSSON  HH AE1 N S AH0 N
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+HANTA(1)  HH AA1 N T AH0
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+HANZEL  HH AE1 N Z AH0 L
+HANZLIK  HH AE1 N Z L IH0 K
+HAO  HH AW1
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+HAP  HH AE1 P
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+HAPEMAN  HH EY1 P M AH0 N
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+HAPHAZARDLY  HH AE1 F AH0 Z ER0 D L IY0
+HAPHAZARDLY(1)  HH AE0 P HH AE1 Z ER0 D L IY0
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+HAPOALIM(1)  HH AH0 P OW2 AH0 L IY1 M
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+HAPPENING  HH AE1 P AH0 N IH0 NG
+HAPPENING(1)  HH AE1 P N IH0 NG
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+HAPPENINGS(1)  HH AE1 P N IH0 NG Z
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+HAPPIER  HH AE1 P IY0 ER0
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+HARADIM  HH AH0 R AE1 D IH2 M
+HARADIM(1)  HH AH0 R AE2 D IY1 M
+HARAHAN  HH AE1 R AH0 HH AE2 N
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+HARANGUING  HH ER0 AE1 NG IH0 NG
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+HARASS  HH ER0 AE1 S
+HARASSED  HH ER0 AE1 S T
+HARASSER  HH ER0 AE1 S ER0
+HARASSERS  HH ER0 AE1 S ER0 Z
+HARASSES  HH ER0 AE1 S IH0 Z
+HARASSING  HH ER0 AE1 S IH0 NG
+HARASSMENT  HH ER0 AE1 S M AH0 N T
+HARASZTI  HH ER0 AE1 S T IY0
+HARAWAY  HH AA1 R AH0 W EY0
+HARB  HH AA1 R B
+HARBACH  HH AA1 R B AA2 K
+HARBAUGH  HH AA1 R B AO2
+HARBECK  HH AA1 R B EH2 K
+HARBER  HH AA1 R B ER0
+HARBERT  HH AA1 R B ER0 T
+HARBERTS  HH AA1 R B ER0 T S
+HARBESON  HH AA1 R B IH0 S AH0 N
+HARBIN  HH AA1 R B IH0 N
+HARBINGER  HH AA1 R B IH0 N JH ER0
+HARBINGERS  HH AA1 R B IH0 NG ER0 Z
+HARBINSON  HH AA1 R B IH0 N S AH0 N
+HARBISON  HH AA1 R B IH0 S AH0 N
+HARBOLD  HH AA1 R B OW2 L D
+HARBOR  HH AA1 R B ER0
+HARBOR'S  HH AA1 R B ER0 Z
+HARBORED  HH AA1 R B ER0 D
+HARBORING  HH AA1 R B ER0 IH0 NG
+HARBORS  HH AA1 R B ER0 Z
+HARBORSIDE  HH AA1 R B ER0 S AY2 D
+HARBORY  HH AA1 R B AH0 R IY0
+HARBOUR  HH AA1 R B ER0
+HARBUCK  HH AA1 R B AH0 K
+HARBURY  HH AA1 R B ER0 IY0
+HARC  HH AA1 R K
+HARCLERODE  HH AA1 R K AH0 L ER0 OW0 D
+HARCLERODE(1)  HH AA1 R K L ER0 OW0 D
+HARCOURT  HH AA1 R K AO2 R T
+HARCOURT'S  HH AA1 R K ER0 T S
+HARCROW  HH AA1 R K R OW0
+HARCUM  HH AA1 R K AH0 M
+HARD  HH AA1 R D
+HARD-ASS  HH AA2 R D AE2 S
+HARD-WORKING  HH AA2 R D W ER1 K IH0 NG
+HARDACRE  HH AA1 R D EY2 K ER0
+HARDAGE  HH AA1 R D IH0 JH
+HARDART  HH AA1 R D AA2 R T
+HARDAWAY  HH AA1 R D AH0 W EY2
+HARDBACK  HH AA1 R D B AE2 K
+HARDBALL  HH AA1 R D B AO2 L
+HARDBOARD  HH AA1 R D B AO2 R D
+HARDCASTLE  HH AA1 R D K AE2 S AH0 L
+HARDCASTLE'S  HH AA1 R D K AE2 S AH0 L Z
+HARDCORE  HH AA1 R D K AO1 R
+HARDCOVER  HH AA1 R D K AH2 V ER0
+HARDCOVERS  HH AA1 R D K AH2 V ER0 Z
+HARDEBECK  HH AA1 R D B EH0 K
+HARDEE  HH AA1 R D IY1
+HARDEE'S  HH AA1 R D IY1 Z
+HARDEGREE  HH AA0 R D IH0 G R IY1
+HARDEMAN  HH AA1 R D M AH0 N
+HARDEN  HH AA1 R D AH0 N
+HARDEN'S  HH AA1 R D AH0 N Z
+HARDENBROOK  HH AA1 R D AH0 N B R UH2 K
+HARDENED  HH AA1 R D AH0 N D
+HARDENER  HH AA1 R D AH0 N ER0
+HARDENERS  HH AA1 R D AH0 N ER0 Z
+HARDENING  HH AA1 R D AH0 N IH0 NG
+HARDENING(1)  HH AA1 R D N IH0 NG
+HARDENS  HH AA1 R D AH0 N Z
+HARDER  HH AA1 R D ER0
+HARDERS  HH AA1 R D ER0 Z
+HARDEST  HH AA1 R D AH0 S T
+HARDESTY  HH AA1 R D AH0 S T IY0
+HARDEY  HH AA1 R D IY0
+HARDGOOD  HH AA1 R D G UH2 D
+HARDGOODS  HH AA1 R D G UH2 D Z
+HARDGRAVE  HH AA1 R D G R EY2 V
+HARDGROVE  HH AA1 R D G R OW2 V
+HARDHEAD  HH AA1 R D HH EH2 D
+HARDHEADED  HH AA1 R D HH EH2 D IH0 D
+HARDICK  HH AA1 R D IH0 K
+HARDIE  HH AA1 R D IY0
+HARDIER  HH AA1 R D IY0 ER0
+HARDIEST  HH AA1 R D IY0 AH0 S T
+HARDIGREE  HH AA0 R D IH0 G R IY1
+HARDIMAN  HH AA1 R D IH0 M AH0 N
+HARDIMON  HH AA1 R D IH0 M AA0 N
+HARDIN  HH AA1 R D IH0 N
+HARDING  HH AA1 R D IH0 NG
+HARDING'S  HH AA1 R D IH0 NG S
+HARDINGER  HH AA1 R D IH0 NG ER0
+HARDISON  HH AA1 R D IH0 S AH0 N
+HARDISTER  HH AA1 R D IH0 S T ER0
+HARDISTY  HH AA1 R D IH0 S T IY0
+HARDLINE  HH AA1 R D L AY2 N
+HARDLINER  HH AA1 R D L AY2 N ER0
+HARDLINERS  HH AA1 R D L AY2 N ER0 Z
+HARDLY  HH AA1 R D L IY0
+HARDMAN  HH AA1 R D M AH0 N
+HARDNESS  HH AA1 R D N AH0 S
+HARDNETT  HH AA1 R D N IH0 T
+HARDPRESSED  HH AA1 R D P R EH2 S T
+HARDRICK  HH AA1 R D R IH0 K
+HARDS  HH AA1 R D Z
+HARDSCRABBLE  HH AA1 R D S K R AE2 B AH0 L
+HARDSHIP  HH AA1 R D SH IH0 P
+HARDSHIPS  HH AA1 R D SH IH0 P S
+HARDT  HH AA1 R T
+HARDTKE  HH AA1 R D K IY0
+HARDWARE  HH AA1 R D W EH2 R
+HARDWAY  HH AA1 R D W EY2
+HARDWICK  HH AA1 R D W IH2 K
+HARDWICKE  HH AA1 R D W IH0 K
+HARDWIN  HH AA1 R D W IH0 N
+HARDWIRED  HH AA2 R D W AY1 ER0 D
+HARDWOOD  HH AA1 R D W UH2 D
+HARDWOODS  HH AA1 R D W UH2 D Z
+HARDWORK  HH AA1 R D W ER2 K
+HARDWORKING  HH AA1 R D W ER2 K IH0 NG
+HARDY  HH AA1 R D IY0
+HARDY'S  HH AA1 R D IY0 Z
+HARDYMON  HH AA1 R D IY0 M AA2 N
+HARE  HH EH1 R
+HARE'S  HH EH1 R Z
+HAREBRAINED  HH EH1 R B R EY2 N D
+HARELSON  HH AE1 R IH0 L S AH0 N
+HAREM  HH EH1 R AH0 M
+HAREN  HH EH1 R AH0 N
+HARER  HH EH1 R ER0
+HARES  HH EH1 R Z
+HAREWOOD  HH EH1 R W UH2 D
+HARFF  HH AA1 R F
+HARFORD  HH AA1 R F ER0 D
+HARGADON  HH AA0 R G AA0 D AO1 N
+HARGAN  HH AA1 R G AH0 N
+HARGARTEN  HH AA1 R G AA0 R T AH0 N
+HARGENS  HH AA1 R G AH0 N Z
+HARGER  HH AA1 R G ER0
+HARGETT  HH AA1 R JH IH0 T
+HARGIS  HH AA1 R G IH0 S
+HARGRAVE  HH AA1 R G R EY2 V
+HARGRAVES  HH AA1 R G R EY2 V Z
+HARGREAVES  HH AA1 R G R IY2 V Z
+HARGROVE  HH AA1 R G R OW2 V
+HARGUS  HH AA1 R G AH0 S
+HARI  HH AA1 R IY0
+HARIG  HH AE1 R IH0 G
+HARIMA  HH EH0 R IY1 M AH0
+HARING  HH EH1 R IH0 NG
+HARIRI  HH ER0 IH1 R IY0
+HARIS  HH AA1 R IY0 S
+HARIS(1)  HH EH1 R IH0 S
+HARITOS  HH EH0 R IY1 T OW0 S
+HARIZ  HH EH1 R IH0 Z
+HARJO  HH AA1 R JH OW0
+HARJU  HH AA1 R Y UW0
+HARK  HH AA1 R K
+HARKAVY  HH AA1 R K AH0 V IY0
+HARKE  HH AA1 R K
+HARKEN  HH AA1 R K AH0 N
+HARKENS  HH AA1 R K AH0 N Z
+HARKER  HH AA1 R K ER0
+HARKEY  HH AA1 R K IY0
+HARKIN  HH AA1 R K IH0 N
+HARKIN'S  HH AA1 R K IH0 N Z
+HARKING  HH AA1 R K IH0 NG
+HARKINS  HH AA1 R K IH0 N Z
+HARKLEROAD  HH AA1 R K L ER0 OW0 D
+HARKLESS  HH AA1 R K L AH0 S
+HARKNESS  HH AA1 R K N AH0 S
+HARKRADER  HH AA1 R K R AH0 D ER0
+HARKRIDER  HH AA1 R K R AY2 D ER0
+HARKS  HH AA1 R K S
+HARL  HH AA1 R L
+HARLACHER  HH AA1 R L AH0 K ER0
+HARLAN  HH AA1 R L AH0 N
+HARLAND  HH AA1 R L AH0 N D
+HARLE  HH AA1 R AH0 L
+HARLEM  HH AA1 R L AH0 M
+HARLEM'S  HH AA1 R L AH0 M Z
+HARLEMAN  HH AA1 R AH0 L M AH0 N
+HARLEQUIN  HH AA1 R L AH0 K W AH0 N
+HARLESS  HH AA1 R L IH0 S
+HARLEY  HH AA1 R L IY0
+HARLEY'S  HH AA1 R L IY0 Z
+HARLEYSVILLE  HH AA1 R L IY0 Z V IH2 L
+HARLIN  HH AA1 R L IH0 N
+HARLIN'S  HH AA1 R L IH0 N Z
+HARLING  HH AA1 R L IH0 NG
+HARLINGEN  HH AA1 R L IH0 NG AH0 N
+HARLISON  HH AA1 R L IH0 S AH0 N
+HARLOFF  HH AA1 R L AO0 F
+HARLOT  HH AA1 R L AH0 T
+HARLOW  HH AA1 R L OW0
+HARM  HH AA1 R M
+HARM'S  HH AA1 R M Z
+HARMAN  HH AA1 R M AH0 N
+HARMATA  HH AA0 R M AA1 T AH0
+HARMATTAN  HH AA2 R M AH0 T AE1 N
+HARMED  HH AA1 R M D
+HARMEL  HH AA1 R M AH0 L
+HARMEN  HH AA1 R M AH0 N
+HARMENING  HH AA1 R M AH0 N IH0 NG
+HARMER  HH AA1 R M ER0
+HARMES  HH AA1 R M Z
+HARMETZ  HH AA1 R M EH2 T S
+HARMEYER  HH AA1 R M AY2 ER0
+HARMFUL  HH AA1 R M F AH0 L
+HARMFULLY  HH AA1 R M F AH0 L IY0
+HARMFULNESS  HH AA1 R M F AH0 L N AH0 S
+HARMING  HH AA1 R M IH0 NG
+HARMISON  HH AA1 R M IH0 S AH0 N
+HARMLESS  HH AA1 R M L AH0 S
+HARMLESSLY  HH AA1 R M L AH0 S L IY0
+HARMON  HH AA1 R M AH0 N
+HARMON'S  HH AA1 R M AH0 N Z
+HARMONIA  HH AA0 R M OW1 N IY0 AH0
+HARMONIC  HH AA0 R M AA1 N IH0 K
+HARMONICA  HH AA0 R M AA1 N IH0 K AH0
+HARMONICS  HH AA0 R M AA1 N IH0 K S
+HARMONIE  HH AA1 R M AH0 N IY0
+HARMONIES  HH AA1 R M AH0 N IY0 Z
+HARMONIOUS  HH AA0 R M OW1 N IY0 AH0 S
+HARMONIOUSLY  HH AA0 R M OW1 N IY0 AH0 S L IY0
+HARMONIUM  HH AA0 R M OW1 N IY0 AH0 M
+HARMONIZATION  HH AA2 R M AH0 N IH0 Z EY1 SH AH0 N
+HARMONIZE  HH AA1 R M AH0 N AY2 Z
+HARMONIZED  HH AA1 R M AH0 N AY2 Z D
+HARMONIZES  HH AA1 R M AH0 N AY2 Z IH0 Z
+HARMONIZING  HH AA1 R M AH0 N AY2 Z IH0 NG
+HARMONY  HH AA1 R M AH0 N IY0
+HARMS  HH AA1 R M Z
+HARMSEN  HH AA1 R M S AH0 N
+HARN  HH AA1 R N
+HARNACK  HH AA1 R N AH0 K
+HARNAGE  HH AA1 R N IH0 JH
+HARNDEN  HH AA1 R N D AH0 N
+HARNE  HH AA1 R N
+HARNED  HH AA1 R N D
+HARNER  HH AA1 R N ER0
+HARNESS  HH AA1 R N AH0 S
+HARNESS(1)  HH AA1 R N IH0 S
+HARNESSED  HH AA1 R N AH0 S T
+HARNESSES  HH AA1 R N AH0 S AH0 Z
+HARNESSES(1)  HH AA1 R N AH0 S IH0 Z
+HARNESSING  HH AA1 R N AH0 S IH0 NG
+HARNETT  HH AA1 R N IH0 T
+HARNEY  HH AA1 R N IY0
+HARNISCH  HH AA1 R N IH0 SH
+HARNISCHFEGER  HH AA1 R N IH0 SH F EH2 G ER0
+HARNISCHFEGER'S  HH AA1 R N IH0 SH F EH2 G ER0 Z
+HARNISH  HH AA1 R N IH0 SH
+HARNOIS  HH AA0 R N W AA1
+HARO  HH AA1 R OW0
+HAROLD  HH EH1 R AH0 L D
+HAROLD'S  HH EH1 R AH0 L D Z
+HAROLDSON  HH AE1 R OW0 L D S AH0 N
+HARP  HH AA1 R P
+HARPE  HH AA1 R P
+HARPED  HH AA1 R P T
+HARPEL  HH AA0 R P EH1 L
+HARPENAU  HH AA1 R P IH0 N OW0
+HARPER  HH AA1 R P ER0
+HARPER'S  HH AA1 R P ER0 Z
+HARPERCOLLINS  HH AA1 R P ER0 K AO1 L IH0 N Z
+HARPERS  HH AA1 R P ER0 Z
+HARPHAM  HH AA1 R F AH0 M
+HARPIN  HH AA0 R P AE1 N
+HARPING  HH AA1 R P IH0 NG
+HARPIST  HH AA1 R P IH0 S T
+HARPISTS  HH AA1 R P IH0 S T S
+HARPLEY  HH AA1 R P L IY0
+HARPO  HH AA1 R P OW0
+HARPOLD  HH AA1 R P OW2 L D
+HARPOLE  HH AA1 R P OW2 L
+HARPOON  HH AA0 R P UW1 N
+HARPOONS  HH AA0 R P UW1 N Z
+HARPOOTLIAN  HH AA0 R P UW1 T L IY0 AH0 N
+HARPS  HH AA1 R P S
+HARPSICHORD  HH AA1 R P S AH0 K AO2 R D
+HARPST  HH AA1 R P S T
+HARPSTER  HH AA1 R P S T ER0
+HARQUEBUS  HH AA1 R K W AH0 B AH0 S
+HARR  HH AE1 R
+HARRA  HH AA1 R AH0
+HARRAH  HH AE1 R AH0
+HARRAH'S  HH EH1 R AH0 Z
+HARRAL  HH AE1 R AH0 L
+HARRALSON  HH AE1 R AH0 L S AH0 N
+HARRE  HH AE1 R
+HARREL  HH AE1 R AH0 L
+HARRELD  HH AE1 R IH0 L D
+HARRELL  HH EH1 R AH0 L
+HARRELL'S  HH AE1 R AH0 L Z
+HARRELSON  HH EH1 R IH0 L S AH0 N
+HARREN  HH AE1 R AH0 N
+HARRER  HH AA1 R ER0
+HARRIED  HH EH1 R IY0 D
+HARRIER  HH EH1 R IY0 ER0
+HARRIES  HH EH1 R IY0 Z
+HARRIET  HH EH1 R IY0 AH0 T
+HARRIETTE  HH AE1 R IY0 EH0 T
+HARRIGAN  HH EH1 R IH0 G AH0 N
+HARRIGER  HH AE1 R IH0 G ER0
+HARRILL  HH AE1 R AH0 L
+HARRIMAN  HH EH1 R IH0 M AH0 N
+HARRING  HH AE1 R IH0 NG
+HARRINGTON  HH EH1 R IH0 NG T AH0 N
+HARRIOTT  HH AE1 R IY0 AH0 T
+HARRIS  HH EH1 R IH0 S
+HARRIS'  HH EH1 R IH0 S
+HARRIS'S  HH EH1 R IH0 S IH0 Z
+HARRISBURG  HH AE1 R IH0 S B ER0 G
+HARRISBURG(1)  HH EH1 R IH0 S B ER0 G
+HARRISBURG'S  HH AE1 R IH0 S B ER0 G Z
+HARRISBURG'S(1)  HH EH1 R IH0 S B ER0 G Z
+HARRISBURGH  HH AE1 R IH0 S B ER0 G
+HARRISBURGH(1)  HH EH1 R IH0 S B ER0 G
+HARRISBURGH'S  HH AE1 R IH0 S B ER0 G Z
+HARRISBURGH'S(1)  HH EH1 R IH0 S B ER0 G Z
+HARRISES  HH AE1 R IH0 S IH0 Z
+HARRISON  HH EH1 R IH0 S AH0 N
+HARRISON'S  HH EH1 R IH0 S AH0 N Z
+HARRISS  HH AE1 R IH0 S
+HARRITY  HH AE1 R IH0 T IY0
+HARROD  HH EH1 R AH0 D
+HARROD'S  HH EH1 R AH0 D Z
+HARRODS  HH EH1 R AH0 D Z
+HARROLD  HH EH1 R AH0 L D
+HARRON  HH AE1 R AH0 N
+HARROP  HH EH1 R AH0 P
+HARROUN  HH ER0 UW1 N
+HARROW  HH AE1 R OW0
+HARROWER  HH AE1 R OW0 W ER0
+HARROWING  HH EH1 R OW0 IH0 NG
+HARRY  HH EH1 R IY0
+HARRY'S  HH EH1 R IY0 Z
+HARRYMAN  HH AE1 R IY0 M AH0 N
+HARSCH  HH AA1 R SH
+HARSCO  HH AA1 R S K OW0
+HARSH  HH AA1 R SH
+HARSHA  HH AA1 R SH AH0
+HARSHAM  HH AA1 R SH AH0 M
+HARSHAW  HH AA1 R SH AO2
+HARSHBARGER  HH AA1 R SH B AA2 R G ER0
+HARSHBERGER  HH AA1 R SH B ER0 G ER0
+HARSHER  HH AA1 R SH ER0
+HARSHEST  HH AA1 R SH AH0 S T
+HARSHFIELD  HH AA1 R SH F IY2 L D
+HARSHLY  HH AA1 R SH L IY0
+HARSHMAN  HH AA1 R SH M AH0 N
+HARSHNESS  HH AA1 R SH N AH0 S
+HARSTAD  HH AA1 R S T AH0 D
+HARSTON  HH AA1 R S T AH0 N
+HART  HH AA1 R T
+HART'S  HH AA1 R T S
+HARTE  HH AA1 R T
+HARTEL  HH AA1 R T AH0 L
+HARTELL  HH AA1 R T AH0 L
+HARTEN  HH AA1 R T AH0 N
+HARTENSTEIN  HH AA1 R T AH0 N S T AY2 N
+HARTENSTEIN(1)  HH AA1 R T AH0 N S T IY2 N
+HARTER  HH AA1 R T ER0
+HARTFIEL  HH AA1 R T F IY2 L
+HARTFIELD  HH AA1 R T F IY2 L D
+HARTFORD  HH AA1 R T F ER0 D
+HARTFORD'S  HH AA1 R T F ER0 D Z
+HARTGRAVES  HH AA1 R T G R EY2 V Z
+HARTGROVE  HH AA1 R T G R OW2 V
+HARTH  HH AA1 R TH
+HARTIG  HH AA1 R T IH0 G
+HARTIGAN  HH AA1 R T IH0 G AH0 N
+HARTIN  HH AA1 R T IH0 N
+HARTING  HH AA1 R T IH0 NG
+HARTINGER  HH AA1 R T IH0 NG ER0
+HARTIS  HH AA1 R T IH0 S
+HARTJE  HH AA1 R T JH EY0
+HARTKE  HH AA1 R T K IY0
+HARTKOPF  HH AA1 R T K AO0 F
+HARTL  HH AA1 R T AH0 L
+HARTLAGE  HH AA1 R T L IH0 JH
+HARTLAND  HH AA1 R T L AH0 N D
+HARTLAUB  HH AA1 R T L AW2 B
+HARTLE  HH AA1 R T AH0 L
+HARTLESS  HH AA1 R T L AH0 S
+HARTLEY  HH AA1 R T L IY0
+HARTLIEB  HH AA1 R T L IY2 B
+HARTLINE  HH AA1 R T L AY2 N
+HARTLING  HH AA1 R T L IH0 NG
+HARTMAN  HH AA1 R T M AH0 N
+HARTMANN  HH AA1 R T M AH0 N
+HARTMARX  HH AA1 R T M AA2 R K S
+HARTNELL  HH AA1 R T N AH0 L
+HARTNER  HH AA1 R T N ER0
+HARTNESS  HH AA1 R T N AH0 S
+HARTNETT  HH AA1 R T N IH0 T
+HARTNEY  HH AA1 R T N IY0
+HARTOG  HH AA1 R T AH0 G
+HARTON  HH AA1 R T AH0 N
+HARTONG  HH AA1 R T AO0 NG
+HARTRANFT  HH AA1 R T R AH2 N F T
+HARTS  HH AA1 R T S
+HARTSELL  HH AA1 R T S AH0 L
+HARTSFIELD  HH AA1 R T S F IY2 L D
+HARTSHORN  HH AA1 R T S HH AO2 R N
+HARTSHORNE  HH AA1 R CH ER0 N
+HARTSOCK  HH AA1 R T S AH0 K
+HARTSOE  HH AA1 R T S OW0
+HARTSON  HH AA1 R T S AH0 N
+HARTSOOK  HH AA1 R T S UH0 K
+HARTSOUGH  HH AA1 R T S AW0
+HARTSTEIN  HH AA1 R T S T AY2 N
+HARTSTEIN(1)  HH AA1 R T S T IY2 N
+HARTSVILLE  HH AA1 R T S V IH0 L
+HARTT  HH AA1 R T
+HARTTER  HH AA1 R T ER0
+HARTUNG  HH AA1 R T AH0 NG
+HARTWELL  HH AA1 R T W EH2 L
+HARTWICK  HH AA1 R T W IH2 K
+HARTWIG  HH AA1 R T W IH0 K
+HARTWOOD  HH AA1 R T W UH2 D
+HARTY  HH AA1 R T IY0
+HARTZ  HH AA1 R T S
+HARTZEL  HH AA1 R T Z AH0 L
+HARTZELL  HH AA1 R T Z AH0 L
+HARTZLER  HH AA1 R T S L ER0
+HARTZOG  HH AA1 R T Z AH0 G
+HARUO  HH AA0 R UW1 OW0
+HARV  HH AA1 R V
+HARVARD  HH AA1 R V ER0 D
+HARVARD'S  HH AA1 R V ER0 D Z
+HARVATH  HH AA1 R V AH0 TH
+HARVE  HH AA1 R V
+HARVEL  HH AA1 R V AH0 L
+HARVELL  HH AA1 R V AH0 L
+HARVEST  HH AA1 R V AH0 S T
+HARVESTABLE  HH AA1 R V AH0 S T AH0 B AH0 L
+HARVESTED  HH AA1 R V AH0 S T IH0 D
+HARVESTER  HH AA1 R V AH0 S T ER0
+HARVESTERS  HH AA1 R V AH0 S T ER0 Z
+HARVESTING  HH AA1 R V AH0 S T IH0 NG
+HARVESTS  HH AA1 R V AH0 S T S
+HARVEY  HH AA1 R V IY0
+HARVEY'S  HH AA1 R V IY0 Z
+HARVIE  HH AA1 R V IY0
+HARVILL  HH AA1 R V IH0 L
+HARVILLE  HH AA1 R V IH2 L
+HARVIN  HH AA1 R V IH0 N
+HARVISON  HH AA1 R V IH0 S AH0 N
+HARWARD  HH AA1 R W ER0 D
+HARWELL  HH AA1 R W EH2 L
+HARWICK  HH AA1 R W IH0 K
+HARWOOD  HH AA1 R W UH2 D
+HARYANA  HH EH0 R Y AA1 N AH0
+HAS  HH AE1 Z
+HAS(1)  HH AH0 Z
+HAS-BEEN  HH AE1 Z B IH2 N
+HAS-BEENS  HH AE1 Z B IH2 N Z
+HASAN  HH AA2 S AA1 N
+HASBRO  HH AE1 Z B R OW0
+HASBRO'S  HH AE1 Z B R OW2 Z
+HASBROOK  HH AE1 S B R UH0 K
+HASBROUCK  HH AE1 Z B R UH2 K
+HASCALL  HH AE1 S K AH0 L
+HASCH  HH AE1 SH
+HASCHKE  HH AE1 SH K
+HASE  HH EY1 Z
+HASEGAWA  HH AA2 S EY0 G AA1 W AH0
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+HELD  HH EH1 L D
+HELDENBRAND  HH EH1 L D IH0 N B R AH0 N D
+HELDENBRAND(1)  HH EH1 L D IH0 N B R AE0 N D
+HELDENTENOR  HH EH1 L D EH0 N T EH2 N ER0
+HELDER  HH EH1 L D ER0
+HELDERMAN  HH EH1 L D ER0 M AH0 N
+HELDMAN  HH EH1 L D M AH0 N
+HELDOR  HH EH1 L D ER0
+HELDRETH  HH EH1 L D R IH0 TH
+HELDRING  HH EH1 L D R IH0 NG
+HELDS  HH EH1 L D Z
+HELDT  HH EH1 L T
+HELEN  HH EH1 L AH0 N
+HELEN'S  HH EH1 L IH0 N Z
+HELENA  HH EH1 L AH0 N AH0
+HELENA'S  HH EH1 L IH0 N AH0 Z
+HELENE  HH AH0 L IY1 N
+HELENS  HH EH1 L AH0 N Z
+HELF  HH EH1 L F
+HELFAND  HH EH1 L F AH0 N D
+HELFER  HH EH1 L F ER0
+HELFGOTT  HH EH1 L F G AA2 T
+HELFMAN  HH EH1 L F M AH0 N
+HELFRICH  HH EH1 L F R IH0 K
+HELGA  HH EH1 L G AH0
+HELGERSON  HH EH1 L G ER0 S AH0 N
+HELGESEN  HH EH1 L G IY0 Z AH0 N
+HELGESON  HH EH1 L G IH0 S AH0 N
+HELGET  HH EH1 L G IH0 T
+HELGREN  HH EH1 L G R EH0 N
+HELICAL  HH EH1 L IH0 K AH0 L
+HELICE  HH EH1 L IH0 S
+HELICON  HH EH1 L IH0 K AA2 N
+HELICONS  HH EH1 L IH0 K AA2 N Z
+HELICOPTER  HH EH1 L IH0 K AA2 P T ER0
+HELICOPTER'S  HH EH1 L AH0 K AA2 P T ER0 Z
+HELICOPTERS  HH EH1 L IH0 K AA2 P T ER0 Z
+HELIE  HH EH1 L IY0
+HELIN  HH EH1 L IH0 N
+HELING  HH IY1 L IH0 NG
+HELINSKI  HH IH0 L IH1 N S K IY0
+HELIONETIC  HH IY2 L IY0 OW0 N EH1 T IH0 K
+HELIONETICS  HH IY2 L IY0 OW0 N EH1 T IH0 K S
+HELIOPOLIS  HH IY2 L IY0 AA1 P AH0 L AH0 S
+HELIOS  HH IY1 L IY0 AA2 S
+HELIOTROPE  HH IY1 L IY0 AH0 T R OW2 P
+HELIUM  HH IY1 L IY0 AH0 M
+HELIX  HH IY1 L IH0 K S
+HELKE  HH EH1 L K
+HELL  HH EH1 L
+HELL'S  HH EH1 L Z
+HELLACIOUS  HH EH2 L EY1 SH AH0 S
+HELLACIOUSLY  HH EH2 L EY1 SH AH0 S L IY0
+HELLACIOUSNESS  HH EH2 L EY1 SH AH0 S N AH0 S
+HELLAMS  HH EH1 L AH0 M Z
+HELLAND  HH EH1 L AH0 N D
+HELLARD  HH EH1 L ER0 D
+HELLBERG  HH EH1 L B ER0 G
+HELLBUSCH  HH EH1 L B AH0 SH
+HELLBUSCH(1)  HH EH1 L B UH0 SH
+HELLE  HH EH1 L
+HELLEN  HH EH1 L AH0 N
+HELLENBRAND  HH EH1 L AH0 N B R AE2 N D
+HELLENIC  HH AH0 L EH1 N IH0 K
+HELLENISM  HH EH1 L AH0 N IH2 Z AH0 M
+HELLENISTIC  HH EH2 L AH0 N IH1 S T IH0 K
+HELLENIZE  HH EH1 L AH0 N AY2 Z
+HELLENIZED  HH EH1 L AH0 N AY2 Z D
+HELLENIZES  HH EH1 L AH0 N AY2 Z IH0 Z
+HELLENIZING  HH EH1 L AH0 N AY2 Z IH0 NG
+HELLER  HH EH1 L ER0
+HELLER'S  HH EH1 L ER0 Z
+HELLERMAN  HH EH1 L ER0 M AH0 N
+HELLFIRE  HH EH1 L F AY2 R
+HELLICKSON  HH EH1 L IH0 K S AH0 N
+HELLIER  HH EH1 L Y ER0
+HELLING  HH EH1 L IH0 NG
+HELLINGER  HH EH1 L IH0 NG ER0
+HELLISH  HH EH1 L IH0 SH
+HELLMAN  HH EH1 L M AH0 N
+HELLMANN  HH EH1 L M AH0 N
+HELLMER  HH EH1 L M ER0
+HELLMUTH  HH EH1 L M UW2 TH
+HELLNER  HH EH1 L N ER0
+HELLO  HH AH0 L OW1
+HELLO(1)  HH EH0 L OW1
+HELLRAISER  HH EH1 L R EY2 Z ER0
+HELLSTROM  HH EH1 L S T R AH0 M
+HELLUMS  HH EH1 L AH0 M Z
+HELLUVA  HH EH2 L UW1 V AH0
+HELLWIG  HH EH1 L W IH0 G
+HELLYER  HH EH1 L IY0 ER0
+HELM  HH EH1 L M
+HELMA  HH EH1 L M AH0
+HELMAN  HH EH1 L M AH0 N
+HELMBRECHT  HH EH1 L M B R IH0 K T
+HELME  HH EH1 L M
+HELMER  HH EH1 L M ER0
+HELMERICH  HH EH1 L M ER0 IH0 K
+HELMERS  HH EH1 L M ER0 Z
+HELMES  HH EH1 L M Z
+HELMET  HH EH1 L M AH0 T
+HELMETED  HH EH1 L M AH0 T IH0 D
+HELMETS  HH EH1 L M AH0 T S
+HELMICH  HH EH1 L M IH0 K
+HELMICK  HH EH1 L M IH0 K
+HELMIG  HH EH1 L M IH0 G
+HELMING  HH EH1 L M IH0 NG
+HELMINIAK  HH EH1 L M IH0 N IY0 AE0 K
+HELMINSKI  HH IH0 L M IH1 N S K IY0
+HELMINTH  HH EH1 L M IH0 N TH
+HELMKAMP  HH EH1 L M K AE2 M P
+HELMKE  HH EH1 L M K
+HELMONT  HH EH1 L M AA2 N T
+HELMS  HH EH1 L M Z
+HELMS'  HH EH1 L M Z
+HELMS'S  HH EH1 L M Z IH0 Z
+HELMSBURTON  HH EH1 L M Z B ER0 T AH0 N
+HELMSLEY  HH EH1 L M Z L IY0
+HELMSLEY'S  HH EH1 L M Z L IY0 Z
+HELMSLEYS  HH EH1 L M Z L IY0 Z
+HELMSMAN  HH EH1 L M Z M AE2 N
+HELMSTETTER  HH EH1 L M S T IH0 T ER0
+HELMUT  HH EH1 L M AH0 T
+HELMUTH  HH EH1 L M UW2 TH
+HELOT  HH EH1 L AH0 T
+HELOTISM  HH EH1 L AH0 T IH2 Z AH0 M
+HELOTRY  HH EH1 L AH0 T R IY0
+HELOTS  HH EH1 L AH0 T S
+HELP  HH EH1 L P
+HELPED  HH EH1 L P T
+HELPER  HH EH1 L P ER0
+HELPERS  HH EH1 L P ER0 Z
+HELPFUL  HH EH1 L P F AH0 L
+HELPFULLY  HH EH1 L P F AH0 L IY0
+HELPING  HH EH1 L P IH0 NG
+HELPINGS  HH EH1 L P IH0 NG Z
+HELPLESS  HH EH1 L P L AH0 S
+HELPLESSLY  HH EH1 L P L AH0 S L IY0
+HELPLESSNESS  HH EH1 L P L AH0 S N AH0 S
+HELPRIN  HH EH1 L P R IH0 N
+HELPS  HH EH1 L P S
+HELSEL  HH EH1 L S AH0 L
+HELSER  HH EH1 L S ER0
+HELSETH  HH EH1 L S IH0 TH
+HELSINKI  HH EH1 L S IH0 NG K IY0
+HELSLEY  HH EH1 L S L IY0
+HELSTROM  HH EH1 L S T R AH0 M
+HELT  HH EH1 L T
+HELTER  HH EH1 L T ER0
+HELTER-SKELTER  HH EH2 L T ER0 S K EH1 L T ER0
+HELTON  HH EH1 L T AH0 N
+HELTSLEY  HH EH1 L T S L IY0
+HELTZEL  HH EH1 L T Z AH0 L
+HELVETICA  HH EH2 L V EH1 T IH0 K AH0
+HELVEY  HH EH1 L V IY0
+HELVIE  HH EH1 L V IY0
+HELWIG  HH EH1 L W IH0 G
+HELZER  HH EH1 L Z ER0
+HEM  HH EH1 M
+HEMAN  HH IY1 M AH0 N
+HEMANI  HH AH0 M AA1 N IY0
+HEMANN  HH EH1 M AH0 N
+HEMANT  HH EH1 M AH0 N T
+HEMATITE  HH EH1 M AH0 T AY2 T
+HEMATOLOGY  HH EH2 M AH0 T AA1 L AH0 JH IY0
+HEMATOLOGY(1)  HH IY2 M AH0 T AA1 L AH0 JH IY0
+HEMBERGER  HH EH1 M B ER0 G ER0
+HEMBREE  HH IH0 M B R IY1
+HEMBRICK  HH EH1 M B R IH2 K
+HEMBY  HH EH1 M B IY0
+HEMDALE  HH EH1 M D EY2 L
+HEMDALE'S  HH EH1 M D EY2 L Z
+HEMEL  HH EH1 M AH0 L
+HEMENWAY  HH EH1 M AH0 N W EY2
+HEMIMORPHITE  HH EH2 M AH0 M AO1 R F AY2 T
+HEMING  HH EH1 M IH0 NG
+HEMINGER  HH EH1 M IH0 NG ER0
+HEMINGWAY  HH EH1 M IH0 NG W EY2
+HEMINGWAY'S  HH EH1 M IH0 NG W EY2 Z
+HEMIPLEGIA  HH EH2 M AH0 P L IY1 JH IY0 AH0
+HEMIPLEGIA(1)  HH EH2 M AH0 P L IY1 JH Y AH0
+HEMISPHERE  HH EH1 M IH0 S F IH2 R
+HEMISPHERIC  HH EH2 M AH0 S F IH1 R IH0 K
+HEMKER  HH EH1 M K ER0
+HEMLER  HH EH1 M L ER0
+HEMLINE  HH EH1 M L AY2 N
+HEMLINES  HH EH1 M L AY2 N Z
+HEMLO  HH EH1 M L OW0
+HEMLOCK  HH EH1 M L AA2 K
+HEMM  HH EH1 M
+HEMME  HH EH1 M
+HEMMED  HH EH1 M D
+HEMMELGARN  HH EH1 M IH0 L G AA0 R N
+HEMMEN  HH EH1 M AH0 N
+HEMMER  HH EH1 M ER0
+HEMMERICH  HH EH1 M ER0 IH0 K
+HEMMERLE  HH EH1 M ER0 L IY0
+HEMMERLING  HH EH1 M ER0 L IH0 NG
+HEMMERT  HH EH1 M ER0 T
+HEMMETER  HH EH1 M IH0 T ER0
+HEMMING  HH EH1 M IH0 NG
+HEMMINGER  HH EH1 M IH0 NG ER0
+HEMMINGHAUS  HH EH1 M IH0 NG HH AW2 S
+HEMMINGS  HH EH1 M IH0 NG Z
+HEMMINGSEN  HH EH1 M IH0 NG S AH0 N
+HEMMINGSON  HH EH1 M IH0 NG S AH0 N
+HEMO  HH IY1 M OW0
+HEMOCYANIN  HH IY2 M AH0 S AY1 AH0 N AH0 N
+HEMODYNAMIC  HH EH2 M OW0 D AY0 N AE1 M IH0 K
+HEMODYNAMICS  HH EH2 M OW0 D AY0 N AE1 M IH0 K S
+HEMOGLOBIN  HH IY2 M AH0 G L OW1 B AH0 N
+HEMOLYTIC  HH IY2 M AH0 L IH1 T IH0 K
+HEMOND  HH EH1 M AH0 N D
+HEMOPHILIA  HH IY2 M AH0 F IY1 L IY0 AH0
+HEMOPHILIAC  HH IY0 M AH0 F IH1 L IY0 AE0 K
+HEMOPHILIAC(1)  HH IY0 M OW0 F IH1 L IY0 AE0 K
+HEMOPHILIAC(2)  HH IY0 M AH0 F IH1 L Y AE0 K
+HEMOPHILIAC(3)  HH IY0 M OW0 F IH1 L Y AE0 K
+HEMOPHILIACS  HH IY2 M AH0 F IH1 L IY0 AE2 K S
+HEMORRHAGE  HH EH1 M ER0 IH0 JH
+HEMORRHAGE(1)  HH EH1 M R AH0 JH
+HEMORRHAGED  HH EH1 M ER0 IH0 JH D
+HEMORRHAGIC  HH EH2 M ER0 AE1 G IH0 K
+HEMORRHAGING  HH EH1 M ER0 IH0 JH IH0 NG
+HEMORRHOID  HH EH1 M ER0 OY2 D
+HEMORRHOIDS  HH EH1 M ER0 OY2 D Z
+HEMOTEC  HH EH1 M OW0 T EH2 K
+HEMP  HH EH1 M P
+HEMPEL  HH EH1 M P AH0 L
+HEMPEN  HH EH1 M P AH0 N
+HEMPFLING  HH EH1 M P F AH0 L IH0 NG
+HEMPFLING(1)  HH EH1 M P F L IH0 NG
+HEMPHILL  HH EH1 M P HH IH2 L
+HEMPSTEAD  HH EH1 M P S T EH0 D
+HEMRIC  HH EH1 M R IH0 K
+HEMRICK  HH EH1 M R IH0 K
+HEMRY  HH EH1 M R IY0
+HEMS  HH EH1 M Z
+HEMSLEY  HH EH1 M Z L IY0
+HEMSTREET  HH EH1 M S T R IY2 T
+HEMY  HH EH1 M IY0
+HEN  HH EH1 N
+HEN'S  HH EH1 N Z
+HENAO  HH EY1 N AW0
+HENARD  HH EH1 N ER0 D
+HENAULT  HH EH1 N AW0 L T
+HENBANE  HH EH1 N B EY2 N
+HENCE  HH EH1 N S
+HENCEFORTH  HH EH1 N S F AO1 R TH
+HENCH  HH EH1 N CH
+HENCHMAN  HH EH1 N CH M AH0 N
+HENCHMEN  HH EH1 N CH M AH0 N
+HENCKEL  HH EH1 N K AH0 L
+HENDEE  HH EH1 N D IY0
+HENDEL  HH EH1 N D AH0 L
+HENDERSHOT  HH EH1 N D ER0 SH AH0 T
+HENDERSHOTT  HH EH1 N D ER0 SH AH0 T
+HENDERSON  HH EH1 N D ER0 S AH0 N
+HENDLER  HH EH1 N D L ER0
+HENDLEY  HH EH1 N D L IY0
+HENDON  HH EH1 N D OW0 N
+HENDRA  HH EH1 N D R AH0
+HENDREN  HH EH1 N D ER0 AH0 N
+HENDRICH  HH EH1 N D R IH0 K
+HENDRICK  HH EH1 N D R IH0 K
+HENDRICKS  HH EH1 N D R IH0 K S
+HENDRICKSEN  HH EH1 N D R IH0 K S AH0 N
+HENDRICKSON  HH EH1 N D R IH0 K S AH0 N
+HENDRIE  HH EH1 N D ER0 IY0
+HENDRIK  HH EH1 N D R IH0 K
+HENDRIKS  HH EH1 N D R IH0 K S
+HENDRIKSEN  HH EH1 N D R IH0 K S AH0 N
+HENDRIX  HH EH1 N D R IH0 K S
+HENDRIXSON  HH EH1 N D R IH0 K S AH0 N
+HENDRON  HH EH1 N D R AH0 N
+HENDRY  HH EH1 N D R IY0
+HENDRY'S  HH EH1 N D R IY0 Z
+HENDRYX  HH EH1 N D R IH0 K S
+HENDY  HH EH1 N D IY0
+HENEGAR  HH EH1 N IH0 G ER0
+HENEGHAN  HH IH0 N EH1 G AH0 N
+HENEHAN  HH EH1 N IH0 HH AE0 N
+HENERY  HH EH1 N ER0 IY0
+HENES  HH IY1 N Z
+HENEY  HH EH1 N IY0
+HENG  HH EH1 NG
+HENGEL  HH EH1 NG G AH0 L
+HENGST  HH EH1 NG G S T
+HENHOUSE  HH EH1 N HH AW2 S
+HENIE  HH EH1 N IY0
+HENIGAN  HH EH1 N IH0 G AH0 N
+HENIN  HH EH1 N IH0 N
+HENINGER  HH EH1 N IH0 NG ER0
+HENION  HH EH1 N Y AH0 N
+HENK  HH EH1 NG K
+HENKE  HH EH1 NG K
+HENKEL  HH EH1 NG K AH0 L
+HENKELMAN  HH EH1 NG K AH0 L M AH0 N
+HENKELS  HH EH1 NG K AH0 L Z
+HENKEN  HH EH1 NG K AH0 N
+HENKES  HH EH1 NG K S
+HENKIN  HH EH1 NG K IH0 N
+HENKLE  HH EH1 NG K AH0 L
+HENLE  HH EH1 N AH0 L
+HENLEY  HH EH1 N L IY0
+HENLEY'S  HH EH1 N L IY0 Z
+HENLEYS  HH EH1 N L IY0 Z
+HENLINE  HH EH1 N L AY2 N
+HENLY  HH EH1 N L IY0
+HENMAN  HH EH1 N M AH0 N
+HENN  HH EH1 N
+HENNA  HH EH1 N AH0
+HENNAN  HH EH1 N AH0 N
+HENNE  HH EH1 N
+HENNE(1)  HH EH1 N IY0
+HENNEBERGER  HH EH1 N B ER0 G ER0
+HENNEBERRY  HH EH1 N B EH0 R IY0
+HENNEKE  HH EH1 N IH0 K
+HENNELLY  HH EH1 N AH0 L IY0
+HENNEMAN  HH EH1 N M AH0 N
+HENNEN  HH EH1 N AH0 N
+HENNEPIN  HH EH1 N IH0 P IH0 N
+HENNER  HH EH1 N ER0
+HENNES  HH EH1 N Z
+HENNESS  HH EH1 N IH0 S
+HENNESSEE  HH EH1 N IH0 S IY0
+HENNESSEY  HH EH1 N AH0 S IY0
+HENNESSEY'S  HH EH1 N AH0 S IY0 Z
+HENNESSY  HH EH1 N AH0 S IY0
+HENNEY  HH EH1 N IY0
+HENNICK  HH EH1 N IH0 K
+HENNIG  HH EH1 N IH0 G
+HENNIGAN  HH EH1 N IH0 G AH0 N
+HENNIGAR  HH EH1 N IH0 G ER0
+HENNING  HH EH1 N IH0 NG
+HENNINGER  HH EH1 N IH0 NG ER0
+HENNINGS  HH EH1 N IH0 NG Z
+HENNINGSEN  HH EH1 N IH0 NG S AH0 N
+HENNINGTON  HH EH1 N IH0 NG T AH0 N
+HENNIS  HH EH1 N IH0 S
+HENNON  HH EH1 N AH0 N
+HENPECK  HH EH1 N P EH2 K
+HENPECKED  HH EH1 N P EH2 K T
+HENRI  HH EH1 N R IY0
+HENRI(1)  AO2 R IY1
+HENRI(2)  AA2 N R IY1
+HENRICH  HH EH1 N R IH0 K
+HENRICHS  HH EH1 N R IH0 K S
+HENRICHSEN  HH EH1 N R IH0 K S AH0 N
+HENRICK  HH EH1 N R IH0 K
+HENRICKS  HH EH1 N R IH0 K S
+HENRICKSEN  HH EH1 N R IH0 K S AH0 N
+HENRICKSON  HH EH1 N R IH0 K S AH0 N
+HENRIE  HH EH1 N ER0 IY0
+HENRIETTA  HH EH2 N R IY0 EH1 T AH0
+HENRIETTE  HH EH2 N R IY0 EH1 T
+HENRIK  HH EH1 N R IH0 K
+HENRIKA  HH EH1 N R IH0 K AH0
+HENRIKSEN  HH EH1 N R IH0 K S AH0 N
+HENRIKSON  HH EH1 N R IH0 K S AH0 N
+HENRIQUE  AA0 N R IY1 K
+HENRIQUES  HH EH0 N R IY1 K EH0 Z
+HENRIQUES(1)  AA0 N R IY1 K
+HENRIQUEZ  HH EH0 N R IY1 K EH0 Z
+HENRIQUEZ(1)  AA0 N R IY1 K
+HENRY  HH EH1 N R IY0
+HENRY'S  HH EH1 N R IY0 Z
+HENS  HH EH1 N Z
+HENSARLING  HH EH1 N S AA0 R L IH0 NG
+HENSCH  HH EH1 N SH
+HENSCHEL  HH EH1 N SH AH0 L
+HENSCHEN  HH EH1 N SH AH0 N
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+HENSLER  HH EH1 N S AH0 L ER0
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+HENTGES  HH EH1 N T JH IH0 Z
+HENTHORN  HH EH1 N TH AO2 R N
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+HENTOFF  HH EH1 N T AO0 F
+HENTON  HH EH1 N T AH0 N
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+HENZE  HH EH1 N Z
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+HENZLER  HH EH1 N Z L ER0
+HEON  HH IY1 AH0 N
+HEOROICO  HH IY2 AH0 R OW1 AH0 K OW0
+HEP  HH EH1 P
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+HEPBURN  HH EH1 P B ER0 N
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+HEPFER  HH EH1 P F ER0
+HEPKER  HH EH1 P K ER0
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+HEPNER  HH EH1 P N ER0
+HEPP  HH EH1 P
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+HEPPER  HH EH1 P ER0
+HEPPLER  HH EH1 P L ER0
+HEPPNER  HH EH1 P N ER0
+HEPTATHLON  HH EH0 P T AE1 TH L AA0 N
+HEPWORTH  HH EH1 P W ER0 TH
+HER  HH ER1
+HER'S  HH ER1 Z
+HERA  HH IH1 R AH0
+HERADIA  HH ER0 AA1 D IY0 AH0
+HERALD  HH EH1 R AH0 L D
+HERALD'S  HH EH1 R AH0 L D Z
+HERALDED  HH EH1 R AH0 L D IH0 D
+HERALDIC  HH EH0 R AE1 L D IH0 K
+HERALDING  HH EH1 R AH0 L D IH0 NG
+HERALDRY  HH EH1 R AH0 L D R IY0
+HERALDS  HH EH1 R AH0 L D Z
+HERB  ER1 B
+HERB(1)  HH ER1 B
+HERB'S  ER1 B Z
+HERB'S(1)  HH ER1 B Z
+HERBACEOUS  ER0 B EY1 SH AH0 S
+HERBAL  ER1 B AH0 L
+HERBAL(1)  HH ER1 B AH0 L
+HERBALIFE  ER1 B AH0 L AY2 F
+HERBALIFE(1)  HH ER1 B AH0 L AY2 F
+HERBALIST  ER1 B AH0 L IH2 S T
+HERBALIST(1)  HH ER1 B AH0 L IH2 S T
+HERBALIST'S  ER1 B AH0 L IH2 S T S
+HERBALIST'S(1)  HH ER1 B AH0 L IH2 S T S
+HERBALISTS  ER1 B AH0 L IH2 S T S
+HERBALISTS(1)  HH ER1 B AH0 L IH2 S T S
+HERBALISTS'  ER1 B AH0 L IH2 S T S
+HERBALISTS'(1)  HH ER1 B AH0 L IH2 S T S
+HERBARIUM  HH ER0 B EH1 R IY2 AH0 M
+HERBARIUM(1)  ER0 B EH1 R IY2 AH0 M
+HERBARIUMS  HH ER0 B EH1 R IY2 AH0 M Z
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+HERBECK  HH ER1 B EH0 K
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+HEROIZE  HH IY1 R OW0 AY2 Z
+HEROIZED  HH IY1 R OW0 AY2 Z D
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+HERRINGSHAW  HH EH1 R IH0 NG SH AO2
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+HERRIOTT  HH EH1 R IY0 AA0 T
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+HERRLINGER  HH EH1 R L IH2 NG ER0
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+HERTZBERG  HH ER1 T S B ER0 G
+HERTZENLEBEN  HH ER1 T AH0 N L EY2 B AH0 N
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+HERTZLER  HH ER1 T Z AH0 L ER0
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+HERTZOG(1)  HH ER1 T Z AA2 G
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+HERVE(1)  HH ER1 V EY0
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+HERZBERGER  HH ER1 Z B ER0 G ER0
+HERZEGOVINA  HH EH2 R T S AH0 G OW0 V IY1 N AH0
+HERZEGOVINA(1)  HH ER2 T S AH0 G OW0 V IY1 N AH0
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+HERZEGOVINA'S(1)  HH ER2 T S AH0 G OW0 V IY1 N AH0 Z
+HERZER  HH ER1 Z ER0
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+HERZIG  HH ER1 Z IH0 G
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+HESELTINE  HH EH1 S AH0 L T IY2 N
+HESELTINE(1)  HH EH1 S AH0 L T AY2 N
+HESELTON  HH IH0 S EH1 L T AH0 N
+HESHENG  HH EH1 SH EH1 NG
+HESIK  HH EH1 S IH0 K
+HESIK(1)  HH EY1 S IH0 K
+HESIOD  HH IY1 S IY0 AH0 D
+HESITANCY  HH EH1 Z IH0 T AH0 N S IY0
+HESITANT  HH EH1 Z IH0 T AH0 N T
+HESITANTLY  HH EH1 Z IH0 T AH0 N T L IY0
+HESITATE  HH EH1 Z AH0 T EY2 T
+HESITATED  HH EH1 Z IH0 T EY2 T IH0 D
+HESITATES  HH EH1 Z AH0 T EY2 T S
+HESITATING  HH EH1 Z AH0 T EY2 T IH0 NG
+HESITATION  HH EH2 Z AH0 T EY1 SH AH0 N
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+HESKETH  HH EH1 S K IH0 TH
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+HESLIN  HH EH1 Z L IH0 N
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+HESSTON  HH EH1 S T AH0 N
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+HFDF  EY1 CH EH1 F D IY1 EH1 F
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+HI  HH AY1
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+IMPONDERABLE  IH2 M P AA1 N D ER0 AH0 B AH0 L
+IMPONDERABLES  IH2 M P AA1 N D ER0 AH0 B AH0 L Z
+IMPORT  IH2 M P AO1 R T
+IMPORT(1)  IH1 M P AO2 R T
+IMPORTANCE  IH2 M P AO1 R T AH0 N S
+IMPORTANT  IH2 M P AO1 R T AH0 N T
+IMPORTANTLY  IH2 M P AO1 R T AH0 N T L IY0
+IMPORTATION  IH2 M P AO0 R T EY1 SH AH0 N
+IMPORTED  IH2 M P AO1 R T IH0 D
+IMPORTER  IH2 M P AO1 R T ER0
+IMPORTERS  IH2 M P AO1 R T ER0 Z
+IMPORTERS'  IH2 M P AO1 R T ER0 Z
+IMPORTING  IH2 M P AO1 R T IH0 NG
+IMPORTS  IH2 M P AO1 R T S
+IMPORTS(1)  IH1 M P AO2 R T S
+IMPORTS'  IH1 M P AO0 R T S
+IMPOSE  IH2 M P OW1 Z
+IMPOSED  IH2 M P OW1 Z D
+IMPOSES  IH2 M P OW1 Z IH0 Z
+IMPOSING  IH2 M P OW1 Z IH0 NG
+IMPOSITION  IH2 M P AH0 Z IH1 SH AH0 N
+IMPOSSIBILITY  IH2 M P AO2 S IH0 B IH1 L IH0 T IY0
+IMPOSSIBLE  IH2 M P AA1 S AH0 B AH0 L
+IMPOSSIBLE'S  IH2 M P AA1 S AH0 B AH0 L Z
+IMPOSSIBLY  IH2 M P AA1 S AH0 B L IY0
+IMPOSTER  IH2 M P AO1 S T ER0
+IMPOSTOR  IH2 M P AO1 S T ER0
+IMPOSTORS  IH2 M P AO1 S T ER0 Z
+IMPOTENCE  IH1 M P AH0 T AH0 N S
+IMPOTENT  IH1 M P AH0 T AH0 N T
+IMPOUND  IH2 M P AW1 N D
+IMPOUNDED  IH2 M P AW1 N D IH0 D
+IMPOUNDING  IH2 M P AW1 N D IH0 NG
+IMPOUNDMENT  IH2 M P AW1 N D M AH0 N T
+IMPOUNDMENTS  IH2 M P AW1 N D M AH0 N T S
+IMPOUNDS  IH2 M P AW1 N D Z
+IMPOVERISH  IH2 M P AA1 V R IH0 SH
+IMPOVERISHED  IH2 M P AA1 V R IH0 SH T
+IMPOVERISHES  IH2 M P AA1 V R IH0 SH AH0 Z
+IMPOVERISHING  IH2 M P AA1 V R IH0 SH IH0 NG
+IMPOVERISHMENT  IH2 M P AA1 V R IH0 SH M AH0 N T
+IMPRACTICABLE  IH2 M P R AE1 K T IH0 K AH0 B AH0 L
+IMPRACTICAL  IH2 M P R AE1 K T AH0 K AH0 L
+IMPRACTICAL(1)  IH2 M P R AE1 K T IH0 K AH0 L
+IMPRECISE  IH1 M P R AH0 S AY2 S
+IMPREGNABLE  IH2 M P R EH1 G N AH0 B AH0 L
+IMPREGNATE  IH2 M P R EH1 G N EY2 T
+IMPREGNATED  IH2 M P R EH1 G N EY2 T AH0 D
+IMPREGNATES  IH2 M P R EH1 G N EY2 T S
+IMPREGNATING  IH2 M P R EH1 G N EY2 T IH0 NG
+IMPREGNATION  IH2 M P R EH1 G N EY1 SH AH0 N
+IMPRESARIO  IH2 M P R IH0 S AA1 R IY0 OW2
+IMPRESS  IH2 M P R EH1 S
+IMPRESS(1)  IH1 M P R EH2 S
+IMPRESSED  IH2 M P R EH1 S T
+IMPRESSES  IH2 M P R EH1 S IH0 Z
+IMPRESSING  IH2 M P R EH1 S IH0 NG
+IMPRESSION  IH2 M P R EH1 SH AH0 N
+IMPRESSIONABLE  IH2 M P R EH1 SH AH0 N AH0 B AH0 L
+IMPRESSIONISM  IH2 M P R EH1 SH AH0 N IH2 Z AH0 M
+IMPRESSIONIST  IH2 M P R EH1 SH AH0 N AH0 S T
+IMPRESSIONIST(1)  IH2 M P R EH1 SH AH0 N IH0 S T
+IMPRESSIONISTIC  IH2 M P R EH2 SH AH0 N IH1 S T IH0 K
+IMPRESSIONISTS  IH2 M P R EH1 SH AH0 N IH0 S T S
+IMPRESSIONS  IH2 M P R EH1 SH AH0 N Z
+IMPRESSIVE  IH2 M P R EH1 S IH0 V
+IMPRESSIVELY  IH2 M P R EH1 S IH0 V L IY0
+IMPRESSMENT  IH2 M P R EH1 S M AH0 N T
+IMPRIMATUR  IH2 M P R IH0 M AA1 T ER0
+IMPRIMIS  IH2 M P R IY1 M IH0 S
+IMPRINT  IH2 M P R IH1 N T
+IMPRINT(1)  IH1 M P R IH0 N T
+IMPRINTED  IH2 M P R IH1 N T IH0 D
+IMPRINTED(1)  IH2 M P R IH1 N IH0 D
+IMPRINTING  IH2 M P R IH1 N T IH0 NG
+IMPRINTING(1)  IH2 M P R IH1 N IH0 NG
+IMPRINTS  IH2 M P R IH1 N T S
+IMPRISON  IH2 M P R IH1 Z AH0 N
+IMPRISONED  IH2 M P R IH1 Z AH0 N D
+IMPRISONING  IH2 M P R IH1 Z AH0 N IH0 NG
+IMPRISONMENT  IH2 M P R IH1 Z AH0 N M AH0 N T
+IMPRISONS  IH2 M P R IH1 Z AH0 N Z
+IMPROBABLE  IH2 M P R AA1 B AH0 B AH0 L
+IMPROBABLY  IH2 M P R AA1 B AH0 B L IY0
+IMPROMPTU  IH2 M P R AA1 M P T UW0
+IMPROPER  IH2 M P R AA1 P ER0
+IMPROPERLY  IH2 M P R AA1 P ER0 L IY0
+IMPROPRIETIES  IH2 M P R AH0 P R AY1 AH0 T IY0 Z
+IMPROPRIETY  IH2 M P R AH0 P R AY1 AH0 T IY0
+IMPROV  IH1 M P R AA2 V
+IMPROVE  IH2 M P R UW1 V
+IMPROVED  IH2 M P R UW1 V D
+IMPROVEMENT  IH2 M P R UW1 V M AH0 N T
+IMPROVEMENTS  IH2 M P R UW1 V M AH0 N T S
+IMPROVES  IH2 M P R UW1 V Z
+IMPROVING  IH2 M P R UW1 V IH0 NG
+IMPROVISATION  IH2 M P R AA0 V IH0 Z EY1 SH AH0 N
+IMPROVISATIONAL  IH2 M P R AA0 V IH0 Z EY1 SH AH0 N AH0 L
+IMPROVISATIONS  IH2 M P R AA0 V IH0 Z EY1 SH AH0 N Z
+IMPROVISE  IH1 M P R AH0 V AY2 Z
+IMPROVISE(1)  IH2 M P R AH0 V AY1 Z
+IMPROVISED  IH1 M P R AH0 V AY2 Z D
+IMPROVISES  IH1 M P R AH0 V AY2 Z AH0 Z
+IMPROVISES(1)  IH2 M P R AH0 V AY1 Z AH0 Z
+IMPROVISING  IH1 M P R AH0 V AY2 Z IH0 NG
+IMPRUDENCE  IH2 M P R UW1 D AH0 N S
+IMPRUDENT  IH2 M P R UW1 D AH0 N T
+IMPRUDENTLY  IH2 M P R UW1 D AH0 N T L IY0
+IMPSON  IH1 M P S AH0 N
+IMPUGN  IH2 M P Y UW1 N
+IMPUGNED  IH2 M P Y UW1 N D
+IMPUGNING  IH2 M P Y UW1 N IH0 NG
+IMPUGNS  IH2 M P Y UW1 N Z
+IMPULSE  IH1 M P AH0 L S
+IMPULSE(1)  IH2 M P AH1 L S
+IMPULSES  IH1 M P AH0 L S IH0 Z
+IMPULSES(1)  IH2 M P AH1 L S IH0 Z
+IMPULSIVE  IH2 M P AH1 L S IH0 V
+IMPULSIVELY  IH2 M P AH1 L S IH0 V L IY0
+IMPUNITY  IH2 M P Y UW1 N IH0 T IY0
+IMPURE  IH2 M P Y UH1 R
+IMPURITIES  IH2 M P Y UH1 R AH0 T IY0 Z
+IMPURITY  IH2 M P Y UH1 R AH0 T IY0
+IMPUTATION  IH2 M P Y AH0 T EY1 SH AH0 N
+IMPUTE  IH2 M P Y UW1 T
+IMPUTED  IH2 M P Y UW1 T IH0 D
+IMRE  IH1 M R AH0
+IMREG  IH2 M R EH1 G
+IMREG'S  IH2 M R EH1 G Z
+IMRIE  IH1 M ER0 IY0
+IMUS  AY1 M AH0 S
+IN  IH0 N
+IN(1)  IH1 N
+IN'S  IH0 N Z
+IN'S(1)  IH1 N Z
+IN-HOUSE  IH2 N HH AW1 S
+IN-LAW  IH2 N L AO1
+IN-LAWS  IH2 N L AO1 Z
+IN.  IH1 N
+IN.(1)  IH1 N CH
+INA  IY1 N AH0
+INABILITY  IH2 N AH0 B IH1 L IH0 T IY0
+INABINET  IH2 N AH0 B IH1 N IH0 T
+INACCESSIBILITY  IH2 N AH0 K S EH2 S AH0 B IH1 L AH0 T IY0
+INACCESSIBLE  IH2 N AH0 K S EH1 S AH0 B AH0 L
+INACCURACIES  IH2 N AE1 K Y ER0 AE2 S IY0 Z
+INACCURACY  IH2 N AE1 K Y ER0 AH0 S IY0
+INACCURATE  IH2 N AE1 K Y ER0 AH0 T
+INACCURATELY  IH2 N AE1 K Y ER0 AH0 T L IY0
+INACOM  IH1 N AH0 K AA2 M
+INACOM(1)  AY1 N AH0 K AA2 M
+INACOMP  AY1 N AH0 K AA2 M P
+INACOMP(1)  IH1 N AH0 K AA2 M P
+INACTION  IH2 N AE1 K SH AH0 N
+INACTIVATE  IH2 N AE1 K T IH0 V EY2 T
+INACTIVATED  IH2 N AE1 K T IH0 V EY2 T IH0 D
+INACTIVATES  IH2 N AE1 K T IH0 V EY2 T S
+INACTIVATION  IH2 N AE2 K T IH0 V EY1 SH AH0 N
+INACTIVE  IH2 N AE1 K T IH0 V
+INACTIVITY  IH2 N AE0 K T IH1 V IH0 T IY0
+INADEQUACIES  IH2 N AE1 D AH0 K W AH0 S IY0 Z
+INADEQUACY  IH2 N AE1 D IH0 K W AH0 S IY0
+INADEQUATE  IH2 N AE1 D AH0 K W AH0 T
+INADEQUATE(1)  IH2 N AE1 D AH0 K W EY2 T
+INADEQUATELY  IH2 N AE1 D AH0 K W AH0 T L IY0
+INADMISSIBLE  IH2 N AH0 D M IH1 S AH0 B AH0 L
+INADMISSIBLE(1)  IH2 N AE0 D M IH1 S AH0 B AH0 L
+INADMISSIBLY  IH2 N AH0 D M IH1 S AH0 B L IY0
+INADMISSIBLY(1)  IH2 N AE0 D M IH1 S AH0 B L IY0
+INADVERTENCE  IH2 N AH0 D V ER1 T AH0 N S
+INADVERTENCE(1)  IH2 N AE0 D V ER1 T AH0 N S
+INADVERTENT  IH2 N AH0 D V ER1 T AH0 N T
+INADVERTENT(1)  IH2 N AE0 D V ER1 T AH0 N T
+INADVERTENTLY  IH2 N AH0 D V ER1 T AH0 N T L IY0
+INADVERTENTLY(1)  IH2 N AE0 D V ER1 T AH0 N T L IY0
+INADVISABLE  IH2 N AH0 D V AY1 Z AH0 B AH0 L
+INADVISABLE(1)  IH2 N AE0 D V AY1 Z AH0 B AH0 L
+INAEZ  IH2 N AE1 Z
+INAEZ(1)  IH2 N EY1 Z
+INAKI  IH2 N AA1 K IY0
+INALIENABLE  IH2 N EY1 L Y AH0 N AH0 B AH0 L
+INAMURA  IH2 N AH0 M UH1 R AH0
+INANE  IH2 N EY1 N
+INANIMATE  IH2 N AE1 N AH0 M AH0 T
+INAPPLICABLE  IH2 N AE1 P L IH0 K AH0 B AH0 L
+INAPPROPRIATE  IH2 N AH0 P R OW1 P R IY0 IH0 T
+INAPPROPRIATELY  IH2 N AH0 P R AA1 P R IY0 AH0 T L IY0
+INARTICULATE  IH2 N AA0 R T IH1 K Y AH0 L AH0 T
+INASMUCH  IH2 N AE1 S M AH0 K
+INATTENTION  IH2 N AH0 T EH1 N CH AH0 N
+INATTENTIVE  IH2 N AH0 T EH1 N T IH0 V
+INAUDIBLE  IH2 N AO1 D AH0 B AH0 L
+INAUDIBLY  IH2 N AO1 D AH0 B L IY0
+INAUGURAL  IH2 N AO1 G ER0 AH0 L
+INAUGURAL(1)  IH2 N AO1 G Y ER0 AH0 L
+INAUGURATE  IH2 N AO1 G Y AH0 R EY2 T
+INAUGURATED  IH2 N AO1 G Y ER0 EY2 T IH0 D
+INAUGURATED(1)  IH2 N AO1 G ER0 EY2 T IH0 D
+INAUGURATES  IH2 N AO1 G Y AH0 R EY2 T S
+INAUGURATING  IH2 N AO1 G Y AH0 R EY2 T IH0 NG
+INAUGURATION  IH2 N AO2 G Y AH0 R EY1 SH AH0 N
+INAUGURATIONS  IH2 N AO2 G Y AH0 R EY1 SH AH0 N Z
+INAUSPICIOUS  IH2 N AW2 S P IH1 SH IH0 S
+INBIO  IH2 N B AY1 OW0
+INBOARD  IH1 N B AO2 R D
+INBODEN  IH1 N B OW0 D AH0 N
+INBODY  IH1 N B AA0 D IY0
+INBORN  IH1 N B AO2 R N
+INBOUND  IH2 N B AW1 N D
+INBOUND(1)  IH1 N B AW0 N D
+INBRED  IH1 N B R EH2 D
+INBREED  IH1 N B R IY2 D
+INBREEDING  IH2 N B R IY1 D IH0 NG
+INC  IH1 NG K
+INC.  IH1 NG K
+INC.'S  IH1 NG K S
+INCA  IH1 NG K AH0
+INCALCULABLE  IH2 N K AE1 L K Y AH0 L AH0 B AH0 L
+INCANDESCENT  IH2 N K AH0 N D EH1 S AH0 N T
+INCANT  IH2 N K AE1 N T
+INCANTATION  IH2 N K AE1 N T EY1 SH AH0 N
+INCANTATORY  IH2 N K AE1 N T AH0 T AO2 R IY0
+INCAPABLE  IH2 N K EY1 P AH0 B AH0 L
+INCAPACITATE  IH2 N K AH0 P AE1 S IH0 T EY2 T
+INCAPACITATED  IH2 N K AH0 P AE1 S IH0 T EY2 T IH0 D
+INCAPACITATING  IH2 N K AH0 P AE1 S IH0 T EY2 T IH0 NG
+INCAPACITATION  IH2 N K AH0 P AE2 S IH0 T EY1 SH AH0 N
+INCAPACITY  IH2 N K AH0 P AE1 S AH0 T IY0
+INCARCERATE  IH2 N K AA1 R S ER0 EY2 T
+INCARCERATED  IH2 N K AA1 R S ER0 EY2 T IH0 D
+INCARCERATES  IH2 N K AA1 R S ER0 EY2 T S
+INCARCERATING  IH2 N K AA1 R S ER0 EY2 T IH0 NG
+INCARCERATION  IH2 N K AA2 R S ER0 EY1 SH AH0 N
+INCARNATE  IH2 N K AA1 R N AH0 T
+INCARNATE(1)  IH2 N K AA1 R N EY2 T
+INCARNATION  IH2 N K AA1 R N EY1 SH AH0 N
+INCARNATIONS  IH2 N K AA0 R N EY1 SH AH0 N Z
+INCAS  IH1 NG K AH0 Z
+INCASE  IH2 N K EY1 S
+INCATA  IH2 NG K AA1 T AH0
+INCATA'S  IH2 NG K AA1 T AH0 Z
+INCE  IH1 N S
+INCENDIARY  IH2 N S EH1 N D IY0 EH0 R IY0
+INCENSE  IH2 N S EH1 N S
+INCENSE(1)  IH1 N S EH2 N S
+INCENSED  IH1 N S EH2 N S T
+INCENTIVE  IH2 N S EH1 N T IH0 V
+INCENTIVE(1)  IH2 N S EH1 N IH0 V
+INCENTIVES  IH2 N S EH1 N T IH0 V Z
+INCENTIVES(1)  IH2 N S IH1 N IH0 V Z
+INCEPTION  IH2 N S EH1 P SH AH0 N
+INCESSANT  IH2 N S EH1 S AH0 N T
+INCESSANTLY  IH2 N S EH1 S AH0 N T L IY0
+INCEST  IH1 N S EH2 S T
+INCESTUOUS  IH2 N S EH1 S CH W AH0 S
+INCH  IH1 N CH
+INCHCAPE  IH1 N CH K EY2 P
+INCHED  IH1 N CH T
+INCHEON  IH1 N CH AO0 N
+INCHES  IH1 N CH AH0 Z
+INCHES(1)  IH1 N CH IH0 Z
+INCHES'  IH1 N CH AH0 Z
+INCHES'(1)  IH1 N CH IH0 Z
+INCHING  IH1 N CH IH0 NG
+INCHOATE  IH2 N K OW1 AH0 T
+INCHON  IH1 N CH AO0 N
+INCIDENCE  IH1 N S AH0 D AH0 N S
+INCIDENCE(1)  IH1 N S IH0 D AH0 N S
+INCIDENCES  IH1 N S AH0 D AH0 N S IH0 Z
+INCIDENT  IH1 N S AH0 D AH0 N T
+INCIDENTAL  IH2 N S IH0 D EH1 N T AH0 L
+INCIDENTALLY  IH2 N S IH0 D EH1 N T AH0 L IY0
+INCIDENTALLY(1)  IH2 N S IH0 D EH1 N T L IY0
+INCIDENTALS  IH2 N S IH0 D EH1 N T AH0 L Z
+INCIDENTS  IH1 N S AH0 D AH0 N T S
+INCINERATE  IH2 N S IH1 N ER0 EY2 T
+INCINERATED  IH2 N S IH1 N ER0 EY2 T IH0 D
+INCINERATING  IH2 N S IH1 N ER0 EY2 T IH0 NG
+INCINERATION  IH2 N S IH1 N ER0 EY2 SH AH0 N
+INCINERATOR  IH2 N S IH1 N ER0 EY2 T ER0
+INCINERATORS  IH2 N S IH1 N ER0 EY2 T ER0 Z
+INCIPIENT  IH2 N S IH1 P IY0 AH0 N T
+INCIRLIK  IH2 N S ER1 L IH0 K
+INCISE  IH2 N S AY1 Z
+INCISED  IH2 N S AY1 Z D
+INCISION  IH2 N S IH1 ZH AH0 N
+INCISIONS  IH2 N S IH1 ZH AH0 N Z
+INCISIVE  IH2 N S AY1 S IH0 V
+INCISOR  IH2 N S AY1 Z ER0
+INCISORS  IH2 N S AY1 Z ER0 Z
+INCITE  IH2 N S AY1 T
+INCITED  IH2 N S AY1 T IH0 D
+INCITEMENT  IH2 N S AY1 T M AH0 N T
+INCITEMENTS  IH2 N S AY1 T M AH0 N T S
+INCITES  IH2 N S AY1 T S
+INCITING  IH2 N S AY1 T IH0 NG
+INCIVILITY  IH2 N S IH0 V IH1 L IH0 T IY0
+INCLEMENT  IH2 N K L EH1 M AH0 N T
+INCLEMENT(1)  IH1 N K L IH0 M AH0 N T
+INCLINATION  IH2 N K L AH0 N EY1 SH AH0 N
+INCLINATIONS  IH2 N K L AH0 N EY1 SH AH0 N Z
+INCLINE  IH2 N K L AY1 N
+INCLINE(1)  IH1 N K L AY0 N
+INCLINED  IH2 N K L AY1 N D
+INCLINES  IH2 N K L AY1 N Z
+INCLINES(1)  IH1 N K L AY0 N Z
+INCLOSURE  IH2 N K L OW1 ZH ER0
+INCLUDE  IH2 N K L UW1 D
+INCLUDED  IH2 N K L UW1 D AH0 D
+INCLUDED(1)  IH2 N K L UW1 D IH0 D
+INCLUDES  IH2 N K L UW1 D Z
+INCLUDING  IH2 N K L UW1 D IH0 NG
+INCLUSION  IH2 N K L UW1 ZH AH0 N
+INCLUSIONS  IH2 N K L UW1 ZH AH0 N Z
+INCLUSIVE  IH2 N K L UW1 S IH0 V
+INCLUSIVENESS  IH2 N K L UW1 S IH0 V N IH0 S
+INCLUSIVENESS(1)  IH2 N K L UW1 S IH0 V N EH0 S
+INCO  IH2 N K OW1
+INCO'S  IH1 NG K OW0 Z
+INCOGNITO  IH2 N K AO0 G N IY1 T OW0
+INCOHERENCE  IH2 N K OW0 HH IH1 R AH0 N S
+INCOHERENT  IH2 N K OW0 HH IH1 R AH0 N T
+INCOHERENTLY  IH2 N K OW0 HH IH1 R AH0 N T L IY0
+INCOM  IH1 NG K AA0 M
+INCOME  IH1 N K AH2 M
+INCOMES  IH1 N K AH2 M Z
+INCOMING  IH1 N K AH2 M IH0 NG
+INCOMMUNICADO  IH2 N K AH0 M Y UW2 N AH0 K AA1 D OW0
+INCOMPARABLE  IH2 N K AA1 M P ER0 AH0 B AH0 L
+INCOMPARABLY  IH2 N K AA1 M P ER0 AH0 B L IY0
+INCOMPATIBILITY  IH2 N K AA2 M P AH0 T IH0 B IH1 L IH0 T IY0
+INCOMPATIBLE  IH2 N K AH0 M P AE1 T AH0 B AH0 L
+INCOMPETENCE  IH2 N K AA1 M P AH0 T AH0 N S
+INCOMPETENCY  IH2 N K AA1 M P AH0 T AH0 N S IY0
+INCOMPETENT  IH2 N K AA1 M P AH0 T AH0 N T
+INCOMPETENTLY  IH2 N K AA1 M P AH0 T AH0 N T L IY0
+INCOMPETENTS  IH2 NG K AA1 M P AH0 T AH0 N T S
+INCOMPLETE  IH2 N K AH0 M P L IY1 T
+INCOMPREHENSIBLE  IH2 NG K AA2 M P R AH0 HH EH1 N S IH0 B AH0 L
+INCOMPREHENSIBLY  IH2 NG K AA2 M P R AH0 HH EH1 N S IH0 B L IY0
+INCOMPRESSIBLE  IH2 N K AH0 M P R EH1 S AH0 B AH0 L
+INCONCEIVABLE  IH2 N K AH0 N S IY1 V AH0 B AH0 L
+INCONCLUSIVE  IH2 N K AH0 N K L UW1 S IH0 V
+INCONCLUSIVELY  IH2 NG K AA1 N K L UW0 S IH0 V L IY0
+INCONGRUITY  IH2 NG K AO0 NG R UW1 IH0 T IY0
+INCONGRUOUS  IH2 NG K AO1 NG R UW0 AH0 S
+INCONGRUOUSLY  IH2 NG K AO1 NG R UW0 AH0 S L IY0
+INCONSEQUENTIAL  IH2 NG K AA2 N S AH0 K W EH1 N CH AH0 L
+INCONSISTENCIES  IH2 NG K AA1 N S IH0 S T EH2 N S IY0 Z
+INCONSISTENCY  IH2 N K AH0 N S IH1 S T AH0 N S IY0
+INCONSISTENT  IH2 N K AH0 N S IH1 S T AH0 N T
+INCONSPICUOUS  IH2 NG K AA1 N S P IH0 K W AH0 S
+INCONSTANCY  IH2 N K AA1 N S T AH0 N S IY0
+INCONTINENCE  IH2 N K AA1 N T AH0 N AH0 N S
+INCONTINENT  IH2 N K AA1 N T AH0 N AH0 N T
+INCONTROVERTIBLE  IH2 NG K AA2 N T R OW0 V ER1 T IH0 B AH0 L
+INCONVENIENCE  IH2 N K AH0 N V IY1 N Y AH0 N S
+INCONVENIENCED  IH2 N K AH0 N V IY1 N Y AH0 N S T
+INCONVENIENCES  IH2 N K AH0 N V IY1 N Y AH0 N S IH0 Z
+INCONVENIENT  IH2 N K AH0 N V IY1 N Y AH0 N T
+INCOORDINATION  IH2 N K OW2 AO1 R D AH0 N EY2 SH AH0 N
+INCORPORATE  IH2 N K AO1 R P ER0 EY2 T
+INCORPORATED  IH2 N K AO1 R P ER0 EY2 T IH0 D
+INCORPORATED'S  IH2 N K AO1 R P ER0 EY2 T IH0 D Z
+INCORPORATES  IH2 N K AO1 R P ER0 EY2 T S
+INCORPORATING  IH2 N K AO1 R P ER0 EY2 T IH0 NG
+INCORPORATION  IH2 N K AO2 R P ER0 EY1 SH AH0 N
+INCORPORATION'S  IH2 N K AO2 R P ER0 EY1 SH AH0 N Z
+INCORPORATIONS  IH2 N K AO2 R P ER0 EY1 SH AH0 N Z
+INCORRECT  IH2 N K ER0 EH1 K T
+INCORRECTLY  IH2 N K ER0 EH1 K T L IY0
+INCORRIGIBLE  IH2 N K AA1 R AH0 JH AH0 B AH0 L
+INCORVAIA  IH2 N K AO0 R V AA1 Y AH0
+INCREASE  IH2 N K R IY1 S
+INCREASE(1)  IH1 N K R IY2 S
+INCREASED  IH2 N K R IY1 S T
+INCREASED(1)  IH1 N K R IY2 S T
+INCREASES  IH2 N K R IY1 S IH0 Z
+INCREASES(1)  IH1 N K R IY2 S IH0 Z
+INCREASING  IH2 N K R IY1 S IH0 NG
+INCREASINGLY  IH2 N K R IY1 S IH0 NG G L IY0
+INCREDIBLE  IH2 N K R EH1 D AH0 B AH0 L
+INCREDIBLY  IH2 N K R EH1 D AH0 B L IY0
+INCREDULITY  IH2 N K R AH0 D UW1 L IH0 T IY0
+INCREDULOUS  IH2 N K R EH1 JH AH0 L AH0 S
+INCREMENT  IH1 N K R AH0 M AH0 N T
+INCREMENTAL  IH2 N K R AH0 M EH1 N T AH0 L
+INCREMENTALISM  IH2 N K R AH0 M EH1 N T AH0 L IH2 Z M
+INCREMENTALLY  IH2 N K R AH0 M EH1 N T AH0 L IY2
+INCREMENTED  IH1 N K R AH0 M EH2 N T IH0 D
+INCREMENTS  IH1 NG K R AH0 M EH2 N T S
+INCRIMINATE  IH2 N K R IH1 M AH0 N EY2 T
+INCRIMINATING  IH2 N K R IH1 M AH0 N EY2 T IH0 NG
+INCRIMINATION  IH2 N K R IH2 M AH0 N EY1 SH AH0 N
+INCRUST  IH2 N K R AH1 S T
+INCRUSTATION  IH2 N K R AH0 S T EY1 SH AH0 N
+INCSTAR  IH1 NG K S T AA2 R
+INCUBATE  IH1 N K Y AH0 B EY2 T
+INCUBATES  IH1 N K Y AH0 B EY2 T S
+INCUBATING  IH1 N K Y AH0 B EY2 T IH0 NG
+INCUBATION  IH2 NG K Y UW0 B EY1 SH AH0 N
+INCUBATOR  IH1 NG K Y AH0 B EY2 T ER0
+INCUBATORS  IH1 NG K Y UW0 B EY2 T ER0 Z
+INCULCATE  IH1 NG K AH0 L K EY2 T
+INCULCATED  IH1 NG K AH0 L K EY2 T AH0 D
+INCULCATES  IH1 NG K AH0 L K EY2 T S
+INCUMBENCY  IH2 N K AH1 M B AH0 N S IY0
+INCUMBENT  IH2 N K AH1 M B AH0 N T
+INCUMBENT'S  IH2 N K AH1 M B AH0 N T S
+INCUMBENTS  IH2 N K AH1 M B AH0 N T S
+INCUR  IH2 N K ER1
+INCURABLE  IH2 N K Y UH1 R AH0 B AH0 L
+INCURRED  IH2 N K ER1 D
+INCURRING  IH2 N K ER1 IH0 NG
+INCURS  IH2 N K ER1 Z
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+INCURSIONS  IH2 N K ER1 ZH AH0 N Z
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+INDECENT  IH2 N D IY1 S AH0 N T
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+INDEED  IH2 N D IY1 D
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+INDEFENSIBLE  IH2 N D IH0 F EH1 N S AH0 B AH0 L
+INDEFINABLE  IH2 N D IH0 F AY1 N AH0 B AH0 L
+INDEFINITE  IH2 N D EH1 F AH0 N AH0 T
+INDEFINITELY  IH2 N D EH1 F AH0 N AH0 T L IY0
+INDELIBLE  IH2 N D EH1 L IH0 B AH0 L
+INDELIBLY  IH2 N D EH1 L AH0 B L IY0
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+INDELICATO  IH2 N D EH0 L IY0 K AA1 T OW0
+INDEMNIFICATION  IH2 N D EH2 M N AH0 F IH0 K EY1 SH AH0 N
+INDEMNIFIED  IH2 N D EH1 M N AH0 F AY2 D
+INDEMNIFY  IH2 N D EH1 M N AH0 F AY2
+INDEMNIFYING  IH2 N D EH2 M N IH0 F AY1 IH0 NG
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+INDEMNITY  IH2 N D EH1 M N AH0 T IY0
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+INDENT  IH2 N D EH1 N T
+INDENTATION  IH2 N D EH2 N T EY1 SH AH0 N
+INDENTURE  IH2 N D EH1 N CH ER0
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+INDENTURES  IH2 N D EH1 N CH ER0 Z
+INDEPENDENCE  IH2 N D IH0 P EH1 N D AH0 N S
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+INDEPENDENT  IH2 N D IH0 P EH1 N D AH0 N T
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+INDESTRUCTIBLE  IH2 N D AH0 S T R AH1 K T IH0 B AH0 L
+INDETERMINATE  IH2 N D IH0 T ER1 M IH0 N IH0 T
+INDEX  IH1 N D EH0 K S
+INDEX'S  IH1 N D EH0 K S IH0 Z
+INDEXATION  IH1 N D EH2 K S EY1 SH AH0 N
+INDEXED  IH1 N D EH0 K S T
+INDEXER  IH1 N D EH2 K S ER0
+INDEXERS  IH1 N D EH2 K S ER0 Z
+INDEXES  IH1 N D EH0 K S IH0 Z
+INDEXING  IH1 N D EH0 K S IH0 NG
+INDIA  IH1 N D IY0 AH0
+INDIA'S  IH1 N D IY0 AH0 Z
+INDIAIA  IH2 N D IY0 AY1 AH0
+INDIAN  IH1 N D IY0 AH0 N
+INDIAN'S  IH1 N D IY0 AH0 N Z
+INDIANA  IH2 N D IY0 AE1 N AH0
+INDIANA'S  IH2 N D IY0 AE1 N AH0 Z
+INDIANAPOLIS  IH2 N D IY0 AH0 N AE1 P AH0 L IH0 S
+INDIANAPOLIS'S  IH2 N D IY0 AH0 N AE1 P AH0 L IH0 S IH0 Z
+INDIANIAN  IH2 N D IY0 AE1 N IY0 AH0 N
+INDIANIANS  IH2 N D IY0 AE1 N IY0 AH0 N Z
+INDIANOLA  IH1 N D IY0 AH0 N OW1 L AH0
+INDIANS  IH1 N D IY0 AH0 N Z
+INDIANS'  IH1 N D IY0 AH0 N Z
+INDIC  IH1 N D IH0 K
+INDICA  IH1 D AH0 K AH0
+INDICATE  IH1 N D AH0 K EY2 T
+INDICATED  IH1 N D AH0 K EY2 T AH0 D
+INDICATED(1)  IH1 N D AH0 K EY2 T IH0 D
+INDICATES  IH1 N D IH0 K EY2 T S
+INDICATING  IH1 N D AH0 K EY2 T IH0 NG
+INDICATION  IH2 N D AH0 K EY1 SH AH0 N
+INDICATIONS  IH2 N D AH0 K EY1 SH AH0 N Z
+INDICATIVE  IH2 N D IH1 K AH0 T IH0 V
+INDICATOR  IH1 N D AH0 K EY2 T ER0
+INDICATORS  IH1 N D AH0 K EY2 T ER0 Z
+INDICES  IH1 N D IH0 S IY2 Z
+INDICES(1)  IH1 N D AH0 S IH0 Z
+INDICIA  IH2 N D IH1 S Y AH0
+INDICT  IH2 N D AY1 T
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+INDICTING  IH2 N D AY1 T IH0 NG
+INDICTMENT  IH2 N D AY1 T M AH0 N T
+INDICTMENTS  IH2 N D AY1 T M AH0 N T S
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+INDIES  IH1 N D IY0 Z
+INDIFFERENCE  IH2 N D IH1 F ER0 AH0 N S
+INDIFFERENCE(1)  IH2 N D IH1 F R AH0 N S
+INDIFFERENT  IH2 N D IH1 F R AH0 N T
+INDIFFERENT(1)  IH2 N D IH1 F ER0 AH0 N T
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+INDIGENOUSLY  IH2 N D IH1 JH AH0 N AH0 S L IY2
+INDIGENT  IH1 N D IH0 JH AH0 N T
+INDIGENTS  IH1 N D IH0 JH AH0 N T S
+INDIGEST  IH2 N D AY0 JH EH1 S T
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+INDIGESTION  IH2 N D AY0 JH EH1 S CH AH0 N
+INDIGNANT  IH2 N D IH1 G N AH0 N T
+INDIGNANTLY  IH2 N D IH1 G N AH0 N T L IY2
+INDIGNATION  IH2 N D IH0 G N EY1 SH AH0 N
+INDIGNITIES  IH2 N D IH1 G N AH0 T IY0 Z
+INDIGNITY  IH2 N D IH1 G N AH0 T IY2
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+INDIRA  IH2 N D IH1 R AH0
+INDIRECT  IH2 N D ER0 EH1 K T
+INDIRECTLY  IH2 N D ER0 EH1 K T L IY2
+INDISCREET  IH2 N D IH0 S K R IY1 T
+INDISCRETION  IH2 N D IH0 S K R EH1 SH AH0 N
+INDISCRETIONS  IH2 N D IH0 S K R EH1 SH AH0 N Z
+INDISCRIMINATE  IH2 N D IH0 S K R IH1 M AH0 N AH0 T
+INDISCRIMINATELY  IH2 N D IH0 S K R IH1 M AH0 N AH0 T L IY2
+INDISPENSABLE  IH2 N D IH0 S P EH1 N S AH0 B AH0 L
+INDISPENSIBLE  IH2 N D IH0 S P EH1 N S IH0 B AH0 L
+INDISPUTABLE  IH2 N D IH0 S P Y UW1 T AH0 B AH0 L
+INDISPUTABLY  IH2 N D IH1 S P Y UW0 T AE2 B L IY2
+INDISTINCT  IH2 N D IH0 S T IH1 NG K T
+INDISTINGUISHABLE  IH2 N D IH0 S T IH1 NG G W IH0 SH AH0 B AH0 L
+INDITE  IH2 N D AY1 T
+INDITED  IH2 N D AY1 T IH0 D
+INDIUM  IH1 N D IY0 AH0 M
+INDIVIDUAL  IH2 N D AH0 V IH1 JH AH0 W AH0 L
+INDIVIDUAL'S  IH2 N D AH0 V IH1 JH AH0 W AH0 L Z
+INDIVIDUALISM  IH2 N D IH0 V IH0 D UW1 AH0 L IH2 Z AH0 M
+INDIVIDUALIST  IH2 N D IH0 V IH0 D UW1 AH0 L IH0 S T
+INDIVIDUALISTIC  IH2 N D IH0 V IH2 JH UW0 AH0 L IH1 S T IH0 K
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+INDIVIDUALITY  IH2 N D IH2 V IH0 JH UW0 AE1 L IH0 T IY0
+INDIVIDUALIZE  IH2 N D IH0 V IH1 JH UW0 AH0 L AY0 Z
+INDIVIDUALIZE(1)  IH2 N D IH0 V IH1 JH AH0 L AY0 Z
+INDIVIDUALIZED  IH2 N D IH0 V IH1 JH UW0 AH0 L AY0 Z D
+INDIVIDUALIZED(1)  IH2 N D IH0 V IH1 JH AH0 L AY0 Z D
+INDIVIDUALLY  IH2 N D IH0 V IH1 JH UW0 AH0 L IY0
+INDIVIDUALLY(1)  IH2 N D IH0 V IH1 JH AH0 L IY0
+INDIVIDUALS  IH2 N D AH0 V IH1 JH AH0 W AH0 L Z
+INDIVIDUALS'  IH2 N D IH0 V IH1 JH AH0 W AH0 L Z
+INDIVISIBLE  IH2 N D IH0 V IH1 S IH0 B AH0 L
+INDO  IH1 N D OW0
+INDO-EUROPEAN  IH2 N D OW0 Y UH2 R AH0 P IY1 AH0 N
+INDOCHINA  IH2 N D OW0 CH AY1 N AH0
+INDOCHINESE  IH2 N D OW0 CH AY2 N IY1 Z
+INDOCTRINATE  IH0 N D AA1 K T R AH0 N EY2 T
+INDOCTRINATED  IH2 N D AA1 K T R AH0 N EY2 T IH0 D
+INDOCTRINATION  IH2 N D AA2 K T R AH0 N EY1 SH AH0 N
+INDOLENT  IH1 N D AH0 L AH0 N T
+INDOMITABLE  IH2 N D AA1 M AH0 T AH0 B AH0 L
+INDONESIA  IH2 N D OW0 N IY1 ZH AH0
+INDONESIA'S  IH2 N D OW0 N IY1 ZH AH0 Z
+INDONESIAN  IH2 N D OW0 N IY1 ZH AH0 N
+INDONESIANS  IH2 N D OW0 N IY1 S IY0 AH0 N Z
+INDONESIANS(1)  IH2 N D OW0 N IY1 ZH AH0 N Z
+INDOOR  IH1 N D AO2 R
+INDOORS  IH1 N D AO2 R Z
+INDOSUEZ  IH1 N D OW0 S UW0 EY1 Z
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+INDOVINA  IH2 N D OW0 V IY1 N AH0
+INDRI  IH2 N D R IY1
+INDUBIOUS  IH2 D UW1 B IY0 AH0 S
+INDUBIOUSLY  IH2 D UW1 B IY0 AH0 S L IY0
+INDUCE  IH2 N D UW1 S
+INDUCED  IH2 N D UW1 S T
+INDUCEMENT  IH2 N D UW1 S M AH0 N T
+INDUCEMENTS  IH2 N D UW1 S M AH0 N T S
+INDUCES  IH2 N D UW1 S IH0 Z
+INDUCING  IH2 N D UW1 S IH0 NG
+INDUCT  IH2 N D AH1 K T
+INDUCTANCE  IH2 N D AH1 K T AH0 N S
+INDUCTED  IH2 N D AH1 K T IH0 D
+INDUCTEE  IH2 N D AH1 K T IY1
+INDUCTEES  IH2 N D AH1 K T IY1 Z
+INDUCTION  IH2 N D AH1 K SH AH0 N
+INDUCTIVE  IH2 N D AH1 K T IH0 V
+INDUCTOR  IH2 N D AH1 K T ER0
+INDULGE  IH2 N D AH1 L JH
+INDULGED  IH2 N D AH1 L JH D
+INDULGENCE  IH2 N D AH1 L JH AH0 N S
+INDULGENCES  IH2 N D AH1 L JH AH0 N S IH0 Z
+INDULGENT  IH2 N D AH1 L JH AH0 N T
+INDULGES  IH2 N D AH1 L JH IH0 Z
+INDULGING  IH2 N D AH1 L JH IH0 NG
+INDUS  IH1 N D AH0 S
+INDUSTRI  IH2 N D AH1 S T R IY0
+INDUSTRIA  IH2 N D AH1 S T R IY0 AH0
+INDUSTRIAL  IH2 N D AH1 S T R IY0 AH0 L
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+INDUSTRIALE  IH2 N D AH0 S T R IY0 AA1 L IY0
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+INDUSTRIALIZED  IH2 N D AH1 S T R IY0 AH0 L AY2 Z D
+INDUSTRIALIZING  IH2 N D AH1 S T R IY0 AH0 L AY2 Z IH0 NG
+INDUSTRIALLY  IH2 N D AH1 S T R IY0 AH0 L IY0
+INDUSTRIALS  IH2 N D AH1 S T R IY0 AH0 L Z
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+INDUSTRIE  IH1 N D AH0 S T R IY0
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+INDUSTRIELLE(1)  IH2 N D AH1 S T R IY0 AH0 L EH2
+INDUSTRIER  IH1 N D AH0 S T R IY2 ER0
+INDUSTRIES  IH1 N D AH0 S T R IY0 Z
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+INEBRIATED  IH2 N EH1 B R IY0 EY2 T AH0 D
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+INEBRIATING  IH2 N EH1 B R IY0 EY2 T IH0 NG
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+INEFFICIENCY  IH2 N IH0 F IH1 SH AH0 N S IY0
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+INEPTLY  IH2 N EH1 P T L IY0
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+INEQUALITY  IH2 N IH0 K W AA1 L AH0 T IY0
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+INEQUALITY(2)  IH1 N IY0 K W AA1 L AH0 T IY0
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+INEVITABILITY  IH2 N EH2 V IH0 T AH0 B IH1 L IH0 T IY0
+INEVITABLE  IH2 N EH1 V AH0 T AH0 B AH0 L
+INEVITABLY  IH2 N EH1 V AH0 T AH0 B L IY0
+INEXACT  IH2 N IH0 G Z AE1 K T
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+INEXPERIENCED  IH2 N EH0 K S P IH1 R IY0 AH0 N S T
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+INEXPLICABLY  IH2 N IH0 K S P L IH1 K AH0 B L IY0
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+INITIATING  IH2 N IH1 SH IY0 EY2 T IH0 NG
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+INSANITY  IH2 N S AE1 N AH0 T IY0
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+INSATIABLE  IH2 N S EY1 SH AH0 B AH0 L
+INSCHO  IH1 N SH OW0
+INSCO  IY1 N S K OW0
+INSCOE  IH2 N S K OW1
+INSCORE  IH2 N S K AO1 R IY0
+INSCRIBE  IH2 N S K R AY1 B
+INSCRIBED  IH2 N S K R AY1 B D
+INSCRIBES  IH2 N S K R AY1 B Z
+INSCRIBING  IH2 N S K R AY1 B IH0 NG
+INSCRIPTION  IH2 N S K R IH1 P SH AH0 N
+INSCRIPTIONS  IH2 N S K R IH1 P SH AH0 N Z
+INSCRUTABLE  IH2 N S K R UW1 T AH0 B AH0 L
+INSECT  IH1 N S EH2 K T
+INSECT'S  IH1 N S EH2 K T S
+INSECTICIDE  IH2 N S EH1 K T AH0 S AY2 D
+INSECTICIDES  IH2 N S EH1 K T AH0 S AY2 D Z
+INSECTIVOROUS  IH2 N S EH0 K T IH1 V ER0 AH0 S
+INSECTS  IH1 N S EH2 K T S
+INSECURE  IH1 N S AH0 K Y ER0
+INSECURITIES  IH2 N S AH0 K Y UH1 R IH0 T IY0 Z
+INSECURITY  IH2 N S IH0 K Y UH1 R IH0 T IY0
+INSEL  IH1 N S AH0 L
+INSEMINATE  IH2 N S EH1 M AH0 N EY2 T
+INSEMINATION  IH2 N S EH2 M AH0 N EY1 SH AH0 N
+INSENSIBLE  IH2 N S EH1 N S AH0 B AH0 L
+INSENSITIVE  IH2 N S EH1 N S AH0 T IH0 V
+INSENSITIVE(1)  IH2 N S EH1 N S IH0 T IH0 V
+INSENSITIVITY  IH2 N S EH2 N S AH0 T IH1 V AH0 T IY0
+INSEPARABLE  IH2 N S EH1 P ER0 AH0 B AH0 L
+INSEPARABLY  IH2 N S EH1 P ER0 AH0 B L IY0
+INSERRA  IH2 N S EH1 R AH0
+INSERT  IH2 N S ER1 T
+INSERT(1)  IH1 N S ER2 T
+INSERTED  IH2 N S ER1 T AH0 D
+INSERTED(1)  IH2 N S ER1 T IH0 D
+INSERTING  IH2 N S ER1 T IH0 NG
+INSERTION  IH2 N S ER1 SH AH0 N
+INSERTS  IH2 N S ER1 T S
+INSERTS(1)  IH1 N S ER2 T S
+INSET  IH1 N S EH2 T
+INSHORE  IH1 N SH AO1 R
+INSIDE  IH2 N S AY1 D
+INSIDE(1)  IH1 N S AY2 D
+INSIDER  IH2 N S AY1 D ER0
+INSIDER'S  IH2 N S AY1 D ER0 Z
+INSIDERS  IH2 N S AY1 D ER0 Z
+INSIDERS'  IH2 N S AY1 D ER0 Z
+INSIDES  IH2 N S AY1 D Z
+INSIDES(1)  IH1 N S AY0 D Z
+INSIDIOUS  IH2 N S IH1 D IY2 AH0 S
+INSIGHT  IH1 N S AY2 T
+INSIGHTFUL  IH1 N S AY2 T F AH0 L
+INSIGHTS  IH1 N S AY2 T S
+INSIGNIA  IH2 N S IH1 G N IY2 AH0
+INSIGNIFICANCE  IH2 N S IH0 G N Y IH1 F IH0 K AH0 N S
+INSIGNIFICANT  IH2 N S IH0 G N Y IH1 F IH0 K AH0 N T
+INSILCO  IH2 N S IH1 L K OW0
+INSINCERE  IH2 N S IH0 N S IH1 R
+INSINUATE  IH2 N S IH1 N Y UW0 EY0 T
+INSINUATED  IH2 N S IH1 N Y UW0 EY0 T IH0 D
+INSINUATES  IH2 N S IH1 N Y UW0 EY0 T S
+INSINUATING  IH2 N S IH1 N Y UW0 EY0 T IH0 NG
+INSINUATION  IH2 N S IH2 N Y UW0 EY1 SH AH0 N
+INSINUATIONS  IH2 N S IH2 N Y UW0 EY1 SH AH0 N Z
+INSIPID  IH2 N S IH1 P AH0 D
+INSIST  IH2 N S IH1 S T
+INSISTED  IH2 N S IH1 S T AH0 D
+INSISTED(1)  IH2 N S IH1 S T IH0 D
+INSISTENCE  IH2 N S IH1 S T AH0 N S
+INSISTENT  IH2 N S IH1 S T AH0 N T
+INSISTENTLY  IH2 N S IH1 S T AH0 N T L IY0
+INSISTING  IH2 N S IH1 S T IH0 NG
+INSISTS  IH2 N S IH1 S T S
+INSITUFORM  IH2 N S IH1 T UW2 F AO0 R M
+INSKEEP  IH1 N Z K IY2 P
+INSKIP  IH1 N S K IH0 P
+INSKO  IH1 N S K OW0
+INSLAW  IH1 N S L AA0
+INSLEY  IH1 N S L IY0
+INSOFAR  IH1 N S AH0 F AA0 R
+INSOLENCE  IH1 N S AH0 L AH0 N S
+INSOLENT  IH1 N S AH0 L AH0 N T
+INSOLUBLE  IH2 N S AA1 L Y AH0 B AH0 L
+INSOLVENCIES  IH2 N S AA1 L V AH0 N S IY0 Z
+INSOLVENCY  IH2 N S AA1 L V AH0 N S IY0
+INSOLVENT  IH2 N S AA1 L V AH0 N T
+INSOMNIA  IH2 N S AA1 M N IY0 AH0
+INSOMNIAC  IH2 N S AA1 M N IY0 AE2 K
+INSOUCIANCE  IH2 N S UW1 S IY0 AH0 N S
+INSOUCIANCE(1)  IH2 N S UW1 S Y AH0 N S
+INSOUCIANT  IH2 N S UW1 S Y AH0 N T
+INSPECT  IH2 N S P EH1 K T
+INSPECTED  IH2 N S P EH1 K T IH0 D
+INSPECTING  IH2 N S P EH1 K T IH0 NG
+INSPECTION  IH2 N S P EH1 K SH AH0 N
+INSPECTIONS  IH2 N S P EH1 K SH AH0 N Z
+INSPECTOR  IH2 N S P EH1 K T ER0
+INSPECTOR'S  IH2 N S P EH1 K T ER0 Z
+INSPECTORATE  IH2 N S P EH1 K T ER0 AH0 T
+INSPECTORS  IH2 N S P EH1 K T ER0 Z
+INSPECTORS'  IH2 N S P EH1 K T ER0 Z
+INSPECTS  IH2 N S P EH1 K T S
+INSPEECH  IH2 N S P IY1 CH
+INSPIRATION  IH2 N S P ER0 EY1 SH AH0 N
+INSPIRATIONAL  IH2 N S P ER0 EY1 SH AH0 N AH0 L
+INSPIRATIONS  IH2 N S P ER0 EY1 SH AH0 N Z
+INSPIRE  IH2 N S P AY1 R
+INSPIRED  IH2 N S P AY1 ER0 D
+INSPIRES  IH2 N S P AY1 R Z
+INSPIRING  IH2 N S P AY1 R IH0 NG
+INSPIRING(1)  IH2 N S P AY1 ER0 IH0 NG
+INSTABILITIES  IH2 N S T AH0 B IH1 L IH0 T IY0 Z
+INSTABILITY  IH2 N S T AH0 B IH1 L IH0 T IY0
+INSTALL  IH2 N S T AO1 L
+INSTALLATION  IH2 N S T AH0 L EY1 SH AH0 N
+INSTALLATIONS  IH2 N S T AH0 L EY1 SH AH0 N Z
+INSTALLED  IH2 N S T AO1 L D
+INSTALLER  IH2 N S T AO1 L ER0
+INSTALLERS  IH2 N S T AO1 L ER0 Z
+INSTALLING  IH2 N S T AO1 L IH0 NG
+INSTALLMENT  IH2 N S T AO1 L M AH0 N T
+INSTALLMENTS  IH2 N S T AO1 L M AH0 N T S
+INSTALLS  IH2 N S T AO1 L Z
+INSTANCE  IH1 N S T AH0 N S
+INSTANCES  IH1 N S T AH0 N S AH0 Z
+INSTANCES(1)  IH1 N S T AH0 N S IH0 Z
+INSTANT  IH1 N S T AH0 N T
+INSTANTANEOUS  IH2 N S T AH0 N T AE1 N IY0 AH0 S
+INSTANTANEOUSLY  IH2 N S T AH0 N T AE1 N IY0 AH0 S L IY0
+INSTANTIATE  IH2 N S T AE1 N SH IY0 EY2 T
+INSTANTIATED  IH2 N S T AE1 N SH IY0 EY2 T AH0 D
+INSTANTIATES  IH2 N S T AE1 N SH IY0 EY2 T S
+INSTANTIATING  IH2 N S T AE1 N SH IY0 EY2 T IH0 NG
+INSTANTIATION  IH2 N S T AE2 N SH IY0 EY1 SH AH0 N
+INSTANTLY  IH1 N S T AH0 N T L IY0
+INSTEAD  IH2 N S T EH1 D
+INSTIGATE  IH1 N S T AH0 G EY2 T
+INSTIGATED  IH1 N S T AH0 G EY2 T IH0 D
+INSTIGATES  IH1 N S T AH0 G EY2 T S
+INSTIGATING  IH1 N S T AH0 G EY2 T IH0 NG
+INSTIGATION  IH2 N S T IH0 G EY1 SH AH0 N
+INSTIGATOR  IH1 N S T AH0 G EY2 T ER0
+INSTIGATORS  IH1 N S T AH0 G EY2 T ER0 Z
+INSTILL  IH2 N S T IH1 L
+INSTILLED  IH2 N S T IH1 L D
+INSTILLING  IH2 N S T IH1 L IH0 NG
+INSTILLS  IH2 N S T IH1 L Z
+INSTINCT  IH1 N S T IH0 NG K T
+INSTINCTIVE  IH2 N S T IH1 NG K T IH0 V
+INSTINCTIVELY  IH2 N S T IH1 NG K T IH0 V L IY2
+INSTINCTS  IH1 N S T IH0 NG K T S
+INSTINET  IH1 N S T IH0 N EH2 T
+INSTITUCIONAL  IH2 N S T IH0 T UW2 S IY0 AH0 N AE1 L
+INSTITUT  IH1 N S T IH0 T UW0 T
+INSTITUTE  IH1 N S T IH0 T UW2 T
+INSTITUTE'S  IH1 N S T IH0 T UW0 T S
+INSTITUTED  IH1 N S T IH0 T UW2 T IH0 D
+INSTITUTES  IH1 N S T IH0 T UW2 T S
+INSTITUTES'  IH1 N S T IH0 T UW2 T S
+INSTITUTING  IH1 N S T IH0 T UW2 T IH0 NG
+INSTITUTION  IH2 N S T IH0 T UW1 SH AH0 N
+INSTITUTION'S  IH2 N S T IH0 T UW1 SH AH0 N Z
+INSTITUTIONAL  IH2 N S T IH0 T UW1 SH AH0 N AH0 L
+INSTITUTIONALIST  IH2 N S T IH0 T UW1 SH AH0 N AH0 L IH0 S T
+INSTITUTIONALISTS  IH2 N S T IH0 T UW1 SH AH0 N AH0 L IH0 S T S
+INSTITUTIONALIZATION  IH2 N S T IH0 T UW2 SH AH0 N AH0 L IH0 Z EY1 SH AH0 N
+INSTITUTIONALIZE  IH2 N S T IH0 T UW1 SH AH0 N AH0 L AY2 Z
+INSTITUTIONALIZED  IH2 N S T IH0 T UW1 SH AH0 N AH0 L AY2 Z D
+INSTITUTIONALIZES  IH2 N S T IH0 T UW1 SH AH0 N AH0 L AY2 Z IH0 Z
+INSTITUTIONALIZING  IH2 N S T IH0 T UW1 SH AH0 N AH0 L AY2 Z IH0 NG
+INSTITUTIONALLY  IH2 N S T IH0 T UW1 SH AH0 N AH0 L IY2
+INSTITUTIONS  IH2 N S T IH0 T UW1 SH AH0 N Z
+INSTITUTIONS'  IH2 N S T IH0 T UW1 SH AH0 N Z
+INSTITUTO  IH2 N S T IH0 T UW1 T OW2
+INSTONE  IH1 N S T OW2 N
+INSTRUCT  IH2 N S T R AH1 K T
+INSTRUCTED  IH2 N S T R AH1 K T AH0 D
+INSTRUCTED(1)  IH2 N S T R AH1 K T IH0 D
+INSTRUCTING  IH2 N S T R AH1 K T IH0 NG
+INSTRUCTION  IH2 N S T R AH1 K SH AH0 N
+INSTRUCTIONAL  IH2 N S T R AH1 K SH AH0 N AH0 L
+INSTRUCTIONS  IH2 N S T R AH1 K SH AH0 N Z
+INSTRUCTIVE  IH2 N S T R AH1 K T IH0 V
+INSTRUCTOR  IH2 N S T R AH1 K T ER0
+INSTRUCTORS  IH2 N S T R AH1 K T ER0 Z
+INSTRUCTS  IH2 N S T R AH1 K T S
+INSTRUMENT  IH1 N S T R AH0 M AH0 N T
+INSTRUMENT'S  IH1 N S T R AH0 M AH0 N T S
+INSTRUMENTAL  IH2 N S T R AH0 M EH1 N T AH0 L
+INSTRUMENTAL(1)  IH2 N S T R AH0 M EH1 N AH0 L
+INSTRUMENTALIST  IH2 N S T R AH0 M EH1 N T AH0 L IH0 S T
+INSTRUMENTALIST(1)  IH2 N S T R AH0 M EH1 N AH0 L IH0 S T
+INSTRUMENTALISTS  IH2 N S T R AH0 M EH1 N T AH0 L IH0 S T S
+INSTRUMENTALISTS(1)  IH2 N S T R AH0 M EH1 N AH0 L IH0 S T S
+INSTRUMENTALISTS(2)  IH2 N S T R AH0 M EH1 N T AH0 L IH0 S
+INSTRUMENTALISTS(3)  IH2 N S T R AH0 M EH1 N AH0 L IH0 S
+INSTRUMENTALITY  IH2 N S T R AH0 M EH0 N T AE1 L IH0 T IY0
+INSTRUMENTALS  IH2 N S T R AH0 M EH1 N T AH0 L Z
+INSTRUMENTALS(1)  IH2 N S T R AH0 M EH1 N AH0 L Z
+INSTRUMENTATION  IH2 N S T R AH0 M EH2 N T EY1 SH AH0 N
+INSTRUMENTS  IH1 N S T R AH0 M AH0 N T S
+INSTRUMENTS'  IH1 N S T R AH0 M AH0 N T S
+INSUBORDINATE  IH2 N S AH0 B AO1 R D AH0 N EY2 T
+INSUBORDINATE(1)  IH2 N S AH0 B AO1 R D AH0 N AH0 T
+INSUBORDINATION  IH2 N S AH0 B AO2 R D AH0 N EY1 SH AH0 N
+INSUBSTANTIAL  IH2 N S AH0 B S T AE1 N SH AH0 L
+INSUBSTANTIATE  IH2 N S AH0 B S T AE1 N CH IY2 EY0 T
+INSUBSTANTIATE(1)  IH2 N S AH0 B S T AE1 N SH IY2 EY0 T
+INSUBSTANTIATED  IH2 N S AH0 B S T AE1 N CH IY2 EY0 T AH0 D
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+INSUFFERABLE  IH2 N S AH1 F ER0 AH0 B AH0 L
+INSUFFICIENCY  IH2 N S AH0 F IH1 SH AH0 N S IY2
+INSUFFICIENT  IH2 N S AH0 F IH1 SH AH0 N T
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+INSULAR  IH1 N S AH0 L ER0
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+INSULATE  IH1 N S AH0 L EY2 T
+INSULATED  IH1 N S AH0 L EY2 T AH0 D
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+INSULATING  IH1 N S AH0 L EY2 T IH0 NG
+INSULATION  IH2 N S AH0 L EY1 SH AH0 N
+INSULATOR  IH1 N S AH0 L EY2 T ER0
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+INSULT  IH2 N S AH1 L T
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+INSULTED  IH2 N S AH1 L T IH0 D
+INSULTING  IH2 N S AH1 L T IH0 NG
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+INSUPPORTABLE  IH2 N S AH0 P AO1 R T AH0 B AH0 L
+INSURANCE  IH2 N SH UH1 R AH0 N S
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+INSURANCES  IH2 N SH UH1 R AH0 N S IH0 Z
+INSURE  IH2 N SH UH1 R
+INSURED  IH2 N SH UH1 R D
+INSURER  IH2 N SH UH1 R ER0
+INSURER'S  IH2 N SH UH1 R ER0 Z
+INSURERS  IH2 N SH UH1 R ER0 Z
+INSURERS'  IH2 N SH UH1 R ER0 Z
+INSURES  IH2 N SH UH1 R Z
+INSURGENCIES  IH2 N S ER1 JH AH0 N S IY0 Z
+INSURGENCY  IH2 N S ER1 JH AH0 N S IY0
+INSURGENT  IH2 N S ER1 JH AH0 N T
+INSURGENTS  IH2 N S ER1 JH AH0 N T S
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+INSURING  IH2 N SH UH1 R IH0 NG
+INSURMOUNTABLE  IH2 N S ER0 M AW1 N T AH0 B AH0 L
+INSURRECTION  IH2 N S ER0 EH1 K SH AH0 N
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+INTAN  IH1 N T AH0 N
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+INTANGIBLY  IH2 N T AE1 N JH AH0 B L IY0
+INTEFADEH  IH2 N T AH0 F AA1 D AH0
+INTEFADEH(1)  IH2 N T IH0 F AA1 D AH0
+INTEGER  IH1 N T AH0 JH ER0
+INTEGERS  IH1 N T AH0 JH ER0 Z
+INTEGON  IH1 N T AH0 G AA0 N
+INTEGRA  IH2 N T EH1 G R AH0
+INTEGRAL  IH1 N T AH0 G R AH0 L
+INTEGRAL(1)  IH2 N T EH1 G R AH0 L
+INTEGRALS  IH1 N T AH0 G R AH0 L Z
+INTEGRATE  IH1 N T AH0 G R EY2 T
+INTEGRATE(1)  IH1 N AH0 G R EY2 T
+INTEGRATED  IH1 N T AH0 G R EY2 T AH0 D
+INTEGRATED(1)  IH1 N T AH0 G R EY2 T IH0 D
+INTEGRATED(2)  IH1 N AH0 G R EY2 T AH0 D
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+INTEGRATES(1)  IH1 N AH0 G R EY2 T S
+INTEGRATING  IH1 N T AH0 G R EY2 T IH0 NG
+INTEGRATING(1)  IH1 N AH0 G R EY2 T IH0 NG
+INTEGRATION  IH2 N T AH0 G R EY1 SH AH0 N
+INTEGRATION(1)  IH2 N AH0 G R EY1 SH AH0 N
+INTEGRATIONS  IH2 N T AH0 G R EY1 SH AH0 N Z
+INTEGRATIONS(1)  IH2 N AH0 G R EY1 SH AH0 N Z
+INTEGRATOR  IH1 N T AH0 G R EY2 T ER0
+INTEGRATOR(1)  IH1 N AH0 G R EY2 T ER0
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+INTEGRATORS(1)  IH1 N AH0 G R EY2 T ER0 Z
+INTEGRELIN  IH2 N T AH0 G R EH1 L AH0 N
+INTEGRELIN(1)  IH2 N T EH1 G R AH0 L IH0 N
+INTEGRITY  IH2 N T EH1 G R AH0 T IY0
+INTEGUMENT  IH2 N T EH1 G Y AH0 M AH0 N T
+INTEL  IH2 N T EH1 L
+INTEL'S  IH2 N T EH1 L Z
+INTELCO  IH2 N T EH1 L K OW0
+INTELCOM  IH2 N T EH1 L K AA0 M
+INTELLECT  IH1 N T AH0 L EH2 K T
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+INTELLECTS  IH1 N T AH0 L EH2 K T S
+INTELLECTS(1)  IH1 N AH0 L EH2 K T S
+INTELLECTS(2)  IH1 N T AH0 L EH2 K S
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+INTELLECTUALLY(2)  IH2 N AH0 L EH1 K CH UW0 AH0 L IY0
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+INTELLICALL  IH2 N T EH1 L IH0 K AO2 L
+INTELLICORP  IH2 N T EH1 L IH0 K AO2 R P
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+INTEND  IH2 N T EH1 N D
+INTENDED  IH2 N T EH1 N D IH0 D
+INTENDING  IH2 N T EH1 N D IH0 NG
+INTENDS  IH2 N T EH1 N D Z
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+INTENSELY  IH2 N T EH1 N S L IY0
+INTENSIFICATION  IH2 N T EH2 N S IH0 F AH0 K EY1 SH AH0 N
+INTENSIFIED  IH2 N T EH1 N S IH0 F AY2 D
+INTENSIFIES  IH2 N T EH1 N S IH0 F AY2 Z
+INTENSIFY  IH2 N T EH1 N S IH0 F AY2
+INTENSIFYING  IH2 N T EH1 N S IH0 F AY2 IH0 NG
+INTENSITIES  IH2 N T EH1 N S IH0 T IY0 Z
+INTENSITY  IH2 N T EH1 N S IH0 T IY0
+INTENSIVE  IH2 N T EH1 N S IH0 V
+INTENSIVELY  IH2 N T EH1 N S IH0 V L IY0
+INTENT  IH2 N T EH1 N T
+INTENTION  IH2 N T EH1 N CH AH0 N
+INTENTIONAL  IH2 N T EH1 N SH AH0 N AH0 L
+INTENTIONALLY  IH2 N T EH1 N SH AH0 N AH0 L IY0
+INTENTIONED  IH2 N T EH1 N CH AH0 N D
+INTENTIONS  IH2 N T EH1 N CH AH0 N Z
+INTENTLY  IH2 N T EH1 N T L IY0
+INTENTS  IH2 N T EH1 N T S
+INTER  IH2 N T ER1
+INTERACCIONES  IH2 N T ER0 AE2 K S IY0 OW1 N Z
+INTERACT  IH2 N T ER0 AE1 K T
+INTERACT(1)  IH2 N ER0 AE1 K T
+INTERACTED  IH2 N T ER0 AE1 K T AH0 D
+INTERACTED(1)  IH2 N ER0 AE1 K T AH0 D
+INTERACTING  IH2 N T ER0 AE1 K T IH0 NG
+INTERACTING(1)  IH2 N ER0 AE1 K T IH0 NG
+INTERACTION  IH2 N T ER0 AE1 K SH AH0 N
+INTERACTION(1)  IH2 N ER0 AE1 K SH AH0 N
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+INTERACTIONS(1)  IH2 N ER0 AE1 K SH AH0 N Z
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+INTERACTIVE(1)  IH2 N ER0 AE1 K T IH0 V
+INTERACTIVITY  IH2 N T ER0 AE2 K T IH1 V IH0 T IY0
+INTERACTIVITY(1)  IH2 N ER0 AE2 K T IH1 V IH0 T IY0
+INTERACTS  IH2 N T ER0 AE1 K T S
+INTERACTS(1)  IH2 N ER0 AE1 K T S
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+INTERBANK  IH2 N T ER0 B AE1 NG K
+INTERBRED  IH2 N T ER0 B R EH1 D
+INTERBREW  IH1 N T ER2 B R UW2
+INTERBREW(1)  IH1 N T ER0 B R UW2
+INTERBREW'S  IH1 N T ER2 B R UW2 Z
+INTERBREW'S(1)  IH1 N T ER0 B R UW2 Z
+INTERCABLE  IH2 N T ER0 K EY1 B AH0 L
+INTERCAPITAL  IH2 N T ER0 K AE1 P AH0 T AH0 L
+INTERCARE  IH1 N T ER0 K EH2 R
+INTERCEDE  IH2 N T ER0 S IY1 D
+INTERCEDED  IH2 N T ER0 S IY1 D IH0 D
+INTERCEDING  IH2 N T ER0 S IY1 D IH0 NG
+INTERCELLULAR  IH2 N T ER0 S EH1 L Y AH0 L ER0
+INTERCEPT  IH2 N T ER0 S EH1 P T
+INTERCEPT(1)  IH2 N ER0 S EH1 P T
+INTERCEPTED  IH2 N T ER0 S EH1 P T AH0 D
+INTERCEPTED(1)  IH2 N T ER0 S EH1 P T IH0 D
+INTERCEPTED(2)  IH2 N ER0 S EH1 P T AH0 D
+INTERCEPTED(3)  IH2 N ER0 S EH1 P T IH0 D
+INTERCEPTING  IH2 N T ER0 S EH1 P T IH0 NG
+INTERCEPTING(1)  IH2 N ER0 S EH1 P T IH0 NG
+INTERCEPTION  IH2 N T ER0 S EH1 P SH AH0 N
+INTERCEPTION(1)  IH2 N ER0 S EH1 P SH AH0 N
+INTERCEPTIONS  IH2 N T ER0 S EH1 P SH AH0 N Z
+INTERCEPTIONS(1)  IH2 N ER0 S EH1 P SH AH0 N Z
+INTERCEPTOR  IH2 N T ER0 S EH1 P T ER0
+INTERCEPTOR(1)  IH2 N ER0 S EH1 P T ER0
+INTERCEPTORS  IH2 N T ER0 S EH1 P T ER0 Z
+INTERCEPTORS(1)  IH2 N ER0 S EH1 P T ER0 Z
+INTERCEPTS  IH2 N T ER0 S EH1 P T S
+INTERCEPTS(1)  IH2 N ER0 S EH1 P T S
+INTERCESSION  IH2 N T ER0 S EH1 SH AH0 N
+INTERCESSION(1)  IH2 N ER0 S EH1 SH AH0 N
+INTERCHANGE  IH2 N T ER0 CH EY1 N JH
+INTERCHANGE(1)  IH2 N ER0 CH EY1 N JH
+INTERCHANGEABLE  IH2 N T ER0 CH EY1 N JH AH0 B AH0 L
+INTERCHANGEABLE(1)  IH2 N ER0 CH EY1 N JH AH0 B AH0 L
+INTERCHANGEABLY  IH2 N T ER0 CH EY1 N JH AH0 B L IY0
+INTERCHANGEABLY(1)  IH2 N ER0 CH EY1 N JH AH0 B L IY0
+INTERCHANGES  IH1 N T ER0 CH EY2 N JH IH0 Z
+INTERCHANGES(1)  IH1 N ER0 CH EY2 N JH IH0 Z
+INTERCITY  IH1 N T ER0 S IH2 T IY0
+INTERCITY(1)  IH1 N ER0 S IH2 T IY0
+INTERCO  IH1 N T ER0 K OW2
+INTERCO'S  IH1 N T ER0 K OW2 Z
+INTERCOLLEGIATE  IH2 N T ER0 K AH0 L IY1 JH AH0 T
+INTERCOM  IH1 N T ER0 K AA2 M
+INTERCOMPANY  IH2 N T ER0 K AH1 M P AH0 N IY0
+INTERCONNECT  IH2 N T ER0 K AH0 N EH1 K T
+INTERCONNECTED  IH2 N T ER0 K AH0 N EH1 K T IH0 D
+INTERCONNECTION  IH2 N T ER0 K AH0 N EH1 K SH AH0 N
+INTERCONNECTIONS  IH2 N T ER0 K AH0 N EH1 K SH AH0 N Z
+INTERCONTINENTAL  IH2 N T ER0 K AA2 N T AH0 N EH1 N T AH0 L
+INTERCONTINENTALE  IH2 N T ER0 K AA0 N T IH1 N AH0 N T AA2 L
+INTERCORP  IH1 N T ER0 K AO2 R P
+INTERCORPORATION  IH2 N T ER0 K AO2 R P ER0 EY1 SH AH0 N
+INTERCOURSE  IH1 N T ER0 K AO2 R S
+INTERCOURSE(1)  IH1 N ER0 K AO2 R S
+INTERCULTURAL  IH2 N T ER0 K AH1 L CH ER0 AH0 L
+INTERCURRENT  IH2 N T ER0 K ER1 AH0 N T
+INTERDEALER  IH1 N T ER0 D IY2 L ER0
+INTERDEPENDENCE  IH2 N T ER0 D AH0 P EH1 N D AH0 N S
+INTERDEPENDENT  IH2 N T ER0 D IH0 P EH1 N D AH0 N T
+INTERDICT  IH1 N T ER0 D IH2 K T
+INTERDICTED  IH1 N T ER0 D IH2 K T IH0 D
+INTERDICTES  IH1 N T ER0 D IH2 K T S
+INTERDICTING  IH2 N T ER0 D IH1 K T IH0 NG
+INTERDICTION  IH2 N T ER0 D IH1 K SH AH0 N
+INTERDIGITAL  IH2 N T ER0 D IH1 JH AH0 T AH0 L
+INTERDISCIPLINARY  IH2 N T ER0 D IH1 S AH0 P L AH0 N EH2 R IY0
+INTERDYNE  IH1 N T ER0 D AY2 N
+INTEREST  IH1 N T AH0 R EH2 S T
+INTERESTED  IH1 N T AH0 R EH2 S T IH0 D
+INTERESTING  IH1 N T AH0 R EH2 S T IH0 NG
+INTERESTINGLY  IH1 N T AH0 R EH2 S T IH0 NG L IY0
+INTERESTRATE  IH1 N T ER0 S T R EY2 T
+INTERESTS  IH1 N T R AH0 S T S
+INTERESTS(1)  IH1 N T R IH0 S T S
+INTERESTS(2)  IH1 N T ER0 AH0 S T S
+INTERESTS(3)  IH1 N T ER0 IH0 S T S
+INTERFACE  IH1 N T ER0 F EY2 S
+INTERFACE(1)  IH1 N ER0 F EY2 S
+INTERFACES  IH1 N T ER0 F EY2 S IH0 Z
+INTERFACES(1)  IH1 N ER0 F EY2 S IH0 Z
+INTERFAITH  IH2 N T ER0 F EY1 TH
+INTERFAX  IH1 N T ER0 F AE2 K S
+INTERFERE  IH2 N T ER0 F IH1 R
+INTERFERE(1)  IH2 N ER0 F IH1 R
+INTERFERED  IH2 N T ER0 F IH1 R D
+INTERFERED(1)  IH2 N ER0 F IH1 R D
+INTERFERENCE  IH2 N T ER0 F IH1 R AH0 N S
+INTERFERENCE(1)  IH2 N ER0 F IH1 R AH0 N S
+INTERFERES  IH2 N T ER0 F IH1 R Z
+INTERFERES(1)  IH2 N ER0 F IH1 R Z
+INTERFERING  IH2 N T ER0 F IH1 R IH0 NG
+INTERFERING(1)  IH2 N ER0 F IH1 R IH0 NG
+INTERFEROMETER  IH2 N T ER0 F ER0 AA1 M AH0 T ER0
+INTERFERON  IH2 N T ER0 F EH1 R AA0 N
+INTERFERON(1)  IH2 N T ER0 F IH1 R AA0 N
+INTERFERONS  IH2 N T ER0 F IH1 R AA0 N Z
+INTERFIRST  IH2 N T ER0 F ER1 S T
+INTERFLUG  IH1 N T ER0 F L AH0 G
+INTERFUNDING  IH1 N T ER0 F AH2 N D IH0 NG
+INTERGENERATIONAL  IH2 N T ER0 JH EH1 N ER0 EY1 SH AH0 N AH0 L
+INTERGOVERNMENTAL  IH2 N T ER0 G AH2 V ER0 N M EH1 N T AH0 L
+INTERGRAPH  IH1 N T ER0 G R AE2 F
+INTERGRAPH'S  IH1 N T ER0 G R AE2 F S
+INTERGROUP  IH2 N T ER0 G R UW1 P
+INTERGROUP'S  IH2 N T ER0 G R UW1 P S
+INTERIM  IH1 N T ER0 AH0 M
+INTERIM(1)  IH1 N T R AH0 M
+INTERIM(2)  IH1 N ER0 AH0 M
+INTERIOR  IH2 N T IH1 R IY0 ER0
+INTERIORS  IH2 N T IH1 R IY0 ER0 Z
+INTERJECT  IH2 N T ER0 JH EH1 K T
+INTERJECTED  IH2 N T ER0 JH EH1 K T IH0 D
+INTERJECTION  IH2 N T ER0 JH EH1 K SH AH0 N
+INTERJECTIONS  IH2 N T ER0 JH EH1 K SH AH0 N Z
+INTERJECTS  IH2 N T ER0 JH EH1 K T S
+INTERLACE  IH2 N T ER0 L EY1 S
+INTERLACED  IH2 N T ER0 L EY1 S T
+INTERLAKE  IH1 N T ER0 L EY2 K
+INTERLAKE'S  IH1 N T ER0 L EY2 K S
+INTERLEAF  IH1 N T ER0 L IY2 F
+INTERLEAVE  IH2 N T ER0 L IY1 V
+INTERLEAVED  IH2 N T ER0 L IY1 V D
+INTERLEAVES  IH2 N T ER0 L IY1 V Z
+INTERLEAVING  IH2 N T ER0 L IY1 V IH0 NG
+INTERLEUKIN  IH2 N T ER0 L UW1 K IH0 N
+INTERLINK  IH2 N T ER0 L IH1 NG K
+INTERLINKED  IH2 N T ER0 L IH1 NG K T
+INTERLOCK  IH2 N T ER0 L AA1 K
+INTERLOCKED  IH2 N T ER0 L AA1 K D
+INTERLOCKING  IH1 N T ER0 L AA2 K IH0 NG
+INTERLOCUTOR  IH2 N T ER0 L AA1 K Y AH0 T ER0
+INTERLOCUTORS  IH2 N T ER0 L AA1 K Y AH0 T ER0 Z
+INTERLOPER  IH1 N T ER0 L OW2 P ER0
+INTERLOPERS  IH1 N T ER0 L OW2 P ER0 Z
+INTERLUDE  IH1 N T ER0 L UW2 D
+INTERLUDES  IH1 N T ER0 L UW2 D Z
+INTERMAGNETIC  IH2 N T ER0 M AE0 G N EH1 T IH0 K
+INTERMAGNETICS  IH2 N T ER0 M AE0 G N EH1 T IH0 K S
+INTERMARK  IH2 N T ER0 M AA1 R K
+INTERMARKET  IH2 N T ER0 M AA1 R K IH0 T
+INTERMARRIAGE  IH2 N T ER0 M EH1 R IH0 JH
+INTERMARRIED  IH2 N T ER0 M AE1 R IY0 D
+INTERMARRY  IH2 N T ER0 M AE1 R IY0
+INTERMEC  IH1 N T ER0 M AH0 K
+INTERMEDIA  IH2 N T ER0 M IY1 D IY0 AH0
+INTERMEDIARIES  IH2 N T ER0 M IY1 D IY0 EH2 R IY0 Z
+INTERMEDIARIES(1)  IH2 N ER0 M IY1 D IY0 EH2 R IY0 Z
+INTERMEDIARY  IH2 N ER0 M IY1 D IY0 EH0 R IY0
+INTERMEDIARY(1)  IH2 N T ER0 M IY1 D IY0 EH0 R IY0
+INTERMEDIATE  IH2 N T ER0 M IY1 D IY0 IH0 T
+INTERMEDIATE(1)  IH2 N ER0 M IY1 D IY0 IH0 T
+INTERMEDIATES  IH2 N T ER0 M IY1 D IY0 AH0 T S
+INTERMEDIATES(1)  IH2 N T ER0 M IY1 D IY0 EY0 T S
+INTERMEDIATES(2)  IH2 N ER0 M IY1 D IY0 AH0 T S
+INTERMEDIATES(3)  IH2 N ER0 M IY1 D IY0 EY0 T S
+INTERMEDIC  IH1 N T ER0 M EH2 D IH0 K
+INTERMEDICS  IH1 N T ER0 M EH2 D IH0 K S
+INTERMET  IH2 N T ER0 M EH1 T
+INTERMINABLE  IH2 N T ER1 M AH0 N AH0 B AH0 L
+INTERMINABLY  IH1 N T ER0 M IH0 N AE2 B L IY0
+INTERMINABLY(1)  IH2 N T ER1 M IH0 N AH0 B L IY0
+INTERMINGLE  IH2 N T ER0 M IH1 NG G AH0 L
+INTERMINGLED  IH2 N T ER0 M IH1 NG G AH0 L D
+INTERMINGLING  IH2 N T ER0 M IH1 NG G AH0 L IH0 NG
+INTERMINGLING(1)  IH2 N T ER0 M IH1 NG G L IH0 NG
+INTERMISSION  IH2 N T ER0 M IH1 SH AH0 N
+INTERMISSIONS  IH2 N T ER0 M IH1 SH AH0 N Z
+INTERMITTENT  IH2 N T ER0 M IH1 T AH0 N T
+INTERMITTENTLY  IH2 N T ER0 M IH1 T AH0 N T L IY0
+INTERMIX  IH2 N T ER0 M IH1 K S
+INTERMIXED  IH2 N T ER0 M IH1 K S T
+INTERMIXING  IH1 N T ER0 M IH1 K S IH0 NG
+INTERMODAL  IH2 N T ER0 M OW1 D AH0 L
+INTERMOLECULAR  IH2 N T ER0 M AH0 L EH1 K Y AH0 L ER0
+INTERMOUNTAIN  IH2 N T ER0 M AW1 N T IH0 N
+INTERN  IH1 N T ER0 N
+INTERNACIONAL  IH2 N T ER0 N AE1 SH AH0 N AA2 L
+INTERNACIONAL(1)  IH2 N T ER0 N AA2 S Y OW0 N AA1 L
+INTERNAL  IH2 N T ER1 N AH0 L
+INTERNALIZE  IH2 N T ER1 N AH0 L AY2 Z
+INTERNALIZED  IH2 N T ER1 N AH0 L AY2 Z D
+INTERNALLY  IH2 N T ER1 N AH0 L IY0
+INTERNATIONAL  IH2 N T ER0 N AE1 SH AH0 N AA0 L
+INTERNATIONAL(1)  IH2 N ER0 N AE1 SH AH0 N AA0 L
+INTERNATIONAL'S  IH2 N T ER0 N AE1 SH AH0 N AA0 L Z
+INTERNATIONAL'S(1)  IH2 N ER0 N AE1 SH AH0 N AA0 L Z
+INTERNATIONALE  IH2 N T ER0 N AE2 SH AH0 N AA1 L
+INTERNATIONALE(1)  IH2 N ER0 N AE2 SH AH0 N AA1 L IY0
+INTERNATIONALISM  IH2 N T ER0 N AE1 SH AH0 N AA0 L IH2 Z AH0 M
+INTERNATIONALISM(1)  IH2 N ER0 N AE1 SH AH0 N AA0 L IH2 Z AH0 M
+INTERNATIONALIST  IH2 N T ER0 N AE1 SH AH0 N AH0 L IH0 S T
+INTERNATIONALIST(1)  IH2 N ER0 N AE1 SH AH0 N AH0 L IH0 S T
+INTERNATIONALISTS  IH2 N T ER0 N AE1 SH AH0 N AA0 L IH0 S T S
+INTERNATIONALIZATION  IH2 N T ER0 N AE2 SH AH0 N AA0 L IH0 Z EY1 SH AH0 N
+INTERNATIONALIZATION(1)  IH2 N ER0 N AE2 SH AH0 N AA0 L IH0 Z EY1 SH AH0 N
+INTERNATIONALIZE  IH2 N T ER0 N AE1 SH AH0 N AH0 L AY2 Z
+INTERNATIONALIZE(1)  IH2 N ER0 N AE1 SH AH0 N AA1 L AY2 Z
+INTERNATIONALIZE(2)  IH2 N ER0 N AE1 SH N AA0 L AY2 Z
+INTERNATIONALIZED  IH2 N T ER0 N AE1 SH AH0 N AA0 L AY0 Z D
+INTERNATIONALIZED(1)  IH2 N ER0 N AE1 SH AH0 N AA0 L AY0 Z D
+INTERNATIONALIZED(2)  IH2 N ER0 N AE1 SH N AA0 L AY0 Z D
+INTERNATIONALLY  IH2 N T ER0 N AE1 SH AH0 N AA0 L IY0
+INTERNATIONALLY(1)  IH2 N T ER0 N AE1 SH N AA0 L IY0
+INTERNATIONALLY(2)  IH2 N ER0 N AE1 SH AH0 N AA0 L IY0
+INTERNATIONALLY(3)  IH2 N ER0 N AE1 SH N AA0 L IY0
+INTERNATIONALS  IH2 N T ER0 N AE1 SH AH0 N AH0 L Z
+INTERNATONAL  IH2 N T ER0 N AE1 SH AH0 N AH0 L
+INTERNATONAL(1)  IH2 N ER0 N AE1 SH AH0 N AH0 L
+INTERNECINE  IH2 N T ER1 N AH0 S IY2 N
+INTERNED  IH1 N T ER2 N D
+INTERNEE  IH2 N T ER0 N IY1
+INTERNEES  IH2 N T ER0 N IY1 Z
+INTERNET  IH1 N T ER0 N EH2 T
+INTERNET'S  IH1 N T ER0 N EH2 T S
+INTERNIST  IH2 N T ER1 N IH0 S T
+INTERNISTS  IH2 N T ER1 N IH0 S T S
+INTERNMENT  IH2 N T ER1 N M AH0 N T
+INTERNORTH  IH1 N T ER0 N AO0 R TH
+INTERNS  IH1 N T ER0 N Z
+INTERNSHIP  IH1 N T ER0 N SH IH2 P
+INTERNSHIPS  IH1 N T ER0 N SH IH2 P S
+INTEROFFICE  IH2 N T ER0 AO1 F AH0 S
+INTEROPERATE  IH2 N T ER0 AO1 P ER0 AH0 T
+INTEROPERATES  IH2 N T ER0 AO1 P ER0 AH0 T S
+INTERPART  IH1 N T ER0 P AA2 R T
+INTERPERSONAL  IH2 N T ER0 P ER1 S AH0 N AH0 L
+INTERPLANETARY  IH2 N T ER0 P L AE1 N AH0 T EH2 R IY0
+INTERPLAY  IH1 N T ER0 P L EY2
+INTERPOL  IH1 N T ER0 P OW2 L
+INTERPOLATE  IH2 T ER1 P AH0 L EY2 T
+INTERPOLATED  IH2 T ER1 P AH0 L EY2 T IH0 D
+INTERPOLATION  IH2 T ER1 P AH0 L EY2 SH AH0 N
+INTERPOLATIONS  IH2 T ER1 P AH0 L EY2 SH AH0 N Z
+INTERPOSE  IH2 N T ER0 P OW1 Z
+INTERPRET  IH2 N T ER1 P R AH0 T
+INTERPRETATION  IH2 N T ER2 P R IH0 T EY1 SH AH0 N
+INTERPRETATIONS  IH2 N T ER2 P R IH0 T EY1 SH AH0 N Z
+INTERPRETED  IH2 N T ER1 P R AH0 T AH0 D
+INTERPRETER  IH2 N T ER1 P R AH0 T ER0
+INTERPRETERS  IH2 N T ER1 P R AH0 T ER0 Z
+INTERPRETING  IH1 N T ER0 P R EH2 T IH0 NG
+INTERPRETIVE  IH2 N T ER1 P R AH0 T IH0 V
+INTERPRETS  IH2 N T ER1 P R AH0 T S
+INTERPROVINCIAL  IH2 N T ER0 P R OW0 V IH1 N SH AH0 L
+INTERPUBLIC  IH2 N T ER0 P AH1 B L IH0 K
+INTERPUBLIC'S  IH2 N T ER0 P AH1 B L IH0 K S
+INTERRACIAL  IH2 N T ER0 R EY1 SH AH0 L
+INTERRANTE  IH2 N T ER0 R AA1 N T IY0
+INTERRED  IH2 N T ER1 D
+INTERREGNUM  IH2 N T ER0 R EH1 G N AH0 M
+INTERRELATE  IH2 N T ER0 R IH0 L EY1 T
+INTERRELATED  IH2 N T ER0 R IH0 L EY1 T IH0 D
+INTERRELATIONSHIP  IH1 N T ER0 R IY0 L EY1 SH AH0 N SH IH0 P
+INTERRENT  IH1 N T ER0 EH2 N T
+INTERROGATE  IH2 N T EH1 R AH0 G EY2 T
+INTERROGATED  IH2 N T EH1 R AH0 G EY2 T IH0 D
+INTERROGATING  IH2 N T EH1 R AH0 G EY2 T IH0 NG
+INTERROGATION  IH2 N T EH2 R AH0 G EY1 SH AH0 N
+INTERROGATIONS  IH2 N T EH2 R AH0 G EY1 SH AH0 N Z
+INTERROGATOR  IH2 N T EH1 R AH0 G EY2 T ER0
+INTERROGATORIES  IH2 N T EH0 R AA1 G AH0 T AO2 R IY0 Z
+INTERROGATORS  IH2 N T EH1 R AH0 G EY2 T ER0 Z
+INTERROGATORY  IH2 N T EH0 R AA1 G AH0 T AO2 R IY0
+INTERRUPT  IH2 N T ER0 AH1 P T
+INTERRUPTED  IH2 N T ER0 AH1 P T IH0 D
+INTERRUPTIBLE  IH2 N T ER0 R AH1 P T IH0 B AH0 L
+INTERRUPTING  IH2 N T ER0 AH1 P T IH0 NG
+INTERRUPTION  IH2 N T ER0 AH1 P SH AH0 N
+INTERRUPTIONS  IH2 N T ER0 AH1 P SH AH0 N Z
+INTERRUPTS  IH2 N T ER0 AH1 P T S
+INTERSCHOLASTIC  IH2 N T ER0 S K AH0 L AE1 S T IH0 K
+INTERSCOPE  IH1 N T ER0 S K OW2 P
+INTERSEC  IH1 N T ER0 S EH0 K
+INTERSECT  IH2 N T ER0 S EH1 K T
+INTERSECTED  IH2 N T ER0 S EH1 K T IH0 D
+INTERSECTING  IH2 N T ER0 S EH1 K T IH0 NG
+INTERSECTION  IH2 N T ER0 S EH1 K SH AH0 N
+INTERSECTIONS  IH2 N T ER0 S EH1 K SH AH0 N Z
+INTERSECTS  IH2 N T ER0 S EH1 K T S
+INTERSEGMENT  IH2 N T ER0 S EH1 G M AH0 N T
+INTERSPEC  IH1 N T ER0 S P EH0 K
+INTERSPERSE  IH2 N T ER0 S P ER1 S
+INTERSPERSED  IH2 N T ER0 S P ER1 S T
+INTERSPERSES  IH2 N T ER0 S P ER1 S AH0 Z
+INTERSTATE  IH2 N T ER0 S T EY1 T
+INTERSTATE'S  IH2 N T ER0 S T EY1 T S
+INTERSTATES  IH1 N T ER0 S T EY2 T S
+INTERSTELLAR  IH2 N T ER0 S T EH1 L ER0
+INTERSTITIAL  IH2 N T ER0 S T IH1 SH AH0 L
+INTERTAN  IH2 N T ER0 T AE1 N
+INTERTECH  IH1 N T ER0 T EH2 K
+INTERTECHNOLOGY  IH2 N T ER0 T AH0 K N AA1 L AH0 JH IY0
+INTERTECT  IH1 N T ER0 T EH2 K T
+INTERTIDAL  IH2 N T ER0 T AY1 D AH0 L
+INTERTRANS  IH1 N T ER0 T R AE2 N Z
+INTERTRIBAL  IH2 N T ER0 T R AY1 B AH0 L
+INTERTWINE  IH2 N T ER0 T W AY1 N
+INTERTWINED  IH2 N T ER0 T W AY1 N D
+INTERTWINING  IH1 N T ER0 T W AY2 N IH0 NG
+INTERURBAN  IH2 N T ER0 ER1 B AH0 N
+INTERVAL  IH1 N T ER0 V AH0 L
+INTERVALS  IH1 N T ER0 V AH0 L Z
+INTERVENE  IH2 N T ER0 V IY1 N
+INTERVENED  IH2 N T ER0 V IY1 N D
+INTERVENES  IH2 N T ER0 V IY1 N Z
+INTERVENING  IH2 N T ER0 V IY1 N IH0 NG
+INTERVENOR  IH2 N T ER0 V IY1 N ER0
+INTERVENORS  IH2 N T ER0 V IY1 N ER0 Z
+INTERVENTION  IH2 N T ER0 V EH1 N SH AH0 N
+INTERVENTIONISM  IH2 N T ER0 V EH1 N SH AH0 N IH2 Z AH0 M
+INTERVENTIONIST  IH2 N T ER0 V EH1 N SH AH0 N IH0 S T
+INTERVENTIONISTS  IH2 N T ER0 V EH1 N SH AH0 N IH0 S T S
+INTERVENTIONS  IH2 N T ER0 V EH1 N SH AH0 N Z
+INTERVIEW  IH1 N T ER0 V Y UW2
+INTERVIEWED  IH1 N T ER0 V Y UW2 D
+INTERVIEWEE  IH2 N T ER0 V Y UW0 IY1
+INTERVIEWEES  IH2 N T ER0 V Y UW0 IY1 Z
+INTERVIEWER  IH1 N T ER0 V Y UW2 ER0
+INTERVIEWERS  IH1 N T ER0 V Y UW2 ER0 Z
+INTERVIEWING  IH1 N T ER0 V Y UW2 IH0 NG
+INTERVIEWS  IH1 N T ER0 V Y UW2 Z
+INTERVOICE  IH2 N T ER0 V OY1 S
+INTERWEAVE  IH2 N T ER0 W IY1 V
+INTERWEAVING  IH2 N T ER0 W IY1 V IH0 NG
+INTERWOVEN  IH2 N T ER0 W OW1 V AH0 N
+INTESTINAL  IH2 N T EH1 S T IH0 N AH0 L
+INTESTINE  IH2 N T EH1 S T AH0 N
+INTESTINE(1)  IH2 N T EH1 S T AY2 N
+INTESTINES  IH2 N T EH1 S T AH0 N Z
+INTEX  IH1 N T EH2 K S
+INTI  IH1 N T IY0
+INTIFADA  IH2 N T IH0 F AA1 D AH0
+INTIMACY  IH1 N T AH0 M AH0 S IY0
+INTIMATE  IH1 N T AH0 M AH0 T
+INTIMATE(1)  IH1 N T IH0 M EY2 T
+INTIMATE(2)  IH1 N IH0 M AH0 T
+INTIMATED  IH1 N T IH0 M EY2 T IH0 D
+INTIMATELY  IH1 N T IH0 M AH0 T L IY0
+INTIMATES  IH1 N T IH0 M AH0 T S
+INTIMATION  IH2 N T IH0 M EY1 SH AH0 N
+INTIMATIONS  IH2 N T IH0 M EY1 SH AH0 N Z
+INTIMIDATE  IH2 N T IH1 M IH0 D EY2 T
+INTIMIDATED  IH2 N T IH1 M IH0 D EY2 T IH0 D
+INTIMIDATES  IH2 N T IH1 M IH0 D EY2 T S
+INTIMIDATING  IH2 N T IH1 M IH0 D EY2 T IH0 NG
+INTIMIDATION  IH2 N T IH2 M IH0 D EY1 SH AH0 N
+INTIS  IH1 N T IH0 S
+INTO  IH1 N T UW0
+INTO(1)  IH0 N T UW1
+INTO(2)  IH0 N T AH0
+INTOLERABLE  IH2 N T AA1 L ER0 AH0 B AH0 L
+INTOLERABLY  IH2 N T AA1 L ER0 AH0 B L IY0
+INTOLERANCE  IH2 N T AA1 L ER0 AH0 N S
+INTOLERANT  IH2 N T AA1 L ER0 AH0 N T
+INTONATION  IH2 N T AH0 N EY1 SH AH0 N
+INTONATIONS  IH2 N T AH0 N EY1 SH AH0 N Z
+INTONE  IH2 N T OW1 N
+INTONED  IH2 N T OW1 N D
+INTONES  IH2 N T OW1 N Z
+INTONING  IH2 N T OW1 N IH0 NG
+INTOURIST  IH2 N T UH1 R IH0 S T
+INTOXICATE  IH2 N T AA1 K S AH0 K EY2 T
+INTOXICATED  IH2 N T AA1 K S IH0 K EY2 T IH0 D
+INTOXICATES  IH2 N T AA1 K S AH0 K EY2 T S
+INTOXICATING  IH2 N T AA1 K S IH0 K EY2 T IH0 NG
+INTOXICATION  IH2 N T AA2 K S AH0 K EY1 SH AH0 N
+INTRA  IH1 N T R AA0
+INTRACELLULAR  IH2 N T R AA0 S EH1 L Y AH0 L ER0
+INTRACOMPANY  IH2 N T R AA0 K AA1 M P AH0 N IY0
+INTRACRANIAL  IH2 N T R AA0 K R EY1 N IY0 AH0 L
+INTRACTABLE  IH2 N T R AE1 K T AH0 B AH0 L
+INTRADAY  IH2 N T R AA0 D EY1
+INTRAMARGINAL  IH2 N T R AA0 M AA1 R JH IH0 N AH0 L
+INTRAMURAL  IH2 N T R AA0 M Y UH1 R AH0 L
+INTRANSIGENCE  IH2 N T R AE1 N S AH0 JH AH0 N S
+INTRANSIGENCE(1)  IH2 N T R AE1 N S IH0 JH AH0 N S
+INTRANSIGENT  IH2 N T R AE1 N S AH0 JH AH0 N T
+INTRANSIGENT(1)  IH2 N T R AE1 N Z AH0 JH AH0 N T
+INTRAOCULAR  IH2 N T R AA0 OW1 K Y UW0 L ER0
+INTRAPARTY  IH1 N T R AA0 P AA2 R T IY0
+INTRASPECIFIC  IH2 N T R AA0 S P AH0 S IH1 F AH0 K
+INTRASTATE  IH2 N T R AA0 S T EY1 T
+INTRAUTERINE  IH2 N T R AA0 Y UW1 T ER0 IH0 N
+INTRAVENOUS  IH2 N T R AA0 V IY1 N AH0 S
+INTRAVENOUSLY  IH2 N T R AA0 V IY1 N AH0 S L IY2
+INTRAVENOUSLY(1)  IH2 N T R AE1 V EH0 N AH0 S L IY2
+INTRAWEST  IH2 N T R AA0 W EH1 S T
+INTRAWEST(1)  IH1 N T R AA0 W AH0 S T
+INTREPID  IH2 N T R EH1 P IH0 D
+INTREPIDITY  IH2 N T R EH1 P IH0 D AH0 T IY2
+INTREPIDLY  IH2 N T R EH1 P IH0 D L IY0
+INTREX  IH1 N T R AH0 K S
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+INTRICACY  IH1 N T R AH0 K AH0 S IY0
+INTRICATE  IH1 N T R AH0 K AH0 T
+INTRICATELY  IH1 N T R AH0 K AH0 T L IY0
+INTRIE  IH1 N T R IY0
+INTRIERI  IH2 N T R IH1 R IY0
+INTRIGUE  IH2 N T R IY1 G
+INTRIGUE(1)  IH1 N T R IY0 G
+INTRIGUED  IH1 N T R IY0 G D
+INTRIGUED(1)  IH2 N T R IY1 G D
+INTRIGUES  IH2 N T R IY1 G Z
+INTRIGUES(1)  IH1 N T R IY0 G Z
+INTRIGUING  IH2 N T R IY1 G IH0 NG
+INTRIGUINGLY  IH2 N T R IY1 G IH0 NG L IY0
+INTRINSIC  IH2 N T R IH1 N S IH0 K
+INTRINSICALLY  IH2 N T R IH1 N S IH0 K AH0 L IY0
+INTRINSICALLY(1)  IH2 N T R IH1 N S IH0 K L IY0
+INTRO  IH1 N T R OW0
+INTRODUCE  IH2 N T R AH0 D UW1 S
+INTRODUCE(1)  IH2 N T R OW0 D UW1 S
+INTRODUCED  IH2 N T R AH0 D UW1 S T
+INTRODUCED(1)  IH2 N T R OW0 D UW1 S T
+INTRODUCES  IH2 N T R AH0 D UW1 S IH0 Z
+INTRODUCES(1)  IH2 N T R OW0 D UW1 S IH0 Z
+INTRODUCING  IH2 N T R AH0 D UW1 S IH0 NG
+INTRODUCING(1)  IH2 N T R OW0 D UW1 S IH0 NG
+INTRODUCTION  IH2 N T R AH0 D AH1 K SH AH0 N
+INTRODUCTION(1)  IH2 N T R OW0 D AH1 K SH AH0 N
+INTRODUCTIONS  IH2 N T R AH0 D AH1 K SH AH0 N Z
+INTRODUCTIONS(1)  IH2 N T R OW0 D AH1 K SH AH0 N Z
+INTRODUCTORY  IH2 N T R AH0 D AH1 K T ER0 IY0
+INTRODUCTORY(1)  IH2 N T R OW0 D AH1 K T ER0 IY0
+INTRON  IH1 N T R AH0 N
+INTROS  IH1 N T R OW0 Z
+INTROSPECT  IH1 N T R AH0 S P EH2 K T
+INTROSPECTION  IH2 N T R AH0 S P EH1 K SH AH0 N
+INTROSPECTION(1)  IH2 N T R OW0 S P EH1 K SH AH0 N
+INTROSPECTIVE  IH2 N T R AH0 S P EH1 K T IH0 V
+INTROSPECTIVE(1)  IH2 N T R OW0 S P EH1 K T IH0 V
+INTROVERT  IH1 N T R OW0 V ER2 T
+INTROVERTED  IH1 N T R OW0 V ER2 T IH0 D
+INTRUDE  IH2 N T R UW1 D
+INTRUDED  IH2 N T R UW1 D AH0 D
+INTRUDER  IH2 N T R UW1 D ER0
+INTRUDERS  IH2 N T R UW1 D ER0 Z
+INTRUDES  IH2 N T R UW1 D Z
+INTRUDING  IH2 N T R UW1 D IH0 NG
+INTRUSION  IH2 N T R UW1 ZH AH0 N
+INTRUSIONS  IH2 N T R UW1 ZH AH0 N Z
+INTRUSIVE  IH2 N T R UW1 S IH0 V
+INTRUSIVENESS  IH2 N T R UW1 S IH0 V N EH0 S
+INTUIT  IH2 N T UW1 AH0 T
+INTUIT'S  IH2 N T UW1 AH0 T S
+INTUITION  IH2 N T UW0 IH1 SH AH0 N
+INTUITIVE  IH2 N T UW1 AH0 T IH0 V
+INTUITIVELY  IH2 N T UW1 IH0 T IH0 V L IY0
+INUIT  IH1 N UW0 T
+INUNDATE  IH1 N AH0 N D EY2 T
+INUNDATED  IH1 N AH0 N D EY2 T IH0 D
+INUNDATING  IH1 N AH0 N D EY2 T IH0 NG
+INUNDATING(1)  IH2 N AH1 N D EY2 T IH0 NG
+INUNDATION  IH2 N AH0 N D EY1 SH AH0 N
+INUNDATIONS  IH2 N AH0 N D EY1 SH AH0 N Z
+INURE  IH2 N Y UH1 R
+INURED  IH2 N Y UH1 R D
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+INVADER  IH2 N V EY1 D ER0
+INVADERS  IH2 N V EY1 D ER0 Z
+INVADES  IH2 N V EY1 D Z
+INVADING  IH2 N V EY1 D IH0 NG
+INVALID  IH1 N V AH0 L AH0 D
+INVALID(1)  IH1 N V AH0 L IH0 D
+INVALID(2)  IH2 N V AE1 L AH0 D
+INVALIDATE  IH2 N V AE1 L IH0 D EY2 T
+INVALIDATED  IH2 N V AE1 L AH0 D EY2 T AH0 D
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+INVALIDATING  IH2 N V AE1 L AH0 D EY2 T IH0 NG
+INVALIDATION  IH2 N V AE2 L AH0 D EY1 SH AH0 N
+INVALIDS  IH1 N V AH0 L AH0 D Z
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+INVARIABLE  IH2 N V EH1 R IY0 AH0 B AH0 L
+INVARIABLY  IH2 N V EH1 R IY0 AH0 B L IY0
+INVARIANCE  IH2 N V EH1 R IY0 AH0 N S
+INVARIANT  IH2 N V EH1 R IY0 AH0 N T
+INVASION  IH2 N V EY1 ZH AH0 N
+INVASIONS  IH2 N V EY1 ZH AH0 N Z
+INVASIVE  IH2 N V EY1 S IH0 V
+INVECTIVE  IH2 N V EH1 K T IH0 V
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+INVENTED  IH2 N V EH1 N T AH0 D
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+INVENTING  IH2 N V EH1 N T IH0 NG
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+INVENTIVE  IH2 N V EH1 N T IH0 V
+INVENTIVENESS  IH2 N V EH1 N T IH0 V N AH0 S
+INVENTOR  IH2 N V EH1 N T ER0
+INVENTORIED  IH1 N V AH0 N T AO2 R IY0 D
+INVENTORIES  IH2 N V AH0 N T AO1 R IY0 Z
+INVENTORS  IH2 N V EH1 N T ER0 Z
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+INVENTS  IH2 N V EH1 N T S
+INVERLAT  IH1 N V ER0 L AE2 T
+INVERNESS  IH1 N V ER0 N EH2 S
+INVERSE  IH2 N V ER1 S
+INVERSELY  IH2 N V ER1 S L IY0
+INVERSION  IH2 N V ER1 ZH AH0 N
+INVERT  IH2 N V ER1 T
+INVERTEBRATE  IH2 N V ER1 T AH0 B R AH0 T
+INVERTEBRATE(1)  IH2 N V ER1 T AH0 B R EY2 T
+INVERTEBRATES  IH2 N V ER1 T AH0 B R AH0 T S
+INVERTEBRATES(1)  IH2 N V ER1 T AH0 B R EY2 T S
+INVERTED  IH2 N V ER1 T IH0 D
+INVESCO  IH2 N V EH1 S K OW0
+INVESCO'S  IH2 N V EH1 S OW0 Z
+INVEST  IH2 N V EH1 S T
+INVESTABLE  IH2 N V EH1 S T AH0 B AH0 L
+INVESTCORP  IH2 N V EH1 S T K AO0 R P
+INVESTED  IH2 N V EH1 S T AH0 D
+INVESTED(1)  IH2 N V EH1 S T IH0 D
+INVESTIGATE  IH2 N V EH1 S T AH0 G EY2 T
+INVESTIGATED  IH2 N V EH1 S T AH0 G EY2 T AH0 D
+INVESTIGATED(1)  IH2 N V EH1 S T AH0 G EY2 T IH0 D
+INVESTIGATES  IH2 N V EH1 S T AH0 G EY2 T S
+INVESTIGATING  IH2 N V EH1 S T AH0 G EY2 T IH0 NG
+INVESTIGATION  IH2 N V EH2 S T AH0 G EY1 SH AH0 N
+INVESTIGATIONAL  IH2 N V EH0 S T IH0 G EY1 SH AH0 N AH0 L
+INVESTIGATIONS  IH2 N V EH2 S T AH0 G EY1 SH AH0 N Z
+INVESTIGATIVE  IH2 N V EH1 S T AH0 G EY2 T IH0 V
+INVESTIGATOR  IH2 N V EH1 S T AH0 G EY2 T ER0
+INVESTIGATOR'S  IH2 N V EH1 S T AH0 G EY2 T ER0 Z
+INVESTIGATORS  IH2 N V EH1 S T AH0 G EY2 T ER0 Z
+INVESTIGATORS'  IH2 N V EH1 S T AH0 G EY2 T ER0 Z
+INVESTIGATORY  IH2 N V EH1 S T AH0 G AH0 T AO2 R IY0
+INVESTIMENTO  IH2 N V EH2 S T IH0 M EH1 N T OW2
+INVESTING  IH2 N V EH1 S T IH0 NG
+INVESTISSEMENTS  IH2 N V EH2 S T IY1 Z IH0 M AA0 N T S
+INVESTITURE  IH2 N V EH1 S T AH0 CH ER0
+INVESTMENT  IH2 N V EH1 S T M AH0 N T
+INVESTMENT(1)  IH2 N V EH1 S M AH0 N T
+INVESTMENT'S  IH2 N V EH1 S T M AH0 N T S
+INVESTMENT'S(1)  IH2 N V EH1 S M AH0 N T S
+INVESTMENTS  IH2 N V EH1 S T M AH0 N T S
+INVESTMENTS(1)  IH2 N V EH1 S M AH0 N T S
+INVESTMENTS'  IH2 N V EH1 S T M AH0 N T S
+INVESTMENTS'(1)  IH2 N V EH1 S M AH0 N T S
+INVESTNET  IH2 N V EH1 S T N EH2 T
+INVESTOR  IH2 N V EH1 S T ER0
+INVESTOR'S  IH2 N V EH1 S T ER0 Z
+INVESTORS  IH2 N V EH1 S T ER0 Z
+INVESTORS'  IH2 N V EH1 S T ER0 Z
+INVESTS  IH2 N V EH1 S T S
+INVETERATE  IH2 N V EH1 T ER0 AH0 T
+INVIDIOUS  IH2 N V IH1 D IY0 AH0 S
+INVIGORATE  IH2 N V IH1 G ER0 IH0 T
+INVIGORATED  IH2 N V IH1 G ER0 EY2 T IH0 D
+INVIGORATING  IH2 N V IH1 G ER0 EY2 T IH0 NG
+INVINCIBILITY  IH2 N V IH2 N S AH0 B IH1 L IH0 T IY0
+INVINCIBLE  IH2 N V IH1 N S AH0 B AH0 L
+INVIOLABLE  IH2 N V AY1 AH0 L AH0 B AH0 L
+INVIOLATE  IH2 N V AY1 AH0 L IH0 T
+INVIRASE  IH2 N V AY1 R EY2 Z
+INVISIBILITY  IH2 N V IH2 Z AH0 B IH1 L AH0 T IY0
+INVISIBLE  IH2 N V IH1 Z AH0 B AH0 L
+INVISIBLES  IH2 N V IH1 Z AH0 B AH0 L Z
+INVITATION  IH2 N V IH0 T EY1 SH AH0 N
+INVITATIONAL  IH2 N V AH0 T EY1 SH AH0 N AH0 L
+INVITATIONS  IH2 N V IH0 T EY1 SH AH0 N Z
+INVITE  IH2 N V AY1 T
+INVITED  IH2 N V AY1 T AH0 D
+INVITED(1)  IH2 N V AY1 T IH0 D
+INVITEE  IH2 N V AY0 T IY1
+INVITEES  IH2 N V AY0 T IY1 Z
+INVITES  IH2 N V AY1 T S
+INVITING  IH2 N V AY1 T IH0 NG
+INVITRON  IH1 N V IH0 T R AA0 N
+INVITRON'S  IH1 N V IH0 T R AA0 N Z
+INVOCATION  IH2 N V AH0 K EY1 SH AH0 N
+INVOICE  IH1 N V OY0 S
+INVOICES  IH1 N V OY0 S IH0 Z
+INVOICING  IH1 N V OY2 S IH0 NG
+INVOKE  IH2 N V OW1 K
+INVOKED  IH2 N V OW1 K T
+INVOKES  IH2 N V OW1 K S
+INVOKING  IH2 N V OW1 K IH0 NG
+INVOLUNTARILY  IH2 N V OW0 L AH1 N T ER0 IH2 L IY0
+INVOLUNTARILY(1)  IH2 N V AA2 L AH0 N T ER1 AH0 L IY0
+INVOLUNTARY  IH2 N V AA1 L AH0 N T EH2 R IY0
+INVOLVE  IH2 N V AA1 L V
+INVOLVED  IH2 N V AA1 L V D
+INVOLVEMENT  IH2 N V AA1 L V M AH0 N T
+INVOLVEMENTS  IH2 N V AA1 L V M AH0 N T S
+INVOLVES  IH2 N V AA1 L V Z
+INVOLVING  IH2 N V AA1 L V IH0 NG
+INVULNERABILITY  IH2 N V AH2 L N ER0 AH0 B IH1 L IH0 T IY0
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+INWARDLY  IH1 N W ER0 D L IY0
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+INY  IH1 N IY0
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+INZER  IH1 N Z ER0
+IO  AY1 OW0
+IODICE  AY1 AH0 D AY2 S
+IODICE(1)  AY2 AH0 D IY1 S EY0
+IODIDE  AY1 AH0 D AY2 D
+IODIDE'S  AY1 AH0 D AY2 D Z
+IODIDES  AY1 AH0 D AY2 D Z
+IODINE  AY1 AH0 D AY2 N
+IOLA  AY0 OW1 L AH0
+IOLANDE  IY0 OW0 L AA1 N D IY0
+IOLANTHE  IY0 AH0 L AA1 N TH EY0
+IOLE  IY0 OW1 L IY0
+IOMEGA  AY2 OW0 M EY1 G AH0
+ION  AY1 AA2 N
+ION(1)  AY1 AH0 N
+IONA  AY0 OW1 N AH0
+IONARDE  IY0 AH0 N AA1 R D EY0
+IONE  AY0 OW1 N IY0
+IONIC  AY2 AA1 N IH0 K
+IONICS  AY2 AA1 N IH0 K S
+IONICS'S  AY2 AA1 N IH0 K S IH0 Z
+IONIZATION  AY2 AH0 N AH0 Z EY1 SH AH0 N
+IONIZE  AY1 AH0 N AY2 Z
+IONIZER  AY1 AH0 N AY2 Z ER0
+IONIZERS  AY1 AH0 N AY2 Z ER0 Z
+IONIZING  AY1 AH0 N AY2 Z IH0 NG
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+IONOSPHERIC  AY2 AA2 N AO0 S F EH1 R IH0 K
+IONS  AY1 AA2 N Z
+IORIO  IY0 AO1 R IY0 OW2
+IOS  IY1 OW0 S
+IOS(1)  AY1 OW0 S
+IOSIF  Y AO1 S AH0 F
+IOSUE  AY0 OW1 S UW0
+IOTA  AY2 OW1 T AH0
+IOTT  AY1 AH0 T
+IOU  AY2 OW2 Y UW1
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+MIRO(1)  M IH0 R OW1
+MIRO'S  M IH1 R OW0 Z
+MIRO'S(1)  M IH0 R OW1 Z
+MIRON  M IH0 R AO1 N
+MIRONENKO  M IH2 R OW0 N EH1 NG K OW0
+MIROSLAV  M IH1 R AH0 S L AA0 V
+MIRRA  M IH1 R AH0
+MIRREN  M IH1 R AH0 N
+MIRROR  M IH1 R ER0
+MIRROR'S  M IH1 R ER0 Z
+MIRRORED  M IH1 R ER0 D
+MIRRORING  M IH1 R ER0 IH0 NG
+MIRRORS  M IH1 R ER0 Z
+MIRSAD  M IH1 R S AE2 D
+MIRSKY  M ER1 S K IY0
+MIRTH  M ER1 TH
+MIRTI  M ER1 T IY0
+MIRTI(1)  M ER1 T IY2
+MIRTLE  M ER1 T AH0 L
+MIRTO  M IH1 R T OW0
+MIRTZ  M ER1 T S
+MIRV  M ER1 V
+MIRZA  M ER1 Z AH0
+MIS  M IH1 S
+MISA  M IY1 Z AH0
+MISA'S  M IY1 Z AH0 Z
+MISADVENTURE  M IH0 S AH0 D V EH1 N CH ER0
+MISADVENTURES  M IH0 S AH0 D V EH1 N CH ER0 Z
+MISALLOCATE  M IH0 S AE1 L AH0 K EY2 T
+MISALLOCATED  M IH0 S AE1 L AH0 K EY2 T IH0 D
+MISALLOCATES  M IH0 S AE1 L AH0 K EY2 T S
+MISALLOCATING  M IH0 S AE1 L AH0 K EY2 T IH0 NG
+MISALLOCATION  M IH0 S AE2 L AH0 K EY1 SH AH0 N
+MISANTHROPE  M IH1 S AH0 N TH R OW2 P
+MISAPPLICATION  M IH0 S AE2 P L AH0 K EY1 SH AH0 N
+MISAPPLIED  M IH2 S AH0 P L AY1 D
+MISAPPLIES  M IH2 S AH0 P L AY1 Z
+MISAPPLY  M IH0 S AH0 P L AY1
+MISAPPLYING  M IH0 S AH0 P L AY1 IH0 NG
+MISAPPREHENSION  M IH0 S AE2 P R IY0 HH EH1 N SH AH0 N
+MISAPPROPRIATE  M IH0 S AH0 P R OW1 P R IY0 EY2 T
+MISAPPROPRIATED  M IH2 S AH0 P R OW1 P R IY0 EY2 T IH0 D
+MISAPPROPRIATES  M IH2 S AH0 P R OW1 P R IY0 EY2 T S
+MISAPPROPRIATING  M IH2 S AH0 P R OW1 P R IY0 EY2 T IH0 NG
+MISAPPROPRIATION  M IH2 S AH0 P R OW2 P R IY0 EY1 SH AH0 N
+MISATER  M IH0 S EY1 T ER0
+MISAWA  M IH0 S AA1 W AH0
+MISBEGOTTEN  M IH2 S B AH0 G AA1 T AH0 N
+MISBEHAVE  M IH2 S B AH0 HH EY1 V
+MISBEHAVE(1)  M IH2 S B IY0 HH EY1 V
+MISBEHAVED  M IH2 S B AH0 HH EY1 V D
+MISBEHAVED(1)  M IH2 S B IY0 HH EY1 V D
+MISBEHAVES  M IH2 S B AH0 HH EY1 V Z
+MISBEHAVES(1)  M IH2 S B IY0 HH EY1 V Z
+MISBEHAVING  M IH2 S B AH0 HH EY1 V IH0 NG
+MISBEHAVING(1)  M IH2 S B IY0 HH EY1 V IH0 NG
+MISBEHAVIOR  M IH2 S B AH0 HH EY1 V Y ER0
+MISBEHAVIOR(1)  M IH2 S B IY0 HH EY1 V Y ER0
+MISBRENER  M IH1 S B R EH2 N ER0
+MISCALCULATE  M IH0 S K AE1 L K Y AH0 L EY2 T
+MISCALCULATED  M IH0 S K AE1 L K Y AH0 L EY2 T IH0 D
+MISCALCULATES  M IH0 S K AE1 L K Y AH0 L EY2 T S
+MISCALCULATION  M IH0 S K AE1 L K Y AH0 L EY1 SH AH0 N
+MISCALCULATIONS  M IH0 S K AE1 L K Y AH0 L EY1 SH AH0 N Z
+MISCARRIAGE  M IH0 S K EH1 R AH0 JH
+MISCARRIAGES  M IH0 S K EH1 R IH0 JH IH0 Z
+MISCAST  M IH0 S K AE1 S T
+MISCAVIGE  M IH0 S K AE1 V IH1 JH
+MISCAYUNA  M IH2 S K AY0 Y UW1 N AH0
+MISCAYUNA'S  M IH2 S K AY0 Y UW1 N AH0 Z
+MISCELLANEOUS  M IH2 S AH0 L EY1 N IY0 AH0 S
+MISCELLANY  M IH1 S AH0 L EY2 N IY0
+MISCH  M IH1 SH
+MISCHA  M IH1 SH AH0
+MISCHARACTERIZATION  M IH0 S K AE2 R AH0 K T ER0 AH0 Z EY1 SH AH0 N
+MISCHARACTERIZATIONS  M IH0 S K AE2 R AH0 K T ER0 AH0 Z EY1 SH AH0 N Z
+MISCHARACTERIZE  M IH0 S K AE1 R AH0 K T ER0 AY2 Z
+MISCHARACTERIZED  M IH0 S K AE1 R AH0 K T ER0 AY2 Z D
+MISCHARACTERIZES  M IH0 S K AE1 R AH0 K T ER0 AY2 Z IH0 Z
+MISCHARACTERIZING  M IH0 S K AE1 R AH0 K T ER0 AY2 Z IH0 NG
+MISCHARGE  M IH0 S CH AA1 R JH
+MISCHARGED  M IH0 S CH AA1 R JH D
+MISCHARGES  M IH0 S CH AA1 R JH IH0 Z
+MISCHARGING  M IH0 S CH AA1 R JH IH0 NG
+MISCHEL  M IH1 SH AH0 L
+MISCHER  M IH1 SH ER0
+MISCHIEF  M IH1 S CH AH0 F
+MISCHIEVOUS  M IH1 S CH AH0 V AH0 S
+MISCHKE  M IH1 SH K
+MISCHLER  M IH1 SH AH0 L ER0
+MISCHLER(1)  M IH1 SH L ER0
+MISCIBILITY  M IH2 S IH0 B IH1 L IH0 T IY0
+MISCIBLE  M IH1 S AH0 B AH0 L
+MISCIBLE(1)  M IH1 S IH0 B AH0 L
+MISCOMMUNICATE  M IH0 S K AH0 M Y UW1 N AH0 K EY2 T
+MISCOMMUNICATION  M IH0 S K AH0 M Y UW2 N AH0 K EY1 SH AH0 N
+MISCONCEIVE  M IH0 S K AH0 N S IY1 V
+MISCONCEIVED  M IH0 S K AH0 N S IY1 V D
+MISCONCEIVES  M IH0 S K AH0 N S IY1 V Z
+MISCONCEIVING  M IH0 S K AH0 N S IY1 V IH0 NG
+MISCONCEPTION  M IH0 S K AH0 N S EH1 P SH AH0 N
+MISCONCEPTIONS  M IH0 S K AH0 N S EH1 P SH AH0 N Z
+MISCONDUCT  M IH0 S K AA1 N D AH0 K T
+MISCONSTRUE  M IH0 S K AH0 N S T R UW1
+MISCONSTRUED  M IH2 S K AH0 N S T R UW1 D
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+MISCONSTRUING  M IH0 S K AH0 N S T R UW1 IH0 NG
+MISCOUNT  M IH1 S K AW1 N T
+MISCREANT  M IH1 S K R IY0 AH0 N T
+MISCREANTS  M IH1 S K R IY0 AH0 N T S
+MISCREATION  M IH0 S K R IY0 EY1 SH AH0 N
+MISCREATIONS  M IH2 S K R IY0 EY1 SH AH0 N Z
+MISCUE  M IH1 S K Y UW2
+MISCUES  M IH1 S K Y UW2 Z
+MISDEED  M IH1 S D IY1 D
+MISDEEDS  M IH0 S D IY1 D Z
+MISDEMEANOR  M IH2 S D AH0 M IY1 N ER0
+MISDEMEANORS  M IH2 S D AH0 M IY1 N ER0 Z
+MISDIAGNOSE  M IH0 S D AY2 IH0 G N OW1 Z
+MISDIAGNOSED  M IH0 S D AY2 IH0 G N OW1 Z D
+MISDIAGNOSES  M IH0 S D AY2 IH0 G N OW1 S IY0 Z
+MISDIAGNOSES(1)  M IH0 S D AY2 IH0 G N OW1 S AH0 Z
+MISDIAGNOSIS  M IH0 S D AY2 IH0 G N OW1 S AH0 S
+MISDIRECT  M IH0 S D IH0 R EH1 K T
+MISDIRECTED  M IH0 S D IH0 R EH1 K T IH0 D
+MISEK  M IH1 S EH0 K
+MISENER  M IH1 S IY0 N ER0
+MISENHEIMER  M IH1 S IH0 N HH AY0 M ER0
+MISER  M AY1 Z ER0
+MISERABLE  M IH1 Z ER0 AH0 B AH0 L
+MISERABLE(1)  M IH1 Z R AH0 B AH0 L
+MISERABLES  M IH1 Z ER0 AH0 B AH0 L Z
+MISERABLES(1)  M IH1 Z R AH0 B AH0 L Z
+MISERABLES(2)  M IH2 Z ER0 AA1 B
+MISERABLY  M IH1 Z ER0 AH0 B L IY0
+MISERABLY(1)  M IH1 Z R AH0 B L IY0
+MISERATION  M IH0 Z ER0 EY1 SH AH0 N
+MISERIES  M IH1 Z ER0 IY0 Z
+MISERLY  M AY1 Z ER0 L IY0
+MISERS  M AY1 Z ER0 Z
+MISERY  M IH1 Z ER0 IY0
+MISES  M AY1 Z IH0 Z
+MISFELDT  M IH1 S F IH0 L T
+MISFIRE  M IH0 S F AY1 ER0
+MISFIT  M IH1 S F IH2 T
+MISFITS  M IH1 S F IH2 T S
+MISFORTUNE  M IH0 S F AO1 R CH AH0 N
+MISFORTUNES  M IH0 S F AO1 R CH AH0 N Z
+MISGIVE  M IH0 S G IH1 V
+MISGIVING  M IH0 S G IH1 V IH0 NG
+MISGIVINGS  M IH0 S G IH1 V IH0 NG Z
+MISGOVERNMENT  M IH0 S G AH1 V ER0 N M AH0 N T
+MISGUIDE  M IH0 S G AY1 D
+MISGUIDED  M IH0 S G AY1 D IH0 D
+MISGUIDES  M IH0 S G AY1 D Z
+MISH  M IH1 SH
+MISHA  M IH1 SH AH0
+MISHANDLE  M IH0 S HH AE1 N D AH0 L
+MISHANDLED  M IH0 S HH AE1 N D AH0 L D
+MISHANDLES  M IH0 S HH AE1 N D AH0 L Z
+MISHANDLING  M IH0 S HH AE1 N D L IH0 NG
+MISHAP  M IH1 S HH AE2 P
+MISHAPS  M IH1 S HH AE2 P S
+MISHAWAKA  M IH2 SH AH0 W AO1 K AH0
+MISHAWAUM  M IH1 SH AH0 W AA2 M
+MISHAWUM  M IH1 SH AH0 W AH2 M
+MISHEAR  M IH0 S HH IY1 R
+MISHEARD  M IH2 S HH ER1 D
+MISHKIN  M IY1 SH K IY0 N
+MISHLER  M IH1 SH L ER0
+MISHMASH  M IH1 SH M AE2 SH
+MISHOE  M IY1 SH UW0
+MISHRA  M IH1 SH R AH0
+MISIAK  M IH1 S IY0 AE0 K
+MISIASZEK  M IH0 S IY0 AA1 SH EH0 K
+MISIDENTIFICATION  M IH2 S AY0 D EH2 N T IH0 F IH0 K EY1 SH AH0 N
+MISIDENTIFICATION(1)  M IH2 S AY0 D EH2 N IH0 F IH0 K EY1 SH AH0 N
+MISIDENTIFIED  M IH0 S AY0 D EH1 N T IH0 F AY2 D
+MISIDENTIFIED(1)  M IH0 S AY0 D EH1 N IH0 F AY2 D
+MISIDENTIFIES  M IH0 S AY0 D EH1 N T IH0 F AY2 Z
+MISIDENTIFIES(1)  M IH0 S AY0 D EH1 N IH0 F AY2 Z
+MISIDENTIFY  M IH0 S AY0 D EH1 N T AH0 F AY2
+MISIDENTIFY(1)  M IH0 S AY0 D EH1 N AH0 F AY2
+MISIDENTIFYING  M IH0 S AY0 D EH1 N T AH0 F AY2 IH0 NG
+MISIDENTIFYING(1)  M IH0 S AY0 D EH1 N AH0 F AY2 IH0 NG
+MISIEWICZ  M IH1 S AH0 V IH0 CH
+MISIMPRESSION  M IH0 S IH0 M P R EH1 SH AH0 N
+MISINFORM  M IH0 S IH2 N F AO1 R M
+MISINFORMATION  M IH2 S IH0 N F ER0 M EY1 SH AH0 N
+MISINFORMED  M IH0 S IH0 N F AO1 R M D
+MISINFORMING  M IH0 S IH0 N F AO1 R M IH0 NG
+MISINFORMS  M IH0 S IH2 N F AO1 R M Z
+MISINTERPRET  M IH0 S IH0 N T ER1 P R AH0 T
+MISINTERPRETATION  M IH0 S IH0 N T ER2 P R AH0 T EY1 SH AH0 N
+MISINTERPRETED  M IH0 S IH0 N T ER1 P R AH0 T IH0 D
+MISINTERPRETING  M IH0 S IH0 N T ER1 P R AH0 T IH0 NG
+MISINTERPRETS  M IH0 S IH0 N T ER1 P R AH0 T S
+MISJUDGE  M IH0 S JH AH1 JH
+MISJUDGED  M IH0 S JH AH1 JH D
+MISJUDGES  M IH0 S JH AH1 JH IH0 Z
+MISJUDGING  M IH0 S JH AH1 JH IH0 NG
+MISJUDGMENT  M IH0 S JH AH1 JH M AH0 N T
+MISJUDGMENTS  M IH0 S JH AH1 JH M AH0 N T S
+MISKA  M IH1 S K AH0
+MISKE  M IH1 S K
+MISKELL  M IH1 S K AH0 L
+MISKITO  M IH0 S K IY1 T OW0
+MISKITOS  M IH0 S K IY1 T OW0 S
+MISKO  M IH1 S K OW0
+MISLABEL  M IH0 S L EY1 B AH0 L
+MISLABELED  M IH0 S L EY1 B AH0 L D
+MISLABELING  M IH0 S L EY1 B AH0 L IH0 NG
+MISLABELING(1)  M IH0 S L EY1 B L IH0 NG
+MISLABELS  M IH0 S L EY1 B AH0 L Z
+MISLAID  M IH0 S L EY1 D
+MISLEAD  M IH0 S L IY1 D
+MISLEADING  M IH0 S L IY1 D IH0 NG
+MISLEADINGLY  M IH0 S L IY1 D IH0 NG L IY0
+MISLEADS  M IH0 S L IY1 D Z
+MISLED  M IH0 S L EH1 D
+MISMANAGE  M IH0 S M AE1 N IH0 JH
+MISMANAGED  M IH0 S M AE1 N IH0 JH D
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+MISMANAGES  M IH0 S M AE1 N IH0 JH AH0 Z
+MISMANAGING  M IH0 S M AE1 N IH0 JH IH0 NG
+MISMATCH  M IH0 S M AE1 CH
+MISMATCH(1)  M IH1 S M AE2 CH
+MISMATCHED  M IH0 S M AE1 CH T
+MISMATCHES  M IH0 S M AE1 CH IH0 Z
+MISNER  M IH1 Z N ER0
+MISNOMER  M IH0 S N OW1 M ER0
+MISOGYNE  M IH1 Z AH0 JH IH0 N
+MISOGYNIST  M IH1 Z AH0 JH IH0 N IH0 S T
+MISOGYNISTS  M IH1 Z AH0 JH IH0 N IH0 S T S
+MISOGYNISTS(1)  M IH1 Z AH0 JH IH0 N IH0 S
+MISOGYNY  M IH1 Z AH0 JH IH0 N IY0
+MISPERCEIVE  M IH0 S P ER0 S IY1 V
+MISPERCEIVED  M IH0 S P ER0 S IY1 V D
+MISPERCEIVES  M IH0 S P ER0 S IY1 V Z
+MISPERCEPTION  M IH2 S P ER0 S EH1 P SH AH0 N
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+MISPLACE  M IH0 S P L EY1 S
+MISPLACED  M IH0 S P L EY1 S T
+MISPLACES  M IH0 S P L EY1 S IH0 Z
+MISPLACING  M IH0 S P L EY1 S IH0 NG
+MISPRICE  M IH0 S P R AY1 S
+MISPRICED  M IH0 S P R AY1 S T
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+MISPRICING  M IH0 S P R AY1 S IH0 NG
+MISPRINT  M IH1 S P R IH1 N T
+MISPRINTED  M IH1 S P R IH1 N T AH0 D
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+MISPRISION  M IH0 S P R IH1 ZH AH0 N
+MISPRONOUNCE  M IH0 S P R AH0 N AW1 N S
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+MISPRONOUNCES  M IH0 S P R AH0 N AW1 N S IH0 Z
+MISPRONOUNCING  M IH0 S P R AH0 N AW1 N S IH0 NG
+MISQUOTE  M IH0 S K W OW1 T
+MISQUOTED  M IH0 S K W OW1 T IH0 D
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+MISREAD  M IH0 S R IY1 D
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+MISREADING  M IH0 S R IY1 D IH0 NG
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+MISRECOGNIZED  M IH0 S R EH1 K AH0 G N AY2 Z D
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+MISREMEMBER  M IH0 S R IY0 M EH1 M B ER0
+MISREMEMBERED  M IH0 S R IY0 M EH1 M B ER0 D
+MISREPORT  M IH0 S R IH0 P AO1 R T
+MISREPORTED  M IH0 S R IH0 P AO1 R T IH0 D
+MISREPRESENT  M IH0 S R EH2 P R AH0 Z EH1 N T
+MISREPRESENTATION  M IH0 S R EH2 P R IH0 Z EH0 N T EY1 SH AH0 N
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+MISREPRESENTED  M IH2 S R EH0 P R IH0 Z EH1 N T IH0 D
+MISREPRESENTED(1)  M IH2 S R EH0 P R IH0 Z EH1 N IH0 D
+MISREPRESENTING  M IH0 S R EH2 P R AH0 Z EH1 N T IH0 NG
+MISREPRESENTING(1)  M IH0 S R EH2 P R AH0 Z EH1 N IH0 NG
+MISREPRESENTS  M IH0 S R EH2 P R AH0 Z EH1 N T S
+MISREPRESENTS(1)  M IH0 S R EH2 P R AH0 Z EH1 N S
+MISRULE  M IH0 S R UW1 L
+MISS  M IH1 S
+MISSAL  M IH1 S AH0 L
+MISSED  M IH1 S T
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+MISSES  M IH1 S IH0 Z
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+MISSEY  M IH1 S IY0
+MISSHAPEN  M IH0 S SH EY1 P AH0 N
+MISSHAPEN(1)  M IH0 S HH AE1 P AH0 N
+MISSIE  M IH1 S IY0
+MISSILDINE  M IH0 S IY0 L D IY1 N IY0
+MISSILDINE(1)  M IH0 S IH0 L D AY1 N
+MISSILE  M IH1 S AH0 L
+MISSILE'S  M IH1 S AH0 L Z
+MISSILES  M IH1 S AH0 L Z
+MISSILES'  M IH1 S AH0 L Z
+MISSIMER  M IH1 S IH0 M ER0
+MISSING  M IH1 S IH0 NG
+MISSION  M IH1 SH AH0 N
+MISSION'S  M IH1 SH AH0 N Z
+MISSIONARIES  M IH1 SH AH0 N EH2 R IY0 Z
+MISSIONARY  M IH1 SH AH0 N EH2 R IY0
+MISSIONS  M IH1 SH AH0 N Z
+MISSIS  M IH1 S IH0 Z
+MISSISSAUGA  M IH2 S IH0 S AO1 G AH0
+MISSISSIPPI  M IH2 S IH0 S IH1 P IY0
+MISSISSIPPI'S  M IH2 S IH0 S IH1 P IY0 Z
+MISSISSIPPIAN  M IH2 S IH0 S IH1 P IY0 AH0 N
+MISSISSIPPIANS  M IH2 S IH0 S IH1 P IY0 AH0 N Z
+MISSISSIPPIS  M IH2 S IH0 S IH1 P IY0 Z
+MISSIVE  M IH1 S IH0 V
+MISSLER  M IH1 S L ER0
+MISSOULA  M IH0 Z UW1 L AH0
+MISSOURI  M AH0 Z UH1 R IY0
+MISSOURI(1)  M AH0 Z ER1 AH0
+MISSOURI'S  M AH0 Z UH1 R IY0 Z
+MISSOURI'S(1)  M AH0 Z ER1 AH0 Z
+MISSOURIAN  M AH0 Z UH1 R IY0 AH0 N
+MISSOURIANS  M AH0 Z UH1 R IY0 AH0 N Z
+MISSPEAK  M IH0 S S P IY1 K
+MISSPEAK(1)  M IH0 S P IY1 K
+MISSPELL  M IH0 S S P EH1 L
+MISSPELL(1)  M IH0 S P EH1 L
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+MISSPELLED(1)  M IH0 S P EH1 L D
+MISSPELLING  M IH0 S S P EH1 L IH0 NG
+MISSPELLING(1)  M IH0 S P EH1 L IH0 NG
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+MISSPELLS(1)  M IH0 S P EH1 L Z
+MISSPEND  M IH0 S S P EH1 N D
+MISSPEND(1)  M IH0 S P EH1 N D
+MISSPENDING  M IH0 S S P EH1 N D IH0 NG
+MISSPENDING(1)  M IH0 S P EH1 N D IH0 NG
+MISSPENT  M IH0 S S P EH1 N T
+MISSPENT(1)  M IH0 S P EH1 N T
+MISSPOKE  M IH0 S S P OW1 K
+MISSPOKE(1)  M IH0 S P OW1 K
+MISSPOKEN  M IH0 S S P OW1 K AH0 N
+MISSPOKEN(1)  M IH0 S P OW1 K AH0 N
+MISSTATE  M IH0 S S T EY1 T
+MISSTATE(1)  M IH0 S T EY1 T
+MISSTATED  M IH0 S S T EY1 T IH0 D
+MISSTATED(1)  M IH0 S T EY1 T IH0 D
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+MISSTATING(1)  M IH0 S T EY1 T IH0 NG
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+MISSTEPS(1)  M IH0 S T EH1 P S
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+OVALLE  OW2 V AA1 L IY0
+OVARIAN  OW0 V EH1 R IY2 AH0 N
+OVARIES  OW1 V ER2 IY0 Z
+OVARY  OW1 V ER2 IY0
+OVATE  OW1 V EY0 T
+OVATION  OW0 V EY1 SH AH0 N
+OVATIONS  OW0 V EY1 SH AH0 N Z
+OVEN  AH1 V AH0 N
+OVENS  AH1 V AH0 N Z
+OVER  OW1 V ER0
+OVERABUNDANCE  OW1 V ER0 AH0 B AH1 N D AH0 N S
+OVERACKER  OW1 V ER0 AH0 K ER0
+OVERACT  OW1 V ER0 AE2 K T
+OVERACTED  OW1 V ER0 AE2 K T IH0 D
+OVERACTED(1)  OW2 V ER0 AE1 K T IH0 D
+OVERACTIVE  OW1 V ER0 AE1 K T IH0 V
+OVERACTS  OW1 V ER0 AE2 K T S
+OVERALL  OW1 V ER0 AO2 L
+OVERALLOTMENT  OW1 V ER0 AH0 L AA1 T M AH0 N T
+OVERALLOTMENTS  OW1 V ER0 AH0 L AA1 T M AH0 N T S
+OVERALLS  OW1 V ER0 AO2 L Z
+OVERAMBITIOUS  OW1 V ER0 AE0 M B IH2 SH AH0 S
+OVERARCHING  OW1 V ER0 AA2 R CH IH0 NG
+OVERATE  OW0 V ER0 EY1 T
+OVERBAUGH  OW0 V ER1 B AO0
+OVERBAY  OW1 V ER0 B EY2
+OVERBEARING  OW1 V ER0 B EH1 R IH0 NG
+OVERBECK  OW1 V ER0 B EH2 K
+OVERBEY  OW1 V ER0 B IY0
+OVERBILL  OW2 V ER0 B IH1 L
+OVERBILLED  OW1 V ER0 B IH2 L D
+OVERBILLING  OW1 V ER0 B IH2 L IH0 NG
+OVERBILLS  OW2 V ER0 B IH1 L Z
+OVERBLOWN  OW2 V ER0 B L OW1 N
+OVERBOARD  OW1 V ER0 B AO2 R D
+OVERBOOK  OW1 V ER0 B UH2 K
+OVERBOOKED  OW1 V ER0 B UH2 K T
+OVERBOOKING  OW1 V ER0 B UH2 K IH0 NG
+OVERBOOKS  OW1 V ER0 B UH2 K S
+OVERBOUGHT  OW1 V ER0 B AO1 T
+OVERBUILDING  OW1 V ER0 B IH2 L D IH0 NG
+OVERBUILT  OW1 V ER0 B IH1 L T
+OVERBURDEN  OW1 V ER0 B ER1 D AH0 N
+OVERBURDENED  OW1 V ER0 B ER1 D AH0 N D
+OVERBUY  OW1 V ER0 B AY2
+OVERBY  OW1 V ER0 B IY0
+OVERCAME  OW1 V ER0 K EY1 M
+OVERCAPACITY  OW1 V ER0 K AH0 P AE1 S AH0 T IY0
+OVERCASH  OW1 V ER0 K AE2 SH
+OVERCAST  OW1 V ER0 K AE2 S T
+OVERCHARGE  OW1 V ER0 CH AA2 R JH
+OVERCHARGED  OW1 V ER0 CH AA1 R JH D
+OVERCHARGES  OW1 V ER0 CH AA2 R JH IH0 Z
+OVERCHARGING  OW2 V ER0 CH AA1 R JH IH0 NG
+OVERCOAT  OW1 V ER0 K OW2 T
+OVERCOATS  OW1 V ER0 K OW2 T S
+OVERCOME  OW1 V ER0 K AH2 M
+OVERCOMES  OW1 V ER0 K AH2 M Z
+OVERCOMING  OW1 V ER0 K AH2 M IH0 NG
+OVERCONFIDENCE  OW2 V ER0 K AA1 N F IH0 D AH0 N S
+OVERCONFIDENT  OW2 V ER0 K AA1 N F IH0 D AH0 N T
+OVERCONSUMPTION  OW1 V ER0 K AH0 N S AH2 M P SH AH0 N
+OVERCOOK  OW2 V ER0 K UH1 K
+OVERCOOKED  OW2 V ER0 K UH1 K T
+OVERCOOKING  OW2 V ER0 K UH1 K IH0 NG
+OVERCOOKS  OW2 V ER0 K UH1 K S
+OVERCROWD  OW2 V ER0 K R AW1 D
+OVERCROWDED  OW1 V ER0 K R AW2 D IH0 D
+OVERCROWDING  OW1 V ER0 K R AW2 D IH0 NG
+OVERCROWDS  OW2 V ER0 K R AW1 D Z
+OVERDAMPING  OW1 V ER0 D AE2 M P IH0 NG
+OVERDEPENDENCE  OW1 V ER0 D IH0 P EH2 N D AH0 N S
+OVERDEPENDENT  OW1 V ER0 D IH0 P EH2 N D AH0 N T
+OVERDID  OW1 V ER0 D IH2 D
+OVERDO  OW1 V ER0 D UW1
+OVERDOES  OW2 V ER0 D AH1 Z
+OVERDOING  OW1 V ER0 D UW1 IH0 NG
+OVERDONE  OW1 V ER0 D AH1 N
+OVERDORF  OW1 V ER0 D AO0 R F
+OVERDOSE  OW1 V ER0 D OW2 S
+OVERDOSED  OW1 V ER0 D OW2 S T
+OVERDOSES  OW1 V ER0 D OW2 S IH0 Z
+OVERDOSING  OW1 V ER0 D OW2 S IH0 NG
+OVERDRAFT  OW1 V ER0 D R AE2 F T
+OVERDRAFTING  OW1 V ER0 D R AE2 F T IH0 NG
+OVERDRAFTS  OW1 V ER0 D R AE2 F T S
+OVERDRAW  OW1 V ER0 D R AO2
+OVERDRAWN  OW1 V ER0 D R AO1 N
+OVERDRESS  OW1 V ER0 D R EH2 S
+OVERDREW  OW1 V ER0 D R UW2
+OVERDRIVE  OW1 V ER0 D R AY2 V
+OVERDUE  OW1 V ER0 D UW1
+OVEREAGER  OW1 V ER0 IY2 G ER0
+OVEREAT  OW1 V ER0 IY2 T
+OVEREATING  OW1 V ER0 IY1 T IH0 NG
+OVEREMPHASIZE  OW1 V ER0 EH1 M F AH0 S AY2 Z
+OVEREMPHASIZED  OW1 V ER0 EH1 M F AH0 S AY2 Z D
+OVEREMPHASIZES  OW1 V ER0 EH1 M F AH0 S AY2 Z IH0 Z
+OVEREMPHASIZING  OW1 V ER0 EH1 M F AH0 S AY2 Z IH0 NG
+OVERESTIMATE  OW2 V ER0 EH1 S T AH0 M EY2 T
+OVERESTIMATED  OW2 V ER0 EH1 S T AH0 M EY2 T IH0 D
+OVERESTIMATES  OW2 V ER0 EH1 S T AH0 M EY2 T S
+OVERESTIMATING  OW2 V ER0 EH1 S T AH0 M EY2 T IH0 NG
+OVEREXCITE  OW2 V ER0 EH0 K S AY1 T
+OVEREXCITED  OW2 V ER0 EH0 K S AY1 T IH0 D
+OVEREXCITES  OW2 V ER0 EH0 K S AY1 T S
+OVEREXCITING  OW2 V ER0 EH0 K S AY1 T IH0 NG
+OVEREXPANSION  OW2 V ER0 IH0 K S P AE1 N SH AH0 N
+OVEREXPOSE  OW2 V ER0 IH0 K S P OW1 Z
+OVEREXPOSED  OW2 V ER0 IH0 K S P OW1 Z D
+OVEREXPOSES  OW2 V ER0 IH0 K S P OW1 Z IH0 Z
+OVEREXPOSING  OW2 V ER0 IH0 K S P OW1 Z IH0 NG
+OVEREXPOSURE  OW2 V ER0 IH0 K S P OW1 ZH ER0
+OVEREXTEND  OW2 V ER0 IH0 K S T EH1 N D
+OVEREXTENDED  OW2 V ER0 IH0 K S T EH1 N D IH0 D
+OVEREXTENDING  OW2 V ER0 IH0 K S T EH1 N D IH0 NG
+OVEREXTENDS  OW2 V ER0 IH0 K S T EH1 N D Z
+OVERFED  OW2 V ER0 F EH1 D
+OVERFEED  OW2 V ER0 F IY1 D
+OVERFELT  OW1 V ER0 F EH2 L T
+OVERFIELD  OW1 V ER0 F IY2 L D
+OVERFILL  OW1 V ER0 F IH2 L
+OVERFISHING  OW1 V ER0 F IH2 SH IH0 NG
+OVERFLIGHT  OW1 V ER0 F L AY2 T
+OVERFLIGHTS  OW1 V ER0 F L AY2 T S
+OVERFLOW  OW1 V ER0 F L OW2
+OVERFLOW(1)  OW2 V ER0 F L OW1
+OVERFLOWED  OW2 V ER0 F L OW1 D
+OVERFLOWING  OW1 V ER0 F L OW2 IH0 NG
+OVERFLOWS  OW1 V ER0 F L OW2 Z
+OVERFLY  OW2 V ER0 F L AY1
+OVERFLYING  OW2 V ER0 F L AY1 IH0 NG
+OVERFUND  OW1 V ER0 F AH2 N D
+OVERFUNDED  OW1 V ER0 F AH2 N D IH0 D
+OVERFUNDING  OW1 V ER0 F AH2 N D IH0 NG
+OVERGAARD  OW1 V ER0 G AA2 R D
+OVERGENEROUS  OW1 V ER0 JH EH2 N ER0 AH0 S
+OVERGRAZING  OW1 V ER0 G R EY2 Z IH0 NG
+OVERGROWING  OW2 V ER0 G R OW1 W IH0 NG
+OVERGROWN  OW2 V ER0 G R OW1 N
+OVERGROWTH  OW1 V ER0 G R OW0 TH
+OVERHANG  OW1 V ER0 HH AE2 NG
+OVERHANGING  OW1 V ER0 HH AE2 NG IH0 NG
+OVERHANGS  OW1 V ER0 HH AE2 NG Z
+OVERHAUL  OW1 V ER0 HH AO2 L
+OVERHAULED  OW1 V ER0 HH AO2 L D
+OVERHAULING  OW1 V ER0 HH AO2 L IH0 NG
+OVERHAULS  OW1 V ER0 HH AO2 L Z
+OVERHEAD  OW1 V ER0 HH EH1 D
+OVERHEADS  OW1 V ER0 HH EH2 D Z
+OVERHEAR  OW1 V ER0 HH IY1 R
+OVERHEARD  OW1 V ER0 HH ER1 D
+OVERHEARING  OW2 V ER0 HH IY1 R IH0 NG
+OVERHEARS  OW1 V ER0 HH IY1 R Z
+OVERHEAT  OW1 V ER0 HH IY2 T
+OVERHEATED  OW1 V ER0 HH IY2 T IH0 D
+OVERHEATING  OW1 V ER0 HH IY2 T IH0 NG
+OVERHEATS  OW1 V ER0 HH IY2 T S
+OVERHOLSER  OW1 V ER0 HH OW2 L S ER0
+OVERHOLT  OW1 V ER0 HH OW0 L T
+OVERHOLTZER  OW1 V ER0 HH OW0 L T Z ER0
+OVERHUNG  OW0 V ER0 HH AH1 NG
+OVERJOYED  OW2 V ER0 JH OY1 D
+OVERKILL  OW1 V ER0 K IH2 L
+OVERKILLING  OW1 V ER0 K IH2 L IH0 NG
+OVERLAID  OW1 V ER0 L EY2 D
+OVERLAIN  OW1 V ER0 L EY2 N
+OVERLAND  OW1 V ER0 L AE2 N D
+OVERLAND(1)  OW1 V ER0 L AH0 N D
+OVERLAP  OW1 V ER0 L AE2 P
+OVERLAPPED  OW1 V ER0 L AE2 P T
+OVERLAPPING  OW1 V ER0 L AE2 P IH0 NG
+OVERLAPS  OW1 V ER0 L AE2 P S
+OVERLAY  OW1 V ER0 L EY2
+OVERLAYS  OW1 V ER0 L EY2 Z
+OVERLEVERAGE  OW2 V ER0 L EH1 V R IH0 JH
+OVERLEVERAGED  OW1 V ER0 L EH1 V R IH0 JH D
+OVERLEY  OW1 V ER0 L IY0
+OVERLOAD  OW1 V ER0 L OW2 D
+OVERLOADED  OW1 V ER0 L OW2 D IH0 D
+OVERLOADING  OW1 V ER0 L OW2 D IH0 NG
+OVERLOADS  OW1 V ER0 L OW2 D Z
+OVERLOCK  OW1 V ER0 L AA2 K
+OVERLOOK  OW1 V ER0 L UH2 K
+OVERLOOKED  OW1 V ER0 L UH2 K T
+OVERLOOKING  OW1 V ER0 L UH2 K IH0 NG
+OVERLOOKS  OW1 V ER0 L UH2 K S
+OVERLORD  OW1 V ER0 L AO2 R D
+OVERLORDS  OW1 V ER0 L AO2 R D Z
+OVERLY  OW1 V ER0 L IY0
+OVERLYING  OW2 V ER0 L AY1 IH0 NG
+OVERMAN  OW1 V ER0 M AH0 N
+OVERMATCH  OW2 V ER0 M AE1 CH
+OVERMATCHED  OW1 V ER0 M AE1 CH T
+OVERMYER  OW1 V ER0 M IY0 ER0
+OVERNIGHT  OW1 V ER0 N AY1 T
+OVERNIGHTER  OW2 V ER0 N AY1 T ER0
+OVERNIGHTERS  OW2 V ER0 N AY1 T ER0 Z
+OVERNITE  OW1 V ER0 N AY1 T
+OVEROPTIMISM  OW2 V ER0 AA1 P T IH0 M IH2 Z AH0 M
+OVERPAID  OW1 V ER0 P EY1 D
+OVERPASS  OW1 V ER0 P AE2 S
+OVERPASSES  OW1 V ER0 P AE2 S IH0 Z
+OVERPAY  OW1 V ER0 P EY2
+OVERPAYING  OW1 V ER0 P EY2 IH0 NG
+OVERPAYMENT  OW1 V ER0 P EY2 M AH0 N T
+OVERPAYMENTS  OW1 V ER0 P EY2 M AH0 N T S
+OVERPECK  OW1 V ER0 P EH2 K
+OVERPLAY  OW1 V ER0 P L EY1
+OVERPLAYED  OW1 V ER0 P L EY1 D
+OVERPLAYING  OW1 V ER0 P L EY1 IH0 NG
+OVERPLAYS  OW1 V ER0 P L EY1 Z
+OVERPOPULATE  OW2 V ER0 P AA1 P Y AH0 L EY0 T
+OVERPOPULATED  OW2 V ER0 P AA1 P Y AH0 L EY0 T IH0 D
+OVERPOPULATES  OW2 V ER0 P AA1 P Y AH0 L EY0 T S
+OVERPOPULATING  OW2 V ER0 P AA1 P Y AH0 L EY0 T IH0 NG
+OVERPOPULATION  OW2 V ER0 P AA2 P Y AH0 L EY1 SH AH0 N
+OVERPOWER  OW2 V ER0 P AW1 ER0
+OVERPOWERED  OW2 V ER0 P AW1 ER0 D
+OVERPOWERING  OW1 V ER0 P AW1 R IH0 NG
+OVERPOWERS  OW2 V ER0 P AW1 ER0 Z
+OVERPRICE  OW2 V ER0 P R AY1 S
+OVERPRICED  OW1 V ER0 P R AY2 S T
+OVERPRICES  OW2 V ER0 P R AY1 S IH0 Z
+OVERPRICING  OW2 V ER0 P R AY1 S IH0 NG
+OVERPRODUCE  OW1 V ER0 P R AH0 D UW1 S
+OVERPRODUCED  OW1 V ER0 P R AH0 D UW1 S T
+OVERPRODUCER  OW1 V ER0 P R AH0 D UW1 S ER0
+OVERPRODUCERS  OW1 V ER0 P R AH0 D UW1 S ER0 Z
+OVERPRODUCING  OW2 V ER0 P R AH0 D Y UW1 S IH0 NG
+OVERPRODUCTION  OW1 V ER0 P R AH0 D AH1 K SH AH0 N
+OVERPROTECT  OW2 V ER0 P R AH0 T EH1 K T
+OVERPROTECTED  OW2 V ER0 P R AH0 T EH1 K T IH0 D
+OVERPROTECTION  OW2 V ER0 P R AH0 T EH1 K SH AH0 N
+OVERPROTECTIVE  OW2 V ER0 P R AH0 T EH1 K T AH0 V
+OVERQUALIFIED  OW1 V ER0 K W AA2 L AH0 F AY2 D
+OVERQUALIFY  OW1 V ER0 K W AA2 L IH0 F AY2
+OVERRAN  OW1 V ER0 R AE1 N
+OVERRATE  OW2 V ER0 R EY1 T
+OVERRATED  OW2 V ER0 R EY1 T IH0 D
+OVERRATES  OW2 V ER0 R EY1 T S
+OVERRATING  OW2 V ER0 R EY1 T IH0 NG
+OVERREACH  OW1 V ER0 R IY2 CH
+OVERREACHED  OW1 V ER0 R IY2 CH T
+OVERREACHES  OW1 V ER0 R IY2 CH IH0 Z
+OVERREACHING  OW1 V ER0 R IY2 CH IH0 NG
+OVERREACT  OW1 V ER0 R IY0 AE1 K T
+OVERREACTED  OW1 V ER0 R IY0 AE2 K T IH0 D
+OVERREACTING  OW1 V ER0 R IY0 AE2 K T IH0 NG
+OVERREACTION  OW1 V ER0 R IY0 AE2 K SH AH0 N
+OVERREACTS  OW1 V ER0 R IY0 AE1 K T S
+OVERREGULATE  OW1 V ER0 R EH1 G Y AH0 L EY2 T
+OVERREGULATED  OW2 V ER0 R EH1 G Y AH0 L EY2 T IH0 D
+OVERREGULATES  OW1 V ER0 R EH1 G Y AH0 L EY2 T S
+OVERREGULATION  OW2 V ER0 R EH2 G Y AH0 L EY1 SH AH0 N
+OVERRELIANCE  OW1 V ER0 R IH0 L AY2 AH0 N S
+OVERREPRESENT  OW1 V ER0 R EH2 P R AH0 Z EH1 N T
+OVERREPRESENTED  OW1 V ER0 R EH2 P R AH0 Z EH1 N T IH0 D
+OVERRIDDEN  OW1 V ER0 R IH1 D AH0 N
+OVERRIDE  OW1 V ER0 R AY2 D
+OVERRIDES  OW1 V ER0 R AY2 D Z
+OVERRIDING  OW1 V ER0 R AY2 D IH0 NG
+OVERRIPE  OW1 V ER0 R AY1 P
+OVERRODE  OW1 V ER0 R OW1 D
+OVERRULE  OW1 V ER0 R UW2 L
+OVERRULED  OW2 V ER0 R UW1 L D
+OVERRULES  OW1 V ER0 R UW2 L Z
+OVERRULING  OW1 V ER0 R UW2 L IH0 NG
+OVERRUN  OW1 V ER0 R AH2 N
+OVERRUNNING  OW1 V ER0 R AH2 N IH0 NG
+OVERRUNS  OW1 V ER0 R AH2 N Z
+OVERS  OW1 V ER0 Z
+OVERSAW  OW1 V ER0 S AO2
+OVERSEA  OW2 V ER0 S IY1
+OVERSEAS  OW1 V ER0 S IY1 Z
+OVERSEE  OW1 V ER0 S IY2
+OVERSEEING  OW1 V ER0 S IY2 IH0 NG
+OVERSEEN  OW1 V ER0 S IY2 N
+OVERSEER  OW1 V ER0 S IY1 ER0
+OVERSEERS  OW2 V ER0 S IY1 ER0 Z
+OVERSEES  OW1 V ER0 S IY2 Z
+OVERSELL  OW1 V ER0 S EH2 L
+OVERSELLING  OW1 V ER0 S EH2 L IH0 NG
+OVERSENSITIVE  OW2 V ER0 S EH1 N S AH0 T IH0 V
+OVERSENSITIVITY  OW2 V ER0 S EH0 N S AH0 T IH1 V IH0 T IY0
+OVERSHADOW  OW1 V ER0 SH AE1 D OW0
+OVERSHADOWED  OW2 V ER0 SH AE1 D OW0 D
+OVERSHADOWING  OW1 V ER0 SH AE1 D OW0 IH0 NG
+OVERSHADOWS  OW1 V ER0 SH AE1 D OW0 Z
+OVERSHOOT  OW1 V ER0 SH UW2 T
+OVERSHOOTING  OW1 V ER0 SH UW2 T IH0 NG
+OVERSHOT  OW1 V ER0 SH AA2 T
+OVERSIGHT  OW1 V ER0 S AY2 T
+OVERSIMPLIFICATION  OW0 V ER0 S IH1 M P L IH0 F IH0 K EY2 SH AH0 N
+OVERSIMPLIFIED  OW0 V ER0 S IH1 M P L IH0 F AY2 D
+OVERSIMPLIFY  OW0 V ER0 S IH1 M P L IH0 F AY2
+OVERSIMPLIFYING  OW0 V ER0 S IH1 M P L IH0 F AY2 IH0 NG
+OVERSIZE  OW2 V ER0 S AY1 Z
+OVERSIZED  OW1 V ER0 S AY2 Z D
+OVERSIZES  OW2 V ER0 S AY1 Z IH0 Z
+OVERSIZING  OW2 V ER0 S AY1 Z IH0 NG
+OVERSLEEP  OW2 V ER0 S L IY1 P
+OVERSLEPT  OW2 V ER0 S L EH1 P T
+OVERSOLD  OW1 V ER0 S OW1 L D
+OVERSON  OW1 V ER0 S AH0 N
+OVERSPEND  OW1 V ER0 S P EH2 N D
+OVERSPENDING  OW1 V ER0 S P EH2 N D IH0 NG
+OVERSPENDS  OW1 V ER0 S P EH2 N D Z
+OVERSPENT  OW1 V ER0 S P EH1 N T
+OVERSTAFF  OW1 V ER0 S T AE2 F
+OVERSTAFFED  OW1 V ER0 S T AE2 F T
+OVERSTATE  OW1 V ER0 S T EY2 T
+OVERSTATED  OW1 V ER0 S T EY2 T IH0 D
+OVERSTATEMENT  OW1 V ER0 S T EY2 T M AH0 N T
+OVERSTATEMENTS  OW1 V ER0 S T EY2 T M AH0 N T S
+OVERSTATES  OW1 V ER0 S T EY2 T S
+OVERSTATING  OW1 V ER0 S T EY2 T IH0 NG
+OVERSTAY  OW2 V ER0 S T EY1
+OVERSTAYED  OW2 V ER0 S T EY1 D
+OVERSTEP  OW1 V ER0 S T EH2 P
+OVERSTEPPED  OW1 V ER0 S T EH2 P T
+OVERSTEPPING  OW1 V ER0 S T EH2 P IH0 NG
+OVERSTOCK  OW1 V ER0 S T AA1 K
+OVERSTOCKED  OW1 V ER0 S T AA1 K T
+OVERSTREET  OW1 V ER0 S T R IY2 T
+OVERSTROM  OW1 V ER0 S T R AA1 M
+OVERSTUFF  OW1 V ER0 S T AH2 F
+OVERSTUFFED  OW1 V ER0 S T AH2 F T
+OVERSUBSCRIBE  OW2 V ER0 S AH0 B S K R AY1 B
+OVERSUBSCRIBED  OW2 V ER0 S AH0 B S K R AY1 B D
+OVERSUPPLIED  OW2 V ER0 S AH0 P L AY1 D
+OVERSUPPLY  OW2 V ER0 S AH0 P L AY1
+OVERT  OW0 V ER1 T
+OVERT(1)  OW1 V ER0 T
+OVERTAKE  OW1 V ER0 T EY2 K
+OVERTAKEN  OW1 V ER0 T EY2 K AH0 N
+OVERTAKES  OW1 V ER0 T EY2 K S
+OVERTAKING  OW1 V ER0 T EY2 K IH0 NG
+OVERTAX  OW1 V ER0 T AE2 K S
+OVERTAXED  OW2 V ER0 T AE1 K S T
+OVERTHREW  OW2 V ER0 TH R UW1
+OVERTHROW  OW1 V ER0 TH R OW2
+OVERTHROWING  OW1 V ER0 TH R OW2 IH0 NG
+OVERTHROWN  OW2 V ER0 TH R OW1 N
+OVERTHROWS  OW1 V ER0 TH R OW2 Z
+OVERTIME  OW1 V ER0 T AY2 M
+OVERTLY  OW0 V ER1 T L IY0
+OVERTON  OW1 V ER0 T AH0 N
+OVERTONE  OW1 V ER0 T OW2 N
+OVERTONES  OW1 V ER0 T OW2 N Z
+OVERTOOK  OW2 V ER0 T UH1 K
+OVERTRAIN  OW0 V ER0 T R EY1 N
+OVERTRAINING  OW0 V ER0 T R EY1 N IH0 NG
+OVERTURE  OW1 V ER0 CH ER0
+OVERTURES  OW1 V ER0 CH UH2 R Z
+OVERTURF  OW1 V ER0 T ER2 F
+OVERTURN  OW1 V ER0 T ER2 N
+OVERTURNED  OW1 V ER0 T ER2 N D
+OVERTURNING  OW1 V ER0 T ER2 N IH0 NG
+OVERTURNS  OW1 V ER0 T ER2 N Z
+OVERUSE  OW2 V ER0 Y UW1 Z
+OVERUSED  OW2 V ER0 Y UW1 Z D
+OVERUSES  OW2 V ER0 Y UW1 Z IH0 Z
+OVERUSING  OW2 V ER0 Y UW1 Z IH0 NG
+OVERVALUATION  OW2 V ER0 V AE2 L Y UW0 EY1 SH AH0 N
+OVERVALUE  OW1 V ER0 V AE2 L Y UW0
+OVERVALUED  OW1 V ER0 V AE2 L Y UW0 D
+OVERVIEW  OW1 V ER0 V Y UW2
+OVERVIEWS  OW1 V ER0 V Y UW2 Z
+OVERWEIGHT  OW2 V ER0 W EY1 T
+OVERWEIGHTED  OW2 V ER0 W EY1 T IH0 D
+OVERWHELM  OW2 V ER0 W EH1 L M
+OVERWHELM(1)  OW2 V ER0 HH W EH1 L M
+OVERWHELMABILITY  OW2 V ER0 W EH1 L M AH0 B IH1 L AH0 T IY0
+OVERWHELMABILITY(1)  OW2 V ER0 HH W EH0 L M AH0 B IH1 L AH0 T IY0
+OVERWHELMED  OW2 V ER0 W EH1 L M D
+OVERWHELMED(1)  OW2 V ER0 HH W EH1 L M D
+OVERWHELMING  OW2 V ER0 W EH1 L M IH0 NG
+OVERWHELMING(1)  OW2 V ER0 HH W EH1 L M IH0 NG
+OVERWHELMINGLY  OW2 V ER0 W EH1 L M IH0 NG L IY0
+OVERWHELMINGLY(1)  OW2 V ER0 HH W EH1 L M IH0 NG L IY0
+OVERWHELMS  OW2 V ER0 W EH1 L M Z
+OVERWHELMS(1)  OW2 V ER0 HH W EH1 L M Z
+OVERWINTER  OW2 V ER0 W IH1 N T ER0
+OVERWORK  OW2 V ER0 W ER1 K
+OVERWORKED  OW2 V ER0 W ER1 K T
+OVERWORKING  OW2 V ER0 W ER1 K IH0 NG
+OVERWORKS  OW2 V ER0 W ER1 K S
+OVERWRITE  OW2 V ER0 R AY1 T
+OVERWRITES  OW2 V ER0 R AY1 T S
+OVERWRITING  OW2 V ER0 R AY1 T IH0 NG
+OVERWRITTEN  OW2 V ER0 R IH1 T AH0 N
+OVERWROTE  OW2 V ER0 R OW1 T
+OVERWROUGHT  OW2 V ER0 R AO1 T
+OVERY  OW1 V ER0 IY0
+OVERZEALOUS  OW2 V ER0 Z EH1 L AH0 S
+OVERZEALOUSLY  OW2 V ER0 Z EH1 L AH0 S L IY0
+OVESON  AA1 V AH0 S AH0 N
+OVETT  OW2 V EH1 T
+OVETT'S  OW2 V EH1 T S
+OVEX  OW1 V EH0 K S
+OVIATT  OW1 V IY0 AA2 T
+OVIATT'S  OW1 V IY0 AA2 T S
+OVIEDO  OW0 V IY0 EY1 D OW0
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+RESTIVO  R EH0 S T IY1 V OW0
+RESTLESS  R EH1 S T L AH0 S
+RESTLESSLY  R EH1 S T L AH0 S L IY0
+RESTLESSNESS  R EH1 S T L AH0 S N AH0 S
+RESTO  R EH1 S T OW0
+RESTOCK  R IY0 S T AA1 K
+RESTOCKED  R IY0 S T AA1 K T
+RESTOCKING  R IY0 S T AA1 K IH0 NG
+RESTON  R EH1 S T AH0 N
+RESTORATION  R EH2 S T ER0 EY1 SH AH0 N
+RESTORATIONS  R EH2 S T ER0 EY1 SH AH0 N Z
+RESTORATIVE  R AH0 S T AO1 R AH0 T IH0 V
+RESTORE  R IH0 S T AO1 R
+RESTORED  R IH0 S T AO1 R D
+RESTORER  R IH0 S T AO1 R ER0
+RESTORES  R IH0 S T AO1 R Z
+RESTORING  R IH0 S T AO1 R IH0 NG
+RESTRAIN  R IY0 S T R EY1 N
+RESTRAINED  R IY0 S T R EY1 N D
+RESTRAINING  R IY0 S T R EY1 N IH0 NG
+RESTRAINS  R IY0 S T R EY1 N Z
+RESTRAINT  R IH0 S T R EY1 N T
+RESTRAINT(1)  R IY0 S T R EY1 N T
+RESTRAINTS  R IH0 S T R EY1 N T S
+RESTRAINTS(1)  R IY0 S T R EY1 N T S
+RESTREPO  R EH0 S T R EH1 P OW0
+RESTRICT  R IY0 S T R IH1 K T
+RESTRICTED  R IY0 S T R IH1 K T AH0 D
+RESTRICTED(1)  R IY0 S T R IH1 K T IH0 D
+RESTRICTING  R IY0 S T R IH1 K T IH0 NG
+RESTRICTION  R IY0 S T R IH1 K SH AH0 N
+RESTRICTIONS  R IY0 S T R IH1 K SH AH0 N Z
+RESTRICTIVE  R IY0 S T R IH1 K T IH0 V
+RESTRICTIVENESS  R AH0 S T R IH1 K T IH0 V N AH0 S
+RESTRICTS  R IY0 S T R IH1 K T S
+RESTROOM  R EH1 S T R UW2 M
+RESTROOMS  R EH1 S T R UW2 M Z
+RESTRUCTURE  R IY0 S T R AH1 K CH ER0
+RESTRUCTURED  R IY0 S T R AH1 K CH ER0 D
+RESTRUCTURES  R IY0 S T R AH1 K CH ER0 Z
+RESTRUCTURING  R IY0 S T R AH1 K CH ER0 IH0 NG
+RESTRUCTURINGS  R IY0 S T R AH1 K CH ER0 IH0 NG Z
+RESTS  R EH1 S T S
+RESTYLE  R IY0 S T AY1 L
+RESTYLED  R IY0 S T AY1 L D
+RESUBMIT  R IY2 S AH0 B M IH1 T
+RESUBMITTED  R IY2 S AH0 B M IH1 T IH0 D
+RESUBMITTING  R IY2 S AH0 B M IH1 T IH0 NG
+RESULT  R IH0 Z AH1 L T
+RESULT(1)  R IY0 Z AH1 L T
+RESULTANT  R IY0 Z AH1 L T AH0 N T
+RESULTED  R IH0 Z AH1 L T IH0 D
+RESULTED(1)  R IY0 Z AH1 L T AH0 D
+RESULTED(2)  R IY0 Z AH1 L T IH0 D
+RESULTING  R IH0 Z AH1 L T IH0 NG
+RESULTING(1)  R IY0 Z AH1 L T IH0 NG
+RESULTS  R IH0 Z AH1 L T S
+RESULTS(1)  R IY0 Z AH1 L T S
+RESUME  R IH0 Z UW1 M
+RESUME(1)  R IY0 Z UW1 M
+RESUME(2)  R EH1 Z AH0 M EY2
+RESUMED  R IH0 Z UW1 M D
+RESUMED(1)  R IY0 Z UW1 M D
+RESUMES  R IH0 Z UW1 M Z
+RESUMES(1)  R IY0 Z UW1 M Z
+RESUMES(2)  R EH1 Z AH0 M EY2 Z
+RESUMING  R IH0 Z UW1 M IH0 NG
+RESUMING(1)  R IY0 Z UW1 M IH0 NG
+RESUMPTION  R IH0 Z AH1 M P SH AH0 N
+RESUMPTION(1)  R IY0 Z AH1 M P SH AH0 N
+RESUMPTION(2)  R IH0 Z AH1 M SH AH0 N
+RESUMPTION(3)  R IY0 Z AH1 M SH AH0 N
+RESUPPLY  R IY0 S AH0 P L AY1
+RESURFACE  R IY0 S ER1 F AH0 S
+RESURFACED  R IY0 S ER1 F AH0 S T
+RESURFACING  R IY0 S ER1 F AH0 S IH0 NG
+RESURGENCE  R IY0 S ER1 JH AH0 N S
+RESURGENCY  R IH0 S ER1 JH AH0 N S IY0
+RESURGENCY(1)  R IY0 S ER1 JH AH0 N S IY0
+RESURGENT  R IH0 S ER1 JH AH0 N T
+RESURGENT(1)  R IY0 S ER1 JH AH0 N T
+RESURGING  R IY0 S ER1 JH IH0 NG
+RESURRECT  R EH2 Z ER0 EH1 K T
+RESURRECTED  R EH2 Z ER0 EH1 K T IH0 D
+RESURRECTING  R EH2 Z ER0 EH1 K T IH0 NG
+RESURRECTION  R EH2 Z ER0 EH1 K SH AH0 N
+RESUSCITATE  R IH0 S AH1 S IH0 T EY2 T
+RESUSCITATE(1)  R IY0 S AH1 S IH0 T EY2 T
+RESUSCITATED  R IH0 S AH1 S IH0 T EY2 T IH0 D
+RESUSCITATING  R IH0 S AH1 S IH0 T EY2 T IH0 NG
+RESUSCITATION  R IH0 S AH2 S IH0 T EY1 SH AH0 N
+RESVERATROL  R EH0 S V ER1 AH0 T R AO0 L
+RET  R EH1 T
+RETA  R EH1 T AH0
+RETABLOS  R IY0 T AE1 B L OW0 S
+RETAIL  R IY1 T EY2 L
+RETAILED  R IY1 T EY2 L D
+RETAILER  R IY1 T EY2 L ER0
+RETAILER'S  R IY1 T EY2 L ER0 Z
+RETAILERS  R IY1 T EY2 L ER0 Z
+RETAILERS'  R IY1 T EY2 L ER0 Z
+RETAILING  R IY1 T EY2 L IH0 NG
+RETAILING'S  R IY1 T EY2 L IH0 NG Z
+RETAILS  R IY1 T EY2 L Z
+RETAIN  R IH0 T EY1 N
+RETAIN(1)  R IY0 T EY1 N
+RETAINED  R IH0 T EY1 N D
+RETAINED(1)  R IY0 T EY1 N D
+RETAINER  R IH0 T EY1 N ER0
+RETAINER(1)  R IY0 T EY1 N ER0
+RETAINERS  R IH0 T EY1 N ER0 Z
+RETAINING  R IH0 T EY1 N IH0 NG
+RETAINING(1)  R IY0 T EY1 N IH0 NG
+RETAINS  R IH0 T EY1 N Z
+RETAINS(1)  R IY0 T EY1 N Z
+RETAKE  R IY1 T EY1 K
+RETAKE(1)  R IY0 T EY1 K
+RETAKEN  R IY0 T EY1 K AH0 N
+RETAKING  R IY0 T EY1 K IH0 NG
+RETALIATE  R IH0 T AE1 L IY0 EY2 T
+RETALIATE(1)  R IY0 T AE1 L IY0 EY2 T
+RETALIATED  R IH0 T AE1 L IY0 EY2 T IH0 D
+RETALIATED(1)  R IY0 T AE1 L IY0 EY2 T IH0 D
+RETALIATING  R IH0 T AE1 L IY0 EY2 T IH0 NG
+RETALIATION  R IY0 T AE2 L IY0 EY1 SH AH0 N
+RETALIATORY  R IY0 T AE1 L Y AH0 T AO2 R IY0
+RETANA  R EH0 T AE1 N AH0
+RETARD  R IH0 T AA1 R D
+RETARD(1)  R IY0 T AA1 R D
+RETARDANT  R IY0 T AA1 R D AH0 N T
+RETARDATION  R IY0 T AA0 R D EY1 SH AH0 N
+RETARDED  R IH0 T AA1 R D IH0 D
+RETARDED(1)  R IY0 T AA1 R D AH0 D
+RETARDED(2)  R IY0 T AA1 R D IH0 D
+RETARDING  R IH0 T AA1 R D IH0 NG
+RETARDING(1)  R IY0 T AA1 R D IH0 NG
+RETARDS  R IH0 T AA1 R D Z
+RETARDS(1)  R IY0 T AA1 R D Z
+RETELL  R IY0 T EH1 L
+RETELLING  R IY0 T EH1 L IH0 NG
+RETEMEYER  R EH1 T AH0 M AY2 R
+RETENTION  R IY0 T EH1 N SH AH0 N
+RETEST  R IY1 T EH1 S T
+RETESTED  R IY0 T EH1 S T IH0 D
+RETESTING  R IY0 T EH1 S T IH0 NG
+RETESTS  R IY1 T EH1 S T S
+RETHERFORD  R IH0 TH ER1 F ER0 D
+RETHINK  R IY0 TH IH1 NG K
+RETHINKING  R IY0 TH IH1 NG K IH0 NG
+RETHOUGHT  R IY0 TH AO1 T
+RETI  R EH1 T IY0
+RETICENCE  R EH1 T IH0 S AH0 N S
+RETICENT  R EH1 T IH0 S AH0 N T
+RETIN  R EH1 T IH0 N
+RETINA  R EH1 T AH0 N AH0
+RETINAL  R EH1 T AH0 N AH0 L
+RETINOBLASTOMA  R EH2 T IH0 N OW2 B L AE2 S T OW1 M AH0
+RETINOID  R EH1 T IH0 N OY0 D
+RETINOIDS  R EH1 T IH0 N OY0 D Z
+RETINOL  R EH1 T IH0 N AO2 L
+RETINUE  R EH1 T AH0 N UW2
+RETINYL  R EH1 T IH0 N AH0 L
+RETIRE  R IH0 T AY1 R
+RETIRE(1)  R IY0 T AY1 R
+RETIRE(2)  R IY2 T AY1 ER0
+RETIRED  R IH0 T AY1 R D
+RETIRED(1)  R IY0 T AY1 ER0 D
+RETIRED(2)  R IY0 T AY1 R D
+RETIREE  R IY0 T AY1 R IY1
+RETIREE'S  R IH0 T AY2 R IY1 Z
+RETIREES  R IY0 T AY1 R IY1 Z
+RETIREES'  R IH0 T AY2 R IY1 Z
+RETIREMENT  R IY0 T AY1 ER0 M AH0 N T
+RETIREMENT(1)  R IH0 T AY1 ER0 M AH0 N T
+RETIREMENT'S  R IH0 T AY1 R M AH0 N T S
+RETIREMENT'S(1)  R IY0 T AY1 R M AH0 N T S
+RETIREMENTS  R IH0 T AY1 R M AH0 N T S
+RETIREMENTS(1)  R IY0 T AY1 R M AH0 N T S
+RETIRES  R IH0 T AY1 R Z
+RETIRES(1)  R IY0 T AY1 ER0 Z
+RETIRES(2)  R IY0 T AY1 R Z
+RETIRING  R IH0 T AY1 R IH0 NG
+RETIRING(1)  R IY0 T AY1 ER0 IH0 NG
+RETIRING(2)  R IY0 T AY1 R IH0 NG
+RETLIN  R EH1 T L IH0 N
+RETO  R IY1 T UW1
+RETOOK  R IY0 T UH1 K
+RETOOL  R IY0 T UW1 L
+RETOOLED  R IY0 T UW1 L D
+RETOOLING  R IY0 T UW1 L IH0 NG
+RETORT  R IY1 T AO2 R T
+RETORTED  R IY0 T AO1 R T IH0 D
+RETORTS  R IH0 T AO1 R T S
+RETORTS(1)  R IY0 T AO1 R T S
+RETOUCHING  R IY0 T AH1 CH IH0 NG
+RETRACE  R IY0 T R EY1 S
+RETRACED  R IY0 T R EY1 S T
+RETRACEMENT  R IY0 T R EY1 S M AH0 N T
+RETRACING  R IY0 T R EY1 S IH0 NG
+RETRACT  R IY0 T R AE1 K T
+RETRACTABLE  R IY0 T R AE1 K T AH0 B AH0 L
+RETRACTED  R IY0 T R AE1 K T AH0 D
+RETRACTING  R IY0 T R AE1 K T IH0 NG
+RETRACTION  R IY0 T R AE1 K SH AH0 N
+RETRACTS  R IY0 T R AE1 K T S
+RETRAIN  R IY0 T R EY1 N
+RETRAINED  R IY0 T R EY1 N D
+RETRAINING  R IY0 T R EY1 N IH0 NG
+RETRANSMISSION  R IY2 T R AE0 N Z M IH1 SH AH0 N
+RETREAD  R IY0 T R EH1 D
+RETREADING  R IY0 T R EH1 D IH0 NG
+RETREADS  R IY0 T R EH1 D Z
+RETREAT  R IY0 T R IY1 T
+RETREATED  R IY0 T R IY1 T AH0 D
+RETREATED(1)  R IY0 T R IY1 T IH0 D
+RETREATING  R IY0 T R IY1 T IH0 NG
+RETREATS  R IY0 T R IY1 T S
+RETRENCH  R IY0 T R EH1 N CH
+RETRENCHED  R IY0 T R EH1 N CH T
+RETRENCHING  R IY0 T R EH1 N CH IH0 NG
+RETRENCHMENT  R IY0 T R EH1 N CH M AH0 N T
+RETRENCHMENTS  R IY0 T R EH1 N CH M AH0 N T S
+RETRIAL  R IY0 T R AY1 AH0 L
+RETRIBUTION  R EH2 T R AH0 B Y UW1 SH AH0 N
+RETRIED  R IY0 T R AY1 D
+RETRIEVAL  R IH0 T R IY1 V AH0 L
+RETRIEVAL(1)  R IY0 T R IY1 V AH0 L
+RETRIEVE  R IH0 T R IY1 V
+RETRIEVE(1)  R IY0 T R IY1 V
+RETRIEVED  R IY0 T R IY1 V D
+RETRIEVER  R IY0 T R IY1 V ER0
+RETRIEVERS  R IY0 T R IY1 V ER0 Z
+RETRIEVES  R IH0 T R IY1 V Z
+RETRIEVES(1)  R IY0 T R IY1 V Z
+RETRIEVING  R IY0 T R IY1 V IH0 NG
+RETRO  R EH1 T R OW0
+RETROACTIVE  R EH2 T R OW0 AE1 K T IH0 V
+RETROACTIVELY  R EH2 T R OW0 AE1 K T IH0 V L IY0
+RETROACTIVITY  R EH2 T R OW0 AE0 K T IH1 V IH0 T IY0
+RETROCESSION  R EH2 T R OW0 S EH1 SH AH0 N
+RETROCESSIONARIES  R EH2 T R OW0 S EH1 SH AH0 N EH0 R IY0 Z
+RETROCESSIONARY  R EH2 T R OW0 S EH1 SH AH0 N EH0 R IY0
+RETROFIT  R EH1 T R OW0 F IH2 T
+RETROFITS  R EH1 T R OW0 F IH2 T S
+RETROFITTED  R EH1 T R OW2 F IH2 T IH0 D
+RETROFITTER  R EH1 T R OW2 F IH2 T ER0
+RETROFITTING  R EH1 T R OW0 F IH2 T IH0 NG
+RETROGRADE  R EH1 T R AH0 G R EY2 D
+RETROSPECT  R EH1 T R AH0 S P EH2 K T
+RETROSPECTIVE  R EH2 T R AH0 S P EH1 K T IH0 V
+RETROSPECTIVELY  R EH2 T R OW0 S P EH1 K T IH0 V L IY0
+RETROVIR  R EH1 T R OW0 V IH2 R
+RETROVIRUS  R EH2 T R OW0 V AY1 R AH0 S
+RETROVIRUSES  R EH2 T R OW0 V AY1 R AH0 S IH0 Z
+RETRY  R IY0 T R AY1
+RETRYING  R IY0 T R AY1 IH0 NG
+RETTBERG  R EH1 T B ER0 G
+RETTER  R EH1 T ER0
+RETTEW  R EH1 CH UW0
+RETTIG  R EH1 T IH0 G
+RETTINGER  R EH1 T IH0 N JH ER0
+RETTINGER(1)  R EH1 T IH0 NG ER0
+RETTKE  R EH1 T K IY0
+RETTON  R EH1 T AH0 N
+RETURN  R IH0 T ER1 N
+RETURN(1)  R IY0 T ER1 N
+RETURNABLE  R IY0 T ER1 N AH0 B AH0 L
+RETURNED  R IH0 T ER1 N D
+RETURNED(1)  R IY0 T ER1 N D
+RETURNEE  R IH0 T ER0 N IY1
+RETURNEES  R IH0 T ER0 N IY1 Z
+RETURNING  R IH0 T ER1 N IH0 NG
+RETURNING(1)  R IY0 T ER1 N IH0 NG
+RETURNS  R IH0 T ER1 N Z
+RETURNS(1)  R IY0 T ER1 N Z
+RETURNS'  R AH0 T ER1 N Z
+RETURNS'(1)  R IY0 T ER1 N Z
+RETZ  R EH1 T S
+RETZER  R EH1 T Z ER0
+RETZLAFF  R EH1 T Z L AH0 F
+RETZLOFF  R EH1 T Z L AO0 F
+REUBEN  R UW1 B AH0 N
+REUBER  R OY1 B ER0
+REUL  R UW1 L
+REULAND  R OY1 L AH0 N D
+REULE  R UW1 L
+REUM  R IY1 AH0 M
+REUNIFICATION  R IY0 UW2 N AH0 F AH0 K EY1 SH AH0 N
+REUNIFIED  R IY0 UW1 N AH0 F AY2 D
+REUNIFY  R IY0 UW1 N AH0 F AY2
+REUNION  R IY0 UW1 N Y AH0 N
+REUNIONS  R IY0 UW1 N Y AH0 N Z
+REUNITE  R IY2 UW0 N AY1 T
+REUNITED  R IY2 UW0 N AY1 T IH0 D
+REUNITES  R IY2 UW0 N AY1 T S
+REUNITING  R IY2 UW0 N AY1 T IH0 NG
+REUSABLE  R IY0 UW1 Z AH0 B AH0 L
+REUSCH  R OY1 SH
+REUSE  R IY0 Y UW1 S
+REUSE(1)  R IY0 Y UW1 Z
+REUSED  R IY0 UW1 Z D
+REUSING  R IY0 Y UW1 Z IH0 NG
+REUSS  R UW1 S
+REUSSER  R OY1 S ER0
+REUST  R UW1 S T
+REUTER  R OY1 T ER0
+REUTER'S  R OY1 T ER0 Z
+REUTERS  R OY1 T ER0 Z
+REUTERS'  R OY1 T ER0 Z
+REUTERS'S  R OY1 T ER0 Z IH0 Z
+REUTERS'S(1)  R OY1 T ER0 Z
+REUTGERS  R OY1 T G ER0 Z
+REUTHER  R OY1 DH ER0
+REUTTER  R OY1 T ER0
+REUTZEL  R OY1 T Z AH0 L
+REV  R EH1 V
+REVA  R EY1 V AH0
+REVAK  R EH1 V AH0 K
+REVALUATION  R IY0 V AE1 L Y UW0 EY1 SH AH0 N
+REVALUATIONS  R IY0 IH0 V AE2 L Y UW0 EY1 SH AH0 N Z
+REVALUE  R IY0 V AE1 L Y UW2
+REVALUED  R IY0 V AE1 L Y UW0 D
+REVALUING  R IY0 V AE1 L Y UW0 IH0 NG
+REVAMP  R IY0 V AE1 M P
+REVAMPED  R IY0 V AE1 M P T
+REVAMPING  R IY0 V AE1 M P IH0 NG
+REVAMPS  R IY0 V AE1 M P S
+REVARD  R IH0 V AA1 R D
+REVCO  R EH1 V K OW0
+REVCO'S  R EH1 V K OW0 Z
+REVEAL  R IH0 V IY1 L
+REVEAL(1)  R IY0 V IY1 L
+REVEALED  R IH0 V IY1 L D
+REVEALED(1)  R IY0 V IY1 L D
+REVEALING  R IH0 V IY1 L IH0 NG
+REVEALING(1)  R IY0 V IY1 L IH0 NG
+REVEALS  R IH0 V IY1 L Z
+REVEALS(1)  R IY0 V IY1 L Z
+REVEILLE  R EH1 V AH0 L IY2
+REVEL  R EH1 V AH0 L
+REVELATION  R EH2 V AH0 L EY1 SH AH0 N
+REVELATIONS  R EH2 V AH0 L EY1 SH AH0 N Z
+REVELATORY  R IH0 V EH1 L AH0 T AO2 R IY0
+REVELED  R EH1 V AH0 L D
+REVELER  R EH1 V AH0 L ER0
+REVELERS  R EH1 V AH0 L ER0 Z
+REVELES  R EY0 V EY1 L EH0 S
+REVELING  R EH1 V AH0 L IH0 NG
+REVELL  R EY0 V EY1 L
+REVELLE  R IH0 V EH1 L
+REVELLER  R EH1 V AH0 L ER0
+REVELLERS  R EH1 V AH0 L ER0 Z
+REVELLO  R EH0 V EH1 L OW0
+REVELRY  R EH1 V AH0 L R IY0
+REVELS  R EH1 V AH0 L Z
+REVENGE  R IY0 V EH1 N JH
+REVENUE  R EH1 V AH0 N UW2
+REVENUE(1)  R EH1 V AH0 N Y UW2
+REVENUER  R EH1 V AH0 N UW2 ER0
+REVENUER(1)  R EH1 V AH0 N Y UW2 ER0
+REVENUERS  R EH1 V AH0 N UW2 ER0 Z
+REVENUERS(1)  R EH1 V AH0 N Y UW2 ER0 Z
+REVENUES  R EH1 V AH0 N UW2 Z
+REVENUES(1)  R EH1 V AH0 N Y UW2 Z
+REVERB  R IY0 V ER1 B
+REVERBED  R IY0 V ER1 B D
+REVERBERATE  R IH0 V ER1 B ER0 AH0 T
+REVERBERATE(1)  R IH0 V ER1 B ER0 EY0 T
+REVERBERATED  R IH0 V ER1 B ER0 EY2 T IH0 D
+REVERBERATED(1)  R IY0 V ER1 B ER0 EY2 T AH0 D
+REVERBERATED(2)  R IY0 V ER1 B ER0 EY2 T IH0 D
+REVERBERATES  R IH0 V ER1 B ER0 EY2 T S
+REVERBERATING  R IH0 V ER1 B ER0 EY2 T IH0 NG
+REVERBERATING(1)  R IY0 V ER1 B ER0 EY2 T IH0 NG
+REVERBERATION  R IY0 V ER2 B ER0 EY1 SH AH0 N
+REVERBERATIONS  R IH0 V ER2 B ER0 EY1 SH AH0 N Z
+REVERCOMB  R EH1 V ER0 K AH0 M
+REVERE  R IH0 V IH1 R
+REVERED  R IH0 V IH1 R D
+REVERENCE  R EH1 V ER0 AH0 N S
+REVEREND  R EH1 V ER0 AH0 N D
+REVEREND(1)  R EH1 V R AH0 N D
+REVERENT  R EH1 V ER0 AH0 N T
+REVERENTIAL  R EH2 V ER0 EH1 N SH AH0 L
+REVERENTLY  R EH1 V ER0 AH0 N T L IY0
+REVERES  R IH0 V IH1 R Z
+REVERIE  R EH1 V ER0 IY0
+REVERIES  R EH1 V ER0 IY0 Z
+REVERING  R IH0 V IH1 R IH0 NG
+REVERON  R EH1 V ER0 AA2 N
+REVERON'S  R EH1 V ER0 AA2 N Z
+REVERSAL  R IH0 V ER1 S AH0 L
+REVERSAL(1)  R IY0 V ER1 S AH0 L
+REVERSALS  R IH0 V ER1 S AH0 L Z
+REVERSALS(1)  R IY0 V ER1 S AH0 L Z
+REVERSE  R IH0 V ER1 S
+REVERSE(1)  R IY0 V ER1 S
+REVERSED  R IH0 V ER1 S T
+REVERSED(1)  R IY0 V ER1 S T
+REVERSER  R IH0 V ER1 S ER0
+REVERSERS  R IH0 V ER1 S ER0 Z
+REVERSES  R IH0 V ER1 S IH0 Z
+REVERSES(1)  R IY0 V ER1 S AH0 Z
+REVERSES(2)  R IY0 V ER1 S IH0 Z
+REVERSIBLE  R IH0 V ER1 S AH0 B AH0 L
+REVERSIBLE(1)  R IY0 V ER1 S AH0 B AH0 L
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+SHIFLET  SH IH1 F L IH0 T
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+SHIFT  SH IH1 F T
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+SPRINKLES  S P R IH1 NG K AH0 L Z
+SPRINKLING  S P R IH1 NG K L IH0 NG
+SPRINKLING(1)  S P R IH1 NG K AH0 L IH0 NG
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+SPRINT'S  S P R IH1 N T S
+SPRINTED  S P R IH1 N T IH0 D
+SPRINTER  S P R IH1 N T ER0
+SPRINTERS  S P R IH1 N T ER0 Z
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+SPRITE  S P R AY1 T
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+SPYCHALSKI  S P IH0 HH AA1 L S K IY0
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+SPYKER  S P AY1 K ER0
+SPYWARE  S P AY1 W EH2 R
+SQL  EH2 S K Y UW2 EH1 L
+SQL(1)  S IY1 K W UH0 L
+SQUABBLE  S K W AA1 B AH0 L
+SQUABBLED  S K W AA1 B AH0 L D
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+SQUADRON  S K W AA1 D R AH0 N
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+SQUANDERING  S K W AA1 N D ER0 IH0 NG
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+SQUARING  S K W EH1 R IH0 NG
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+SQUATS  S K W AA1 T S
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+SQUATTERS  S K W AA1 T ER0 Z
+SQUATTING  S K W AA1 T IH0 NG
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+SQUAWK  S K W AO1 K
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+SQUAWKS  S K W AO1 K S
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+SQUEAKING  S K W IY1 K IH0 NG
+SQUEAKS  S K W IY1 K S
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+SQUEEGEE  S K W IY1 JH IY0
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+SQUEEZING  S K W IY1 Z IH0 NG
+SQUELCH  S K W EH1 L CH
+SQUELCHED  S K W EH1 L CH T
+SQUELCHING  S K W EH1 L CH IH0 NG
+SQUIBB  S K W IH1 B
+SQUIBB'S  S K W IH1 B Z
+SQUID  S K W IH1 D
+SQUIER  S K W AY1 ER0
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+SQUIGGLE  S K W IH1 G AH0 L
+SQUIGGLES  S K W IH1 G AH0 L Z
+SQUILLACE  S K W IH1 L AH0 S
+SQUILLACOTE  S K W IH1 L AH0 K OW0 T
+SQUILLACOTE'S  S K W IH1 L AH0 K OW0 T S
+SQUILLANTE  S K W IY0 L AA1 N T EY0
+SQUINT  S K W IH1 N T
+SQUINTING  S K W IH1 N T IH0 NG
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+SQUIRM  S K W ER1 M
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+SQUISHY  S K W IH1 SH IY0
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+SR  S IY1 N Y ER0
+SR(1)  S IH1 S T ER0
+SRADER  SH R EY1 D ER0
+SRAM  SH R AE1 M
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+SRDAN  S ER1 D AE2 N
+SREBRENICA  S R EY2 B R EH0 N IY1 T S AH0
+SREBRENICA(1)  SH R EY2 B R AH0 N IY1 T S AH0
+SREBRENICA'S  S R EY2 B R EH0 N IY1 T S AH0 Z
+SREBRENICA'S(1)  SH R EY2 B R AH0 N IY1 T S AH0 Z
+SREMAC  S R IY1 M AE2 K
+SRI  SH R IY1
+SRI(1)  EH1 S AA1 R AY1
+SRI(2)  S R IY1
+SRI-LANKA  S R IY1 L AE1 NG K AH0
+SRI-LANKA(1)  SH R IY1 L AE1 NG K AH0
+SRINAGAR  S R IY1 N AH0 G AA2 R
+SRINAGAR(1)  SH R IY1 N AH0 G AA2 R
+SRINAGARB  S R IY1 N AH0 G AA2 R B
+SRINAGARB(1)  SH R IY1 N AH0 G AA2 R B
+SRINIVAS  S R IY1 N IH0 V AA2 S
+SRINIVASAN  S R IY0 N IY2 V AA0 S AA1 N
+SRINIVASAN(1)  SH R IY0 N IY2 V AA0 S AA1 N
+SRIRAM  S R IY1 R AA2 M
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+SRIVASTAVA  S R IY0 V AA0 S T AA1 V AH0
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+ST-JOHNS  S EY1 N T JH AA1 N Z
+ST.  S T R IY1 T
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+STA  S T AH1
+STA(1)  EH1 S T IY1 EY1
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+STABILIZATION  S T EY2 B AH0 L AH0 Z EY1 SH AH0 N
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+STABILIZED  S T EY1 B AH0 L AY2 Z D
+STABILIZER  S T EY1 B AH0 L AY2 Z ER0
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+STABILIZES  S T EY1 B AH0 L AY2 Z AH0 Z
+STABILIZES(1)  S T EY1 B AH0 L AY2 Z IH0 Z
+STABILIZING  S T EY1 B AH0 L AY2 Z IH0 NG
+STABLE  S T EY1 B AH0 L
+STABLER  S T EY1 B AH0 L ER0
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+STABLEST  S T EY1 B AH0 L AH0 S T
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+STAFFORDSHIRE  S T AE1 F ER0 D SH ER0
+STAFFORDSHIRE(1)  S T AE1 F ER0 D SH AY2 ER0
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+STAFFORDSHIRE'S(1)  S T AE1 F ER0 D SH AY2 ER0 Z
+STAFFS  S T AE1 F S
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+STAGFLATE  S T AE2 G F L EY1 T
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+STAKE  S T EY1 K
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+STALIN  S T AA1 L AH0 N
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+STALK  S T AO1 K
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+STALKER  S T AO1 K ER0
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+STALKING  S T AO1 K IH0 NG
+STALKS  S T AO1 K S
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+STALLING  S T AO1 L IH0 NG
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+STALLS  S T AO1 L Z
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+STAMANT  S T AE1 M AH0 N T
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+STAMP  S T AE1 M P
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+STAMPEDE  S T AE0 M P IY1 D
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+STAN  S T AE1 N
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+STANBERY  S T AE1 N B ER0 IY0
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+STANCE  S T AE1 N S
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+STANCH  S T AE1 N CH
+STANCHED  S T AE1 N CH T
+STANCHFIELD  S T AE1 N CH F IY0 L D
+STANCHING  S T AE1 N CH IH0 NG
+STANCIK  S T AE1 N S IH0 K
+STANCIL  S T AE1 N S IH0 L
+STANCILL  S T AE1 N S IH0 L
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+STANCO  S T AE1 NG K OW0
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+STANCZYK  S T AE1 N CH IH0 K
+STAND  S T AE1 N D
+STAND-BY  S T AE1 N D B AY2
+STAND-UP  S T AE1 N D AH2 P
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+STANDARDIZATION  S T AE0 N D ER0 D IH0 Z EY1 SH AH0 N
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+STANDARDIZED  S T AE1 N D ER0 D AY2 Z D
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+STANDARDS  S T AE1 N D ER0 D Z
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+STANDEFER  S T AE1 N D IH0 F ER0
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+STANDER  S T AE1 N D ER0
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+STANDERFORD  S T AE1 N D ER0 F ER0 D
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+STANDIFER  S T AE1 N D IH0 F ER0
+STANDIFORD  S T AE1 N D IH0 F ER0 D
+STANDIN'  S T AE1 N D IH0 N
+STANDING  S T AE1 N D IH0 NG
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+STANDOFFS  S T AE1 N D AO2 F S
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+STANDRIDGE  S T AE1 N D R IH0 JH
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+STANG  S T AE1 NG
+STANGA  S T AA1 NG G AH0
+STANGE  S T AE1 N JH
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+STANGELAND  S T EY1 NG G IH0 L AH0 N D
+STANGELAND(1)  S T EY1 NG G L AH0 N D
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+STANGER  S T AE1 NG ER0
+STANGL  S T AE1 NG G AH0 L
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+STANGLER(1)  S T AE1 NG G L ER0
+STANGO  S T AA1 NG G OW0
+STANHOPE  S T AE1 N HH OW2 P
+STANIAR  S T EY1 N IY0 AA0 R
+STANICH  S T AE1 N IH0 CH
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+STANIS  S T AE1 N IH0 S
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+STANISLAS  S T AE1 N IH0 S L AA0 S
+STANISLAV  S T AE1 N IH0 S L AA0 V
+STANISLAW  S T AE1 N IH0 S L AO2
+STANISLAWSKI  S T AE2 N IH0 S L AA1 F S K IY2
+STANISZEWSKI  S T AE2 N IH0 SH EH1 F S K IY2
+STANK  S T AE1 NG K
+STANKE  S T AE1 NG K
+STANKEVICH  S T AE1 NG K AH0 V IH2 CH
+STANKEY  S T AE1 NG K IY0
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+STANKOVICH  S T AE1 NG K AH0 V IH0 CH
+STANKOWSKI  S T AH0 NG K AO1 F S K IY0
+STANKUS  S T AE1 NG K AH0 S
+STANLEIGH  S T AE1 N L AH0
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+STANLEY'S  S T AE1 N L IY0 Z
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+STANN  S T AE1 N
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+STANZA  S T AE1 N Z AH0
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+STANZIONE  S T AA0 N Z IY0 OW1 N IY0
+STAPEL  S T AE1 P AH0 L
+STAPF  S T AE1 P F
+STAPH  S T AE1 F
+STAPHYLOCOCCUS  S T AE2 F AH0 L AH0 K AO1 K AH0 S
+STAPLE  S T EY1 P AH0 L
+STAPLED  S T EY1 P AH0 L D
+STAPLER  S T EY1 P AH0 L ER0
+STAPLER(1)  S T EY1 P L ER0
+STAPLERS  S T EY1 P AH0 L ER0 Z
+STAPLERS(1)  S T EY1 P L ER0 Z
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+STAPLEY  S T AE1 P L IY0
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+STAPP  S T AE1 P
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+STAR'S  S T AA1 R Z
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+STARBIRD  S T AA1 R B ER2 D
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+STARBUCK  S T AA1 R B AH2 K
+STARBUCK'S  S T AA1 R B AH2 K S
+STARBUCKS  S T AA1 R B AH2 K S
+STARCEVICH  S T AA1 R S IH0 V IH0 CH
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+STARCHLIKE  S T AA1 R CH L AY2 K
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+STARCK  S T AA1 R K
+STARCKMANN  S T AA1 R K M AH0 N
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+STARCROSS  S T AA1 R K R AO2 S
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+STARDOM  S T AA1 R D AH0 M
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+STARFLEET'S  S T AA1 R F IY2 T S
+STARGATE  S T AA1 R G EY2 T
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+STARGAZERS  S T AA1 R G EY2 Z ER0 Z
+STARIN  S T AE1 R IH0 N
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+STARLET  S T AA1 R L AH0 T
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+STARODUBSTEV  S T AA2 R OW0 D AH1 B S T EH2 V
+STARON  S T AE1 R AH0 N
+STARPLEX  S T AA1 R P L EH2 K S
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+STARRETT  S T AE1 R EH0 T
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+STARTING  S T AA1 R T IH0 NG
+STARTLE  S T AA1 R T AH0 L
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+STARTLING  S T AA1 R T L IH0 NG
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+STASH  S T AE1 SH
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+STASZAK  S T AA1 SH AH0 K
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+STATEHOOD  S T EY1 T HH UH2 D
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+STATESMANLIKE  S T AH0 T EH1 S M AH0 N L AY2 K
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+STATESMEN  S T EY1 T S M IH0 N
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+STATISTICIAN  S T AE2 T AH0 S T IH1 SH AH0 N
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+STOWAWAY  S T OW1 AH0 W EY2
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+STOWE  S T OW1
+STOWED  S T OW1 D
+STOWELL  S T AA1 W EH0 L
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+STOWING  S T OW1 W IH0 NG
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+STOYER  S T OY1 ER0
+STRACENER  S T R AE1 S IY0 N ER0
+STRACHAN  S T R AE1 CH AH0 N
+STRACK  S T R AE1 K
+STRACKE  S T R AE1 K
+STRADA  S T R AA1 D AH0
+STRADDLE  S T R AE1 D AH0 L
+STRADDLED  S T R AE1 D AH0 L D
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+STRADDLING  S T R AE1 D L IH0 NG
+STRADER  S T R EY1 D ER0
+STRADFORD  S T R AE1 D F ER0 D
+STRADIVARIUS  S T R AE2 D IH0 V EH1 R IY0 AH0 S
+STRADLEY  S T R AE1 D L IY0
+STRADLING  S T R AE1 D L IH0 NG
+STRAFE  S T R EY1 F
+STRAFING  S T R EY1 F IH0 NG
+STRAGGLE  S T R AE1 G AH0 L
+STRAGGLED  S T R AE1 G AH0 L D
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+STRAGGLERS  S T R AE1 G L ER0 Z
+STRAHAN  S T R AE1 HH AH0 N
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+STRAHLE  S T R EY1 HH AH0 L
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+STRAHM  S T R AA1 M
+STRAIGHT  S T R EY1 T
+STRAIGHTAWAY  S T R EY1 T AH0 W EY2
+STRAIGHTAWAYS  S T R EY1 T AH0 W EY2 Z
+STRAIGHTEDGE  S T R EY1 T EH2 JH
+STRAIGHTEN  S T R EY1 T AH0 N
+STRAIGHTENED  S T R EY1 T AH0 N D
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+STRAIGHTENING(1)  S T R EY1 T N IH0 NG
+STRAIGHTENS  S T R EY1 T AH0 N Z
+STRAIGHTER  S T R EY1 T ER0
+STRAIGHTFORWARD  S T R EY1 T F AO1 R W ER0 D
+STRAIGHTFORWARDLY  S T R EY2 T F AO1 R W ER0 D L IY0
+STRAIGHTS  S T R EY1 T S
+STRAIN  S T R EY1 N
+STRAINED  S T R EY1 N D
+STRAINING  S T R EY1 N IH0 NG
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+STRAIT  S T R EY1 T
+STRAITJACKET  S T R EY1 T JH AE2 K AH0 T
+STRAITS  S T R EY1 T S
+STRAKA  S T R AA1 K AH0
+STRAKER  S T R EY1 K ER0
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+STRAM  S T R AE1 M
+STRANAHAN  S T R AE1 N AH0 HH AE0 N
+STRAND  S T R AE1 N D
+STRANDBERG  S T R AE1 N D B ER0 G
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+STRANDED  S T R AE1 N D IH0 D
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+STRANDLINE  S T R AE1 N D L AY2 N
+STRANDLINE(1)  S T R AE1 N L AY2 N
+STRANDLINES  S T R AE1 N D L AY2 N Z
+STRANDLINES(1)  S T R AE1 N L AY2 N Z
+STRANDS  S T R AE1 N D Z
+STRANG  S T R AE1 NG
+STRANGE  S T R EY1 N JH
+STRANGELOVE  S T R EY1 N JH L AH2 V
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+STRANGENESS  S T R EY1 N JH N AH0 S
+STRANGER  S T R EY1 N JH ER0
+STRANGER'S  S T R EY1 N JH ER0 Z
+STRANGERS  S T R EY1 N JH ER0 Z
+STRANGEST  S T R EY1 N JH IH0 S T
+STRANGIS  S T R AE1 N JH IH0 S
+STRANGLE  S T R AE1 NG G AH0 L
+STRANGLED  S T R AE1 NG G AH0 L D
+STRANGLEHOLD  S T R AE1 NG G AH0 L HH OW2 L D
+STRANGLER  S T R AE1 NG G L ER0
+STRANGLING  S T R AE1 NG G AH0 L IH0 NG
+STRANGLING(1)  S T R AE1 NG G L IH0 NG
+STRANGULATE  S T R AE1 NG G Y AH0 L EY2 T
+STRANGULATION  S T R AE2 NG G Y AH0 L EY1 SH AH0 N
+STRANGULATIONS  S T R AE2 NG G Y AH0 L EY1 SH AH0 N Z
+STRANGWAYES  S T R AE1 NG W EY2 Z
+STRANO  S T R AA1 N OW0
+STRANSKY  S T R AE1 N S K IY0
+STRAP  S T R AE1 P
+STRAPPED  S T R AE1 P T
+STRAPPING  S T R AE1 P IH0 NG
+STRAPS  S T R AE1 P S
+STRASBERG  S T R AE1 S B ER0 G
+STRASBOURG  S T R AE1 S B AO2 R G
+STRASBURG  S T R AE1 S B ER0 G
+STRASBURGER  S T R AE1 S B ER0 G ER0
+STRASSBURG  S T R AE1 S B ER0 G
+STRASSBURGER  S T R AE1 S B ER0 G ER0
+STRASSER  S T R AE1 S ER0
+STRASSMAN  S T R AE1 S M AH0 N
+STRASSNER  S T R AE1 S N ER0
+STRASZHEIM  S T R AE1 S HH AY2 M
+STRATA  S T R AE1 T AH0
+STRATACOM  S T R AE1 T AH0 K AA0 M
+STRATAGEM  S T R AE1 T AH0 JH AH0 M
+STRATAGEMS  S T R AE1 T AH0 JH AH0 M Z
+STRATAS  S T R AE1 T AH0 Z
+STRATE  S T R EY1 T
+STRATEGEM  S T R AE1 T IH0 JH EH0 M
+STRATEGIC  S T R AH0 T IY1 JH IH0 K
+STRATEGICAL  S T R AH0 T IY1 JH IH0 K AH0 L
+STRATEGICALLY  S T R AH0 T IY1 JH IH0 K L IY0
+STRATEGIES  S T R AE1 T AH0 JH IY0 Z
+STRATEGIST  S T R AE1 T IH0 JH IH0 S T
+STRATEGISTS  S T R AE1 T IH0 JH IH0 S T S
+STRATEGIZE  S T R AE1 T AH0 JH AY0 Z
+STRATEGIZING  S T R AE1 T AH0 JH AY0 Z IH0 NG
+STRATEGY  S T R AE1 T AH0 JH IY0
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+STRATFORD  S T R AE1 T F ER0 D
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+STRATIFICATION   S T R AE2 T AH0 F IH0 K EY1 SH AH0 N
+STRATIFIED  S T R AE1 T AH0 F AY2 D
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+STRATIGRAPHIC  S T R AE2 T AH0 G R AE1 F IH0 K
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+STRATMANN  S T R AE1 T M AH0 N
+STRATOCASTER  S T R AE1 T OW0 K AE2 S T ER0
+STRATOFLEX  S T R AE1 T OW0 F L EH2 K S
+STRATOSPHERE  S T R AE1 T AH0 S F IH2 R
+STRATOSPHERIC  S T R AE2 T AH0 S F IH1 R IH0 K
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+STRATUS  S T R AE1 T AH0 S
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+STRAUB  S T R AW1 B
+STRAUBE  S T R AW1 B
+STRAUCH  S T R AW1 K
+STRAUGHAN  S T R AO1 AH0 N
+STRAUGHN  S T R AO1 N
+STRAUGHTER  S T R AO1 T ER0
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+STRAUM(1)  S T R AA1 M
+STRAUS  S T R AW1 S
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+STRAUSE  S T R AW1 S
+STRAUSER  S T R AW1 S ER0
+STRAUSS  S T R AW1 S
+STRAUSS'S  S T R AW1 S IH0 Z
+STRAUSSER  S T R AW1 S ER0
+STRAVINSKY  S T R AH0 V IH1 N S K IY0
+STRAVINSKY'S  S T R AH0 V IH1 N S K IY0 Z
+STRAW  S T R AO1
+STRAWBERRIES  S T R AO1 B EH2 R IY0 Z
+STRAWBERRY  S T R AO1 B EH2 R IY0
+STRAWBRIDGE  S T R AO1 B R IH2 JH
+STRAWDER  S T R AO1 D ER0
+STRAWDERMAN  S T R AO1 D ER0 M AH0 N
+STRAWN  S T R AO1 N
+STRAWS  S T R AO1 Z
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+STRAYER  S T R EY1 ER0
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+STREAKED  S T R IY1 K T
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+STREAKING  S T R IY1 K IH0 NG
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+STREAMING  S T R IY1 M IH0 NG
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+STREAMLINED  S T R IY1 M L AY2 N D
+STREAMLINING  S T R IY1 M L AY2 N IH0 NG
+STREAMS  S T R IY1 M Z
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+STREBE  S T R IY1 B
+STREBECK  S T R IY1 B EH0 K
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+STREFF  S T R EH1 F
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+STREISAND'S(1)  S T R AY1 S AE2 N D Z
+STREIT  S T R AY1 T
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+STRENGER  S T R EH1 NG G ER0
+STRENGTH  S T R EH1 NG K TH
+STRENGTH(1)  S T R EH1 NG TH
+STRENGTHEN  S T R EH1 NG TH AH0 N
+STRENGTHENED  S T R EH1 NG TH AH0 N D
+STRENGTHENING  S T R EH1 NG TH AH0 N IH0 NG
+STRENGTHENS  S T R EH1 NG TH AH0 N Z
+STRENGTHS  S T R EH1 NG K TH S
+STRENGTHS(1)  S T R EH1 NG TH S
+STRENIO  S T R IY1 N IY0 OW0
+STRENUOUS  S T R EH1 N Y UW0 AH0 S
+STRENUOUSLY  S T R EH1 N Y UW0 AH0 S L IY0
+STREP  S T R EH1 P
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+STREPTOCOCCUS  S T R EH2 P T AH0 K AO1 K AH0 S
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+STREPTOKINASE(1)  S T R EH2 P T OW0 K AY1 N EY2 Z
+STRESS  S T R EH1 S
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+STRESSES  S T R EH1 S AH0 Z
+STRESSES(1)  S T R EH1 S IH0 Z
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+STRETCHERS  S T R EH1 CH ER0 Z
+STRETCHES  S T R EH1 CH AH0 Z
+STRETCHES(1)  S T R EH1 CH IH0 Z
+STRETCHING  S T R EH1 CH IH0 NG
+STRETTO  S T R EH1 T OW2
+STREVIG  S T R EH1 V IH0 G
+STREW  S T R UW1
+STREWN  S T R UW1 N
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+STRIBLING  S T ER1 AH0 B AH0 L IH0 NG
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+STROLLING  S T R OW1 L IH0 NG
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+STRUEBING  S T R UH1 B IH0 NG
+STRUGGLE  S T R AH1 G AH0 L
+STRUGGLE'S  S T R AH1 G AH0 L Z
+STRUGGLED  S T R AH1 G AH0 L D
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+STRUGGLING  S T R AH1 G AH0 L IH0 NG
+STRUGGLING(1)  S T R AH1 G L IH0 NG
+STRUM  S T R AH1 M
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+STRUMS  S T R AH1 M Z
+STRUNG  S T R AH1 NG
+STRUNK  S T R AH1 NG K
+STRUNK'S  S T R AH1 NG K S
+STRUPP  S T R AH1 P
+STRUSS  S T R AH1 S
+STRUT  S T R AH1 T
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+STRUTS  S T R AH1 T S
+STRUTTING  S T R AH1 T IH0 NG
+STRUTTON  S T R AH1 T AH0 N
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+TRAMONTANO  T R AA0 M OW0 N T AA1 N OW0
+TRAMONTE  T R AA0 M OW1 N T IY0
+TRAMONTIN  T R AH0 M AA1 N T IH0 N
+TRAMP  T R AE1 M P
+TRAMPE  T R AE1 M P
+TRAMPED  T R AE1 M P T
+TRAMPING  T R AE1 M P IH0 NG
+TRAMPLE  T R AE1 M P AH0 L
+TRAMPLED  T R AE1 M P AH0 L D
+TRAMPLES  T R AE1 M P AH0 L Z
+TRAMPLING  T R AE1 M P L IH0 NG
+TRAMPOLINE  T R AE2 M P AH0 L IY1 N
+TRAMPS  T R AE1 M P Z
+TRAMS  T R AE1 M Z
+TRAN  T R AE1 N
+TRANBERG  T R AE1 N B ER0 G
+TRANCE  T R AE1 N S
+TRANCHE  T R AE1 N CH
+TRANCHES  T R AE1 N CH EH0 Z
+TRANCHINA  T R AA0 N K IY1 N AH0
+TRANE  T R EY1 N
+TRANG  T R AE1 NG
+TRANI  T R AA1 N IY0
+TRANQUIL  T R AE1 NG K W AH0 L
+TRANQUIL(1)  T R AE1 NG K W IH0 L
+TRANQUILITY  T R AE0 NG K W IH1 L IH0 T IY0
+TRANQUILIZE  T R AE1 NG K W AH0 L AY2 Z
+TRANQUILIZER  T R AE1 NG K W AH0 L AY2 Z ER0
+TRANQUILIZERS  T R AE1 NG K W AH0 L AY2 Z ER0 Z
+TRANQUILIZING  T R AE1 NG K W AH0 L AY2 Z IH0 NG
+TRANQUILLITY  T R AE0 NG K W IH1 L IH0 T IY0
+TRANS  T R AE1 N Z
+TRANSACT  T R AE0 N Z AE1 K T
+TRANSACTED  T R AE0 N S AE1 K T IH0 D
+TRANSACTED(1)  T R AE0 N Z AE1 K T IH0 D
+TRANSACTION  T R AE0 N Z AE1 K SH AH0 N
+TRANSACTION'S  T R AE0 N Z AE1 K SH AH0 N Z
+TRANSACTIONS  T R AE0 N Z AE1 K SH AH0 N Z
+TRANSAFRICA  T R AE2 N Z AE1 F R IH0 K AH0
+TRANSALASKA  T R AE2 N Z AH0 L AE1 S K AH0
+TRANSALTA  T R AE2 N Z AO1 L T AH0
+TRANSAMERICA  T R AE2 N S AH0 M EH1 R IH0 K AH0
+TRANSAMERICA'S  T R AE2 N Z AH0 M EH1 R IH0 K AH0 Z
+TRANSAMERICAN  T R AE2 N Z AH0 M EH1 R IH0 K AH0 N
+TRANSAMERICAN'S  T R AE2 N Z AH0 M EH1 R IH0 K AH0 N Z
+TRANSATLANTIC  T R AE2 N Z AH0 T L AE1 N T IH0 K
+TRANSATLANTIC(1)  T R AE2 N Z AH0 T L AE1 N IH0 K
+TRANSCANADA  T R AE2 N Z K AE1 N AH0 T AH0
+TRANSCANADA'S  T R AE2 N Z K AE1 N AH0 D AH0 Z
+TRANSCAPITAL  T R AE2 N Z K AE1 P IH0 T AH0 L
+TRANSCEND  T R AE0 N S EH1 N D
+TRANSCENDED  T R AE0 N S EH1 N D IH0 D
+TRANSCENDENCE  T R AE0 N S EH1 N D AH0 N S
+TRANSCENDENT  T R AE0 N S EH1 N D AH0 N T
+TRANSCENDENTAL  T R AE2 N S AH0 N D EH1 N T AH0 L
+TRANSCENDENTAL(1)  T R AE2 N S AH0 N D EH1 N AH0 L
+TRANSCENDING  T R AE0 N S EH1 N D IH0 NG
+TRANSCENDS  T R AE0 N S EH1 N D Z
+TRANSCHANNEL  T R AH1 N S CH AE1 N AH0 L
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+TRANSCISCO  T R AE2 N S IH1 S K OW0
+TRANSCO  T R AE1 N S K OW0
+TRANSCON  T R AE1 N Z K AA0 N
+TRANSCONTINENTAL  T R AE2 N Z K AA2 N T IH0 N EH1 N T AH0 L
+TRANSCONTINENTAL(1)  T R AE2 N Z K AA2 N IH0 N EH1 N T AH0 L
+TRANSCONTINENTAL(2)  T R AE2 N Z K AA2 N T IH0 N EH1 N AH0 L
+TRANSCONTINENTAL(3)  T R AE2 N Z K AA2 N IH0 N EH1 N AH0 L
+TRANSCONTINENTAL'S  T R AE2 N Z K AA2 N T IH0 N EH1 N T AH0 L Z
+TRANSCONTINENTAL'S(1)  T R AE2 N Z K AA2 N IH0 N EH1 N T AH0 L Z
+TRANSCONTINENTAL'S(2)  T R AE2 N Z K AA2 N T IH0 N EH1 N AH0 L Z
+TRANSCONTINENTAL'S(3)  T R AE2 N Z K AA2 N IH0 N EH1 N AH0 L Z
+TRANSCRIBE  T R AE0 N S K R AY1 B
+TRANSCRIBED  T R AE0 N S K R AY1 B D
+TRANSCRIBER  T R AE0 N S K R AY1 B ER0
+TRANSCRIBERS  T R AE0 N S K R AY1 B ER0 Z
+TRANSCRIBES  T R AE0 N S K R AY1 B Z
+TRANSCRIBING  T R AE0 N S K R AY1 B IH0 NG
+TRANSCRIPT  T R AE1 N S K R IH2 P T
+TRANSCRIPTION  T R AE2 N S K R IH1 P SH AH0 N
+TRANSCRIPTIONS  T R AE2 N S K R IH1 P SH AH0 N Z
+TRANSCRIPTS  T R AE1 N S K R IH2 P T S
+TRANSDUCER  T R AE0 N S D UW1 S ER0
+TRANSDUCERS  T R AE0 N S D UW1 S ER0 Z
+TRANSECT  T R AE1 N S EH2 K T
+TRANSECTED  T R AE1 N S EH2 K T IH0 D
+TRANSECTION  T R AE1 N S EH2 K SH AH0 N
+TRANSFER  T R AE0 N S F ER1
+TRANSFER(1)  T R AE1 N S F ER0
+TRANSFERABILITY  T R AE2 N S F ER0 AH0 B IH1 L IH0 T IY0
+TRANSFERABLE  T R AE0 N S F ER1 AH0 B AH0 L
+TRANSFERED  T R AE0 N S F ER1 D
+TRANSFERENCE  T R AE0 N S F ER1 AH0 N S
+TRANSFERING  T R AE0 N S F ER1 IH0 NG
+TRANSFERRABLE  T R AE2 N S F ER1 AH0 B AH0 L
+TRANSFERRED  T R AE0 N S F ER1 D
+TRANSFERRED(1)  T R AE1 N S F ER0 D
+TRANSFERRING  T R AE0 N S F ER1 IH0 NG
+TRANSFERS  T R AE0 N S F ER1 Z
+TRANSFERS(1)  T R AE1 N S F ER0 Z
+TRANSFIX  T R AE0 N S F IH1 K S
+TRANSFIXED  T R AE0 N S F IH1 K S T
+TRANSFORM  T R AE0 N S F AO1 R M
+TRANSFORM(1)  T R AE1 N S F AO0 R M
+TRANSFORMATION  T R AE2 N S F ER0 M EY1 SH AH0 N
+TRANSFORMATIONAL  T R AE2 N S F ER0 M EY1 SH AH0 N AH0 L
+TRANSFORMATIONS  T R AE2 N S F ER0 M EY1 SH AH0 N Z
+TRANSFORMATIVE  T R AE2 N S F AO1 R M AA0 T IH2 V
+TRANSFORMED  T R AE0 N S F AO1 R M D
+TRANSFORMER  T R AE0 N S F AO1 R M ER0
+TRANSFORMERS  T R AE0 N S F AO1 R M ER0 Z
+TRANSFORMING  T R AE0 N S F AO1 R M IH0 NG
+TRANSFORMS  T R AE0 N S F AO1 R M Z
+TRANSFUSE  T R AE0 N S F Y UW1 Z
+TRANSFUSED  T R AE0 N S F Y UW1 Z D
+TRANSFUSION  T R AE0 N S F Y UW1 ZH AH0 N
+TRANSFUSIONS  T R AE0 N S F Y UW1 ZH AH0 N Z
+TRANSGENDER  T R AE1 N Z JH EH1 N D ER0
+TRANSGENIC  T R AE2 N Z JH EH1 N IH0 K
+TRANSGRESS  T R AE0 N Z G R EH1 S
+TRANSGRESSED  T R AE0 N Z G R EH1 S T
+TRANSGRESSES  T R AE0 N Z G R EH1 S IH0 S
+TRANSGRESSING  T R AE0 N Z G R EH1 S IH0 NG
+TRANSGRESSION  T R AE0 N Z G R EH1 SH AH0 N
+TRANSGRESSIONS  T R AE0 N Z G R EH1 SH AH0 N Z
+TRANSGRESSOR  T R AE0 N Z G R EH1 S ER0
+TRANSIENCE  T R AE1 N Z IY0 AH0 N S
+TRANSIENT  T R AE1 N ZH AH0 N T
+TRANSIENTS  T R AE1 N Z IY0 AH0 N T S
+TRANSILLUMINATION  T R AE2 N Z AH0 L UW2 M AH0 N EY1 SH AH0 N
+TRANSIMAGE  T R AE2 N Z IH1 M IH0 JH
+TRANSISTOR  T R AE0 N Z IH1 S T ER0
+TRANSISTORS  T R AE0 N Z IH1 S T ER0 Z
+TRANSIT  T R AE1 N Z IH0 T
+TRANSITED  T R AE1 N Z IH0 T IH0 D
+TRANSITING  T R AE1 N Z IH0 T IH0 NG
+TRANSITION  T R AE0 N Z IH1 SH AH0 N
+TRANSITIONAL  T R AE0 N S IH1 SH AH0 N AH0 L
+TRANSITIONAL(1)  T R AE0 N Z IH1 SH AH0 N AH0 L
+TRANSITIONING  T R AE0 N Z IH1 SH AH0 N IH0 NG
+TRANSITIONS  T R AE0 N Z IH1 SH AH0 N Z
+TRANSITORY  T R AE1 N Z AH0 T AO2 R IY0
+TRANSITS  T R AE1 N Z IH0 T S
+TRANSKEI  T R AE1 N Z K EY2
+TRANSLATE  T R AE0 N Z L EY1 T
+TRANSLATE(1)  T R AE0 N S L EY1 T
+TRANSLATED  T R AE0 N S L EY1 T IH0 D
+TRANSLATES  T R AE0 N Z L EY1 T S
+TRANSLATES(1)  T R AE1 N S L EY2 T S
+TRANSLATING  T R AE0 N Z L EY1 T IH0 NG
+TRANSLATING(1)  T R AE1 N S L EY2 T IH0 NG
+TRANSLATION  T R AE0 N Z L EY1 SH AH0 N
+TRANSLATION(1)  T R AE0 N S L EY1 SH AH0 N
+TRANSLATIONS  T R AE0 N Z L EY1 SH AH0 N Z
+TRANSLATIONS(1)  T R AE0 N S L EY1 SH AH0 N Z
+TRANSLATOR  T R AE0 N S L EY1 T ER0
+TRANSLATOR(1)  T R AE0 N Z L EY1 T ER0
+TRANSLATORS  T R AE0 N S L EY1 T ER0 Z
+TRANSLATORS(1)  T R AE0 N Z L EY1 T ER0 Z
+TRANSLOGIC  T R AE2 N Z L AA1 JH IH0 K
+TRANSLUCENT  T R AE0 N S L UW1 S AH0 N T
+TRANSMARK  T R AE1 N Z M AA2 R K
+TRANSMEDIA  T R AE2 N Z M IY1 D IY0 AH0
+TRANSMISSION  T R AE0 N S M IH1 SH AH0 N
+TRANSMISSION(1)  T R AE0 N Z M IH1 SH AH0 N
+TRANSMISSIONS  T R AE0 N Z M IH1 SH AH0 N Z
+TRANSMIT  T R AE0 N Z M IH1 T
+TRANSMITS  T R AE0 N Z M IH1 T S
+TRANSMITTABLE  T R AE0 N Z M IH1 T AH0 B AH0 L
+TRANSMITTAL  T R AE0 N S M IH1 T AH0 L
+TRANSMITTED  T R AE0 N S M IH1 T IH0 D
+TRANSMITTER  T R AE0 N S M IH1 T ER0
+TRANSMITTER'S  T R AE0 N S M IH1 T ER0 Z
+TRANSMITTERS  T R AE0 N S M IH1 T ER0 Z
+TRANSMITTING  T R AE0 N S M IH1 T IH0 NG
+TRANSNATIONAL  T R AE0 N S N AE1 SH AH0 N AH0 L
+TRANSOCEANIC  T R AE2 N Z OW0 SH IY0 AE1 N IH0 K
+TRANSOHIO  T R AE2 N Z OW0 HH AY1 OW0
+TRANSOM  T R AE1 N S AH0 M
+TRANSOMS  T R AE1 N S AH0 M Z
+TRANSOU  T R AE1 N Z UW2
+TRANSPAC  T R AE1 N Z P AE2 K
+TRANSPACIFIC  T R AE2 N S P AH0 S IH1 F IH0 K
+TRANSPARENCIES  T R AE0 N S P EH1 R AH0 N S IY0 Z
+TRANSPARENCY  T R AE0 N S P EH1 R AH0 N S IY0
+TRANSPARENT  T R AE0 N S P EH1 R AH0 N T
+TRANSPARENTLY  T R AE0 N S P EH1 R AH0 N T L IY0
+TRANSPARK  T R AE1 N S P AA1 R K
+TRANSPIRE  T R AE0 N S P AY1 ER0
+TRANSPIRED  T R AE0 N S P AY1 ER0 D
+TRANSPIRES  T R AE0 N S P AY1 ER0 Z
+TRANSPIRING  T R AE0 N S P AY1 ER0 IH0 NG
+TRANSPLANT  T R AE0 N S P L AE1 N T
+TRANSPLANTATION  T R AE2 N Z P L AE0 N T EY1 SH AH0 N
+TRANSPLANTED  T R AE0 N S P L AE1 N T IH0 D
+TRANSPLANTING  T R AE0 N S P L AE1 N T IH0 NG
+TRANSPLANTS  T R AE0 N S P L AE1 N T S
+TRANSPONDER  T R AE0 N S P AA1 N D ER0
+TRANSPONDERS  T R AE0 N S P AA1 N D ER0 Z
+TRANSPORT  T R AE0 N S P AO1 R T
+TRANSPORT(1)  T R AE1 N S P AO0 R T
+TRANSPORTABLE  T R AE0 N S P AO1 R T AH0 B AH0 L
+TRANSPORTATION  T R AE2 N S P ER0 T EY1 SH AH0 N
+TRANSPORTATION'S  T R AE2 N S P ER0 T EY1 SH AH0 N Z
+TRANSPORTED  T R AE0 N S P AO1 R T IH0 D
+TRANSPORTER  T R AE0 N S P AO1 R T ER0
+TRANSPORTERS  T R AE0 N S P AO1 R T ER0 Z
+TRANSPORTING  T R AE0 N S P AO1 R T IH0 NG
+TRANSPORTS  T R AE0 N S P AO1 R T S
+TRANSPORTS(1)  T R AE1 N S P AO0 R T S
+TRANSPOSE  T R AE0 N S P OW1 Z
+TRANSPOSED  T R AE0 N S P OW1 Z D
+TRANSRACIAL  T R AE2 N Z R EY1 SH AH0 L
+TRANSRAPID  T R AE1 N Z R AE1 P IH0 D
+TRANSSEXUAL  T R AE0 N S EH1 K SH Y UW0 AH0 L
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+TRANSTAR  T R AE0 N S T AA1 R
+TRANSTECHNOLOGY  T R AE2 N Z T AH0 K N AA1 L AH0 JH IY0
+TRANSTECTOR  T R AE2 N Z T EH1 K T ER0
+TRANSUE  T R AE1 N Z UW0
+TRANSVAAL  T R AE0 N Z V AA1 L
+TRANSVERSE  T R AE0 N Z V ER1 S
+TRANSVESTITE  T R AE0 N Z V EH1 S T AY0 T
+TRANSVESTITES  T R AE0 N Z V EH1 S T AY0 T S
+TRANSWAY  T R AE1 N Z W EY2
+TRANSWESTERN  T R AE2 N Z W EH1 S T ER0 N
+TRANSWORLD  T R AE0 N S W ER1 L D
+TRANSYLVANIA  T R AE2 N Z IY0 L V EY1 N IY0 AH0
+TRANSYLVANIA(1)  T R AE2 N S IH0 L V EY1 N Y AH0
+TRANT  T R AE1 N T
+TRANTER  T R AE1 N T ER0
+TRANTHAM  T R AE1 N TH AH0 M
+TRANUM  T R AE1 N AH0 M
+TRANZONIC  T R AE0 N Z AA1 N IH0 K
+TRAP  T R AE1 P
+TRAPANI  T R AA0 P AA1 N IY0
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+TRAPELO  T R AH0 P EH1 L OW0
+TRAPEZE  T R AH0 P IY1 Z
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+TRAPP  T R AE1 P
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+TRAUT  T R AO1 T
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+TRAVAIL  T R AH0 V EY1 L
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+TRAVELERS'  T R AE1 V AH0 L ER0 Z
+TRAVELGATE  T R AE1 V AH0 L G EY2 T
+TRAVELING  T R AE1 V AH0 L IH0 NG
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diff --git a/utils/tts/gpt_sovits/text/engdict-hot.rep b/utils/tts/gpt_sovits/text/engdict-hot.rep
new file mode 100644
index 0000000000000000000000000000000000000000..120e5ef63a7bd050747a39460dd4ed4580920344
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/engdict-hot.rep
@@ -0,0 +1,2 @@
+CHATGPT CH AE1 T JH IY1 P IY1 T IY1
+JSON JH EY1 S AH0 N
\ No newline at end of file
diff --git a/utils/tts/gpt_sovits/text/engdict_cache.pickle b/utils/tts/gpt_sovits/text/engdict_cache.pickle
new file mode 100644
index 0000000000000000000000000000000000000000..cdca61190dbe75c37ce3a05e8d5c942fc1a2d726
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/engdict_cache.pickle
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9bff9393f4b192d873a11335efc8f124771087b6dc847d34fd240c2846889d2b
+size 5965909
diff --git a/utils/tts/gpt_sovits/text/english.py b/utils/tts/gpt_sovits/text/english.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d974d8ead6f20d151972f08637c3def0cff4d5e
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/english.py
@@ -0,0 +1,364 @@
+import os
+import pickle
+import re
+import unicodedata
+from builtins import str as unicode
+from string import punctuation
+
+import wordsegment
+from g2p_en import G2p
+from g2p_en.expand import normalize_numbers
+from nltk import pos_tag
+from nltk.tokenize import TweetTokenizer
+
+from utils.tts.gpt_sovits.text import symbols
+
+word_tokenize = TweetTokenizer().tokenize
+
+current_file_path = os.path.dirname(__file__)
+CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
+CMU_DICT_FAST_PATH = os.path.join(current_file_path, "cmudict-fast.rep")
+CMU_DICT_HOT_PATH = os.path.join(current_file_path, "engdict-hot.rep")
+CACHE_PATH = os.path.join(current_file_path, "engdict_cache.pickle")
+NAMECACHE_PATH = os.path.join(current_file_path, "namedict_cache.pickle")
+
+arpa = {
+    "AH0",
+    "S",
+    "AH1",
+    "EY2",
+    "AE2",
+    "EH0",
+    "OW2",
+    "UH0",
+    "NG",
+    "B",
+    "G",
+    "AY0",
+    "M",
+    "AA0",
+    "F",
+    "AO0",
+    "ER2",
+    "UH1",
+    "IY1",
+    "AH2",
+    "DH",
+    "IY0",
+    "EY1",
+    "IH0",
+    "K",
+    "N",
+    "W",
+    "IY2",
+    "T",
+    "AA1",
+    "ER1",
+    "EH2",
+    "OY0",
+    "UH2",
+    "UW1",
+    "Z",
+    "AW2",
+    "AW1",
+    "V",
+    "UW2",
+    "AA2",
+    "ER",
+    "AW0",
+    "UW0",
+    "R",
+    "OW1",
+    "EH1",
+    "ZH",
+    "AE0",
+    "IH2",
+    "IH",
+    "Y",
+    "JH",
+    "P",
+    "AY1",
+    "EY0",
+    "OY2",
+    "TH",
+    "HH",
+    "D",
+    "ER0",
+    "CH",
+    "AO1",
+    "AE1",
+    "AO2",
+    "OY1",
+    "AY2",
+    "IH1",
+    "OW0",
+    "L",
+    "SH",
+}
+
+
+def replace_phs(phs):
+    rep_map = {"'": "-"}
+    phs_new = []
+    for ph in phs:
+        if ph in symbols:
+            phs_new.append(ph)
+        elif ph in rep_map.keys():
+            phs_new.append(rep_map[ph])
+        else:
+            print("ph not in symbols: ", ph)
+    return phs_new
+
+
+def read_dict():
+    g2p_dict = {}
+    start_line = 49
+    with open(CMU_DICT_PATH) as f:
+        line = f.readline()
+        line_index = 1
+        while line:
+            if line_index >= start_line:
+                line = line.strip()
+                word_split = line.split("  ")
+                word = word_split[0].lower()
+
+                syllable_split = word_split[1].split(" - ")
+                g2p_dict[word] = []
+                for syllable in syllable_split:
+                    phone_split = syllable.split(" ")
+                    g2p_dict[word].append(phone_split)
+
+            line_index = line_index + 1
+            line = f.readline()
+
+    return g2p_dict
+
+
+def read_dict_new():
+    g2p_dict = {}
+    with open(CMU_DICT_PATH) as f:
+        line = f.readline()
+        line_index = 1
+        while line:
+            if line_index >= 57:
+                line = line.strip()
+                word_split = line.split("  ")
+                word = word_split[0].lower()
+                g2p_dict[word] = [word_split[1].split(" ")]
+
+            line_index = line_index + 1
+            line = f.readline()
+
+    with open(CMU_DICT_FAST_PATH) as f:
+        line = f.readline()
+        line_index = 1
+        while line:
+            if line_index >= 0:
+                line = line.strip()
+                word_split = line.split(" ")
+                word = word_split[0].lower()
+                if word not in g2p_dict:
+                    g2p_dict[word] = [word_split[1:]]
+
+            line_index = line_index + 1
+            line = f.readline()
+
+    return g2p_dict
+
+
+def hot_reload_hot(g2p_dict):
+    with open(CMU_DICT_HOT_PATH) as f:
+        line = f.readline()
+        line_index = 1
+        while line:
+            if line_index >= 0:
+                line = line.strip()
+                word_split = line.split(" ")
+                word = word_split[0].lower()
+                # 自定义发音词直接覆盖字典
+                g2p_dict[word] = [word_split[1:]]
+
+            line_index = line_index + 1
+            line = f.readline()
+
+    return g2p_dict
+
+
+def cache_dict(g2p_dict, file_path):
+    with open(file_path, "wb") as pickle_file:
+        pickle.dump(g2p_dict, pickle_file)
+
+
+def get_dict():
+    if os.path.exists(CACHE_PATH):
+        with open(CACHE_PATH, "rb") as pickle_file:
+            g2p_dict = pickle.load(pickle_file)
+    else:
+        g2p_dict = read_dict_new()
+        cache_dict(g2p_dict, CACHE_PATH)
+
+    g2p_dict = hot_reload_hot(g2p_dict)
+
+    return g2p_dict
+
+
+def get_namedict():
+    if os.path.exists(NAMECACHE_PATH):
+        with open(NAMECACHE_PATH, "rb") as pickle_file:
+            name_dict = pickle.load(pickle_file)
+    else:
+        name_dict = {}
+
+    return name_dict
+
+
+def text_normalize(text):
+    # todo: eng text normalize
+    # 适配中文及 g2p_en 标点
+    rep_map = {
+        "[;:：，；]": ",",
+        '["’]': "'",
+        "。": ".",
+        "！": "!",
+        "？": "?",
+    }
+    for p, r in rep_map.items():
+        text = re.sub(p, r, text)
+
+    # 来自 g2p_en 文本格式化处理
+    # 增加大写兼容
+    text = unicode(text)
+    text = normalize_numbers(text)
+    text = "".join(char for char in unicodedata.normalize("NFD", text) if unicodedata.category(char) != "Mn")  # Strip accents
+    text = re.sub("[^ A-Za-z'.,?!\-]", "", text)
+    text = re.sub(r"(?i)i\.e\.", "that is", text)
+    text = re.sub(r"(?i)e\.g\.", "for example", text)
+
+    return text
+
+
+class en_G2p(G2p):
+    def __init__(self):
+        super().__init__()
+        # 分词初始化
+        wordsegment.load()
+
+        # 扩展过时字典, 添加姓名字典
+        self.cmu = get_dict()
+        self.namedict = get_namedict()
+
+        # 剔除读音错误的几个缩写
+        for word in ["AE", "AI", "AR", "IOS", "HUD", "OS"]:
+            del self.cmu[word.lower()]
+
+        # 修正多音字
+        self.homograph2features["read"] = (["R", "IY1", "D"], ["R", "EH1", "D"], "VBP")
+        self.homograph2features["complex"] = (
+            ["K", "AH0", "M", "P", "L", "EH1", "K", "S"],
+            ["K", "AA1", "M", "P", "L", "EH0", "K", "S"],
+            "JJ",
+        )
+
+    def __call__(self, text):
+        # tokenization
+        words = word_tokenize(text)
+        tokens = pos_tag(words)  # tuples of (word, tag)
+
+        # steps
+        prons = []
+        for o_word, pos in tokens:
+            # 还原 g2p_en 小写操作逻辑
+            word = o_word.lower()
+
+            if re.search("[a-z]", word) is None:
+                pron = [word]
+            # 先把单字母推出去
+            elif len(word) == 1:
+                # 单读 A 发音修正, 这里需要原格式 o_word 判断大写
+                if o_word == "A":
+                    pron = ["EY1"]
+                else:
+                    pron = self.cmu[word][0]
+            # g2p_en 原版多音字处理
+            elif word in self.homograph2features:  # Check homograph
+                pron1, pron2, pos1 = self.homograph2features[word]
+                if pos.startswith(pos1):
+                    pron = pron1
+                # pos1比pos长仅出现在read
+                elif len(pos) < len(pos1) and pos == pos1[: len(pos)]:
+                    pron = pron1
+                else:
+                    pron = pron2
+            else:
+                # 递归查找预测
+                pron = self.qryword(o_word)
+
+            prons.extend(pron)
+            prons.extend([" "])
+
+        return prons[:-1]
+
+    def qryword(self, o_word):
+        word = o_word.lower()
+
+        # 查字典, 单字母除外
+        if len(word) > 1 and word in self.cmu:  # lookup CMU dict
+            return self.cmu[word][0]
+
+        # 单词仅首字母大写时查找姓名字典
+        if o_word.istitle() and word in self.namedict:
+            return self.namedict[word][0]
+
+        # oov 长度小于等于 3 直接读字母
+        if len(word) <= 3:
+            phones = []
+            for w in word:
+                # 单读 A 发音修正, 此处不存在大写的情况
+                if w == "a":
+                    phones.extend(["EY1"])
+                else:
+                    phones.extend(self.cmu[w][0])
+            return phones
+
+        # 尝试分离所有格
+        if re.match(r"^([a-z]+)('s)$", word):
+            phones = self.qryword(word[:-2])[:]
+            # P T K F TH HH 无声辅音结尾 's 发 ['S']
+            if phones[-1] in ["P", "T", "K", "F", "TH", "HH"]:
+                phones.extend(["S"])
+            # S Z SH ZH CH JH 擦声结尾 's 发 ['IH1', 'Z'] 或 ['AH0', 'Z']
+            elif phones[-1] in ["S", "Z", "SH", "ZH", "CH", "JH"]:
+                phones.extend(["AH0", "Z"])
+            # B D G DH V M N NG L R W Y 有声辅音结尾 's 发 ['Z']
+            # AH0 AH1 AH2 EY0 EY1 EY2 AE0 AE1 AE2 EH0 EH1 EH2 OW0 OW1 OW2 UH0 UH1 UH2 IY0 IY1 IY2 AA0 AA1 AA2 AO0 AO1 AO2
+            # ER ER0 ER1 ER2 UW0 UW1 UW2 AY0 AY1 AY2 AW0 AW1 AW2 OY0 OY1 OY2 IH IH0 IH1 IH2 元音结尾 's 发 ['Z']
+            else:
+                phones.extend(["Z"])
+            return phones
+
+        # 尝试进行分词，应对复合词
+        comps = wordsegment.segment(word.lower())
+
+        # 无法分词的送回去预测
+        if len(comps) == 1:
+            return self.predict(word)
+
+        # 可以分词的递归处理
+        return [phone for comp in comps for phone in self.qryword(comp)]
+
+
+_g2p = en_G2p()
+
+
+def g2p(text):
+    # g2p_en 整段推理，剔除不存在的arpa返回
+    phone_list = _g2p(text)
+    phones = [ph if ph != "<unk>" else "UNK" for ph in phone_list if ph not in [" ", "<pad>", "UW", "</s>", "<s>"]]
+
+    return replace_phs(phones)
+
+
+if __name__ == "__main__":
+    print(g2p("hello"))
+    print(g2p(text_normalize("e.g. I used openai's AI tool to draw a picture.")))
+    print(g2p(text_normalize("In this; paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")))
diff --git a/utils/tts/gpt_sovits/text/namedict_cache.pickle b/utils/tts/gpt_sovits/text/namedict_cache.pickle
new file mode 100644
index 0000000000000000000000000000000000000000..9ad2ed9a6478077f8fe4469192597fa8f6442cf6
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/namedict_cache.pickle
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:559552094c4a6e995213e3fa586330e078ef8cb3a7a95a3109e945111cd2bfc1
+size 760663
diff --git a/utils/tts/gpt_sovits/text/opencpop-strict.txt b/utils/tts/gpt_sovits/text/opencpop-strict.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1bede89a429e34b1a58781ee570fc313b80c0aee
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/opencpop-strict.txt
@@ -0,0 +1,429 @@
+a	AA a
+ai	AA ai
+an	AA an
+ang	AA ang
+ao	AA ao
+ba	b a
+bai	b ai
+ban	b an
+bang	b ang
+bao	b ao
+bei	b ei
+ben	b en
+beng	b eng
+bi	b i
+bian	b ian
+biao	b iao
+bie	b ie
+bin	b in
+bing	b ing
+bo	b o
+bu	b u
+ca	c a
+cai	c ai
+can	c an
+cang	c ang
+cao	c ao
+ce	c e
+cei	c ei
+cen	c en
+ceng	c eng
+cha	ch a
+chai	ch ai
+chan	ch an
+chang	ch ang
+chao	ch ao
+che	ch e
+chen	ch en
+cheng	ch eng
+chi	ch ir
+chong	ch ong
+chou	ch ou
+chu	ch u
+chua	ch ua
+chuai	ch uai
+chuan	ch uan
+chuang	ch uang
+chui	ch ui
+chun	ch un
+chuo	ch uo
+ci	c i0
+cong	c ong
+cou	c ou
+cu	c u
+cuan	c uan
+cui	c ui
+cun	c un
+cuo	c uo
+da	d a
+dai	d ai
+dan	d an
+dang	d ang
+dao	d ao
+de	d e
+dei	d ei
+den	d en
+deng	d eng
+di	d i
+dia	d ia
+dian	d ian
+diao	d iao
+die	d ie
+ding	d ing
+diu	d iu
+dong	d ong
+dou	d ou
+du	d u
+duan	d uan
+dui	d ui
+dun	d un
+duo	d uo
+e	EE e
+ei	EE ei
+en	EE en
+eng	EE eng
+er	EE er
+fa	f a
+fan	f an
+fang	f ang
+fei	f ei
+fen	f en
+feng	f eng
+fo	f o
+fou	f ou
+fu	f u
+ga	g a
+gai	g ai
+gan	g an
+gang	g ang
+gao	g ao
+ge	g e
+gei	g ei
+gen	g en
+geng	g eng
+gong	g ong
+gou	g ou
+gu	g u
+gua	g ua
+guai	g uai
+guan	g uan
+guang	g uang
+gui	g ui
+gun	g un
+guo	g uo
+ha	h a
+hai	h ai
+han	h an
+hang	h ang
+hao	h ao
+he	h e
+hei	h ei
+hen	h en
+heng	h eng
+hong	h ong
+hou	h ou
+hu	h u
+hua	h ua
+huai	h uai
+huan	h uan
+huang	h uang
+hui	h ui
+hun	h un
+huo	h uo
+ji	j i
+jia	j ia
+jian	j ian
+jiang	j iang
+jiao	j iao
+jie	j ie
+jin	j in
+jing	j ing
+jiong	j iong
+jiu	j iu
+ju	j v
+jv	j v
+juan	j van
+jvan	j van
+jue	j ve
+jve	j ve
+jun	j vn
+jvn	j vn
+ka	k a
+kai	k ai
+kan	k an
+kang	k ang
+kao	k ao
+ke	k e
+kei	k ei
+ken	k en
+keng	k eng
+kong	k ong
+kou	k ou
+ku	k u
+kua	k ua
+kuai	k uai
+kuan	k uan
+kuang	k uang
+kui	k ui
+kun	k un
+kuo	k uo
+la	l a
+lai	l ai
+lan	l an
+lang	l ang
+lao	l ao
+le	l e
+lei	l ei
+leng	l eng
+li	l i
+lia	l ia
+lian	l ian
+liang	l iang
+liao	l iao
+lie	l ie
+lin	l in
+ling	l ing
+liu	l iu
+lo	l o
+long	l ong
+lou	l ou
+lu	l u
+luan	l uan
+lun	l un
+luo	l uo
+lv	l v
+lve	l ve
+ma	m a
+mai	m ai
+man	m an
+mang	m ang
+mao	m ao
+me	m e
+mei	m ei
+men	m en
+meng	m eng
+mi	m i
+mian	m ian
+miao	m iao
+mie	m ie
+min	m in
+ming	m ing
+miu	m iu
+mo	m o
+mou	m ou
+mu	m u
+na	n a
+nai	n ai
+nan	n an
+nang	n ang
+nao	n ao
+ne	n e
+nei	n ei
+nen	n en
+neng	n eng
+ni	n i
+nian	n ian
+niang	n iang
+niao	n iao
+nie	n ie
+nin	n in
+ning	n ing
+niu	n iu
+nong	n ong
+nou	n ou
+nu	n u
+nuan	n uan
+nun	n un
+nuo	n uo
+nv	n v
+nve	n ve
+o	OO o
+ou	OO ou
+pa	p a
+pai	p ai
+pan	p an
+pang	p ang
+pao	p ao
+pei	p ei
+pen	p en
+peng	p eng
+pi	p i
+pian	p ian
+piao	p iao
+pie	p ie
+pin	p in
+ping	p ing
+po	p o
+pou	p ou
+pu	p u
+qi	q i
+qia	q ia
+qian	q ian
+qiang	q iang
+qiao	q iao
+qie	q ie
+qin	q in
+qing	q ing
+qiong	q iong
+qiu	q iu
+qu	q v
+qv	q v
+quan	q van
+qvan	q van
+que	q ve
+qve	q ve
+qun	q vn
+qvn	q vn
+ran	r an
+rang	r ang
+rao	r ao
+re	r e
+ren	r en
+reng	r eng
+ri	r ir
+rong	r ong
+rou	r ou
+ru	r u
+rua	r ua
+ruan	r uan
+rui	r ui
+run	r un
+ruo	r uo
+sa	s a
+sai	s ai
+san	s an
+sang	s ang
+sao	s ao
+se	s e
+sen	s en
+seng	s eng
+sha	sh a
+shai	sh ai
+shan	sh an
+shang	sh ang
+shao	sh ao
+she	sh e
+shei	sh ei
+shen	sh en
+sheng	sh eng
+shi	sh ir
+shou	sh ou
+shu	sh u
+shua	sh ua
+shuai	sh uai
+shuan	sh uan
+shuang	sh uang
+shui	sh ui
+shun	sh un
+shuo	sh uo
+si	s i0
+song	s ong
+sou	s ou
+su	s u
+suan	s uan
+sui	s ui
+sun	s un
+suo	s uo
+ta	t a
+tai	t ai
+tan	t an
+tang	t ang
+tao	t ao
+te	t e
+tei	t ei
+teng	t eng
+ti	t i
+tian	t ian
+tiao	t iao
+tie	t ie
+ting	t ing
+tong	t ong
+tou	t ou
+tu	t u
+tuan	t uan
+tui	t ui
+tun	t un
+tuo	t uo
+wa	w a
+wai	w ai
+wan	w an
+wang	w ang
+wei	w ei
+wen	w en
+weng	w eng
+wo	w o
+wu	w u
+xi	x i
+xia	x ia
+xian	x ian
+xiang	x iang
+xiao	x iao
+xie	x ie
+xin	x in
+xing	x ing
+xiong	x iong
+xiu	x iu
+xu	x v
+xv	x v
+xuan	x van
+xvan	x van
+xue	x ve
+xve	x ve
+xun	x vn
+xvn	x vn
+ya	y a
+yan	y En
+yang	y ang
+yao	y ao
+ye	y E
+yi	y i
+yin	y in
+ying	y ing
+yo	y o
+yong	y ong
+you	y ou
+yu	y v
+yv	y v
+yuan	y van
+yvan	y van
+yue	y ve
+yve	y ve
+yun	y vn
+yvn	y vn
+za	z a
+zai	z ai
+zan	z an
+zang	z ang
+zao	z ao
+ze	z e
+zei	z ei
+zen	z en
+zeng	z eng
+zha	zh a
+zhai	zh ai
+zhan	zh an
+zhang	zh ang
+zhao	zh ao
+zhe	zh e
+zhei	zh ei
+zhen	zh en
+zheng	zh eng
+zhi	zh ir
+zhong	zh ong
+zhou	zh ou
+zhu	zh u
+zhua	zh ua
+zhuai	zh uai
+zhuan	zh uan
+zhuang	zh uang
+zhui	zh ui
+zhun	zh un
+zhuo	zh uo
+zi	z i0
+zong	z ong
+zou	z ou
+zu	z u
+zuan	z uan
+zui	z ui
+zun	z un
+zuo	z uo
diff --git a/utils/tts/gpt_sovits/text/symbols.py b/utils/tts/gpt_sovits/text/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..70499492c3a2fac668847b1317e6e213cb086ece
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/symbols.py
@@ -0,0 +1,401 @@
+import os
+
+# punctuation = ['!', '?', '…', ",", ".","@"]#@是SP停顿
+punctuation = ["!", "?", "…", ",", "."]  # @是SP停顿
+punctuation.append("-")
+pu_symbols = punctuation + ["SP", "SP2", "SP3", "UNK"]
+# pu_symbols = punctuation + ["SP", 'SP2', 'SP3','SP4', "UNK"]
+pad = "_"
+
+c = [
+    "AA",
+    "EE",
+    "OO",
+    "b",
+    "c",
+    "ch",
+    "d",
+    "f",
+    "g",
+    "h",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "p",
+    "q",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "w",
+    "x",
+    "y",
+    "z",
+    "zh",
+]
+v = [
+    "E1",
+    "En1",
+    "a1",
+    "ai1",
+    "an1",
+    "ang1",
+    "ao1",
+    "e1",
+    "ei1",
+    "en1",
+    "eng1",
+    "er1",
+    "i1",
+    "i01",
+    "ia1",
+    "ian1",
+    "iang1",
+    "iao1",
+    "ie1",
+    "in1",
+    "ing1",
+    "iong1",
+    "ir1",
+    "iu1",
+    "o1",
+    "ong1",
+    "ou1",
+    "u1",
+    "ua1",
+    "uai1",
+    "uan1",
+    "uang1",
+    "ui1",
+    "un1",
+    "uo1",
+    "v1",
+    "van1",
+    "ve1",
+    "vn1",
+    "E2",
+    "En2",
+    "a2",
+    "ai2",
+    "an2",
+    "ang2",
+    "ao2",
+    "e2",
+    "ei2",
+    "en2",
+    "eng2",
+    "er2",
+    "i2",
+    "i02",
+    "ia2",
+    "ian2",
+    "iang2",
+    "iao2",
+    "ie2",
+    "in2",
+    "ing2",
+    "iong2",
+    "ir2",
+    "iu2",
+    "o2",
+    "ong2",
+    "ou2",
+    "u2",
+    "ua2",
+    "uai2",
+    "uan2",
+    "uang2",
+    "ui2",
+    "un2",
+    "uo2",
+    "v2",
+    "van2",
+    "ve2",
+    "vn2",
+    "E3",
+    "En3",
+    "a3",
+    "ai3",
+    "an3",
+    "ang3",
+    "ao3",
+    "e3",
+    "ei3",
+    "en3",
+    "eng3",
+    "er3",
+    "i3",
+    "i03",
+    "ia3",
+    "ian3",
+    "iang3",
+    "iao3",
+    "ie3",
+    "in3",
+    "ing3",
+    "iong3",
+    "ir3",
+    "iu3",
+    "o3",
+    "ong3",
+    "ou3",
+    "u3",
+    "ua3",
+    "uai3",
+    "uan3",
+    "uang3",
+    "ui3",
+    "un3",
+    "uo3",
+    "v3",
+    "van3",
+    "ve3",
+    "vn3",
+    "E4",
+    "En4",
+    "a4",
+    "ai4",
+    "an4",
+    "ang4",
+    "ao4",
+    "e4",
+    "ei4",
+    "en4",
+    "eng4",
+    "er4",
+    "i4",
+    "i04",
+    "ia4",
+    "ian4",
+    "iang4",
+    "iao4",
+    "ie4",
+    "in4",
+    "ing4",
+    "iong4",
+    "ir4",
+    "iu4",
+    "o4",
+    "ong4",
+    "ou4",
+    "u4",
+    "ua4",
+    "uai4",
+    "uan4",
+    "uang4",
+    "ui4",
+    "un4",
+    "uo4",
+    "v4",
+    "van4",
+    "ve4",
+    "vn4",
+    "E5",
+    "En5",
+    "a5",
+    "ai5",
+    "an5",
+    "ang5",
+    "ao5",
+    "e5",
+    "ei5",
+    "en5",
+    "eng5",
+    "er5",
+    "i5",
+    "i05",
+    "ia5",
+    "ian5",
+    "iang5",
+    "iao5",
+    "ie5",
+    "in5",
+    "ing5",
+    "iong5",
+    "ir5",
+    "iu5",
+    "o5",
+    "ong5",
+    "ou5",
+    "u5",
+    "ua5",
+    "uai5",
+    "uan5",
+    "uang5",
+    "ui5",
+    "un5",
+    "uo5",
+    "v5",
+    "van5",
+    "ve5",
+    "vn5",
+]
+
+v_without_tone = [
+    "E",
+    "En",
+    "a",
+    "ai",
+    "an",
+    "ang",
+    "ao",
+    "e",
+    "ei",
+    "en",
+    "eng",
+    "er",
+    "i",
+    "i0",
+    "ia",
+    "ian",
+    "iang",
+    "iao",
+    "ie",
+    "in",
+    "ing",
+    "iong",
+    "ir",
+    "iu",
+    "o",
+    "ong",
+    "ou",
+    "u",
+    "ua",
+    "uai",
+    "uan",
+    "uang",
+    "ui",
+    "un",
+    "uo",
+    "v",
+    "van",
+    "ve",
+    "vn",
+]
+
+# japanese
+ja_symbols = [
+    "I",
+    "N",
+    "U",
+    "a",
+    "b",
+    "by",
+    "ch",
+    "cl",
+    "d",
+    "dy",
+    "e",
+    "f",
+    "g",
+    "gy",
+    "h",
+    "hy",
+    "i",
+    "j",
+    "k",
+    "ky",
+    "m",
+    "my",
+    "n",
+    "ny",
+    "o",
+    "p",
+    "py",
+    "r",
+    "ry",
+    "s",
+    "sh",
+    "t",
+    "ts",
+    "u",
+    "v",
+    "w",
+    "y",
+    "z",
+    # "[", #上升调型
+    # "]", #下降调型
+    # "$", #结束符
+    # "^", #开始符
+]
+
+arpa = {
+    "AH0",
+    "S",
+    "AH1",
+    "EY2",
+    "AE2",
+    "EH0",
+    "OW2",
+    "UH0",
+    "NG",
+    "B",
+    "G",
+    "AY0",
+    "M",
+    "AA0",
+    "F",
+    "AO0",
+    "ER2",
+    "UH1",
+    "IY1",
+    "AH2",
+    "DH",
+    "IY0",
+    "EY1",
+    "IH0",
+    "K",
+    "N",
+    "W",
+    "IY2",
+    "T",
+    "AA1",
+    "ER1",
+    "EH2",
+    "OY0",
+    "UH2",
+    "UW1",
+    "Z",
+    "AW2",
+    "AW1",
+    "V",
+    "UW2",
+    "AA2",
+    "ER",
+    "AW0",
+    "UW0",
+    "R",
+    "OW1",
+    "EH1",
+    "ZH",
+    "AE0",
+    "IH2",
+    "IH",
+    "Y",
+    "JH",
+    "P",
+    "AY1",
+    "EY0",
+    "OY2",
+    "TH",
+    "HH",
+    "D",
+    "ER0",
+    "CH",
+    "AO1",
+    "AE1",
+    "AO2",
+    "OY1",
+    "AY2",
+    "IH1",
+    "OW0",
+    "L",
+    "SH",
+}
+
+symbols = [pad] + c + v + ja_symbols + pu_symbols + list(arpa)
+symbols = sorted(set(symbols))
+if __name__ == "__main__":
+    print(len(symbols))
diff --git a/utils/tts/gpt_sovits/text/tone_sandhi.py b/utils/tts/gpt_sovits/text/tone_sandhi.py
new file mode 100644
index 0000000000000000000000000000000000000000..c557dad118631f2aa9b75d84e8775bea8c34eaf0
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/tone_sandhi.py
@@ -0,0 +1,806 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import List
+from typing import Tuple
+
+import jieba_fast as jieba
+from pypinyin import lazy_pinyin
+from pypinyin import Style
+
+
+class ToneSandhi:
+    def __init__(self):
+        self.must_neural_tone_words = {
+            "麻烦",
+            "麻利",
+            "鸳鸯",
+            "高粱",
+            "骨头",
+            "骆驼",
+            "马虎",
+            "首饰",
+            "馒头",
+            "馄饨",
+            "风筝",
+            "难为",
+            "队伍",
+            "阔气",
+            "闺女",
+            "门道",
+            "锄头",
+            "铺盖",
+            "铃铛",
+            "铁匠",
+            "钥匙",
+            "里脊",
+            "里头",
+            "部分",
+            "那么",
+            "道士",
+            "造化",
+            "迷糊",
+            "连累",
+            "这么",
+            "这个",
+            "运气",
+            "过去",
+            "软和",
+            "转悠",
+            "踏实",
+            "跳蚤",
+            "跟头",
+            "趔趄",
+            "财主",
+            "豆腐",
+            "讲究",
+            "记性",
+            "记号",
+            "认识",
+            "规矩",
+            "见识",
+            "裁缝",
+            "补丁",
+            "衣裳",
+            "衣服",
+            "衙门",
+            "街坊",
+            "行李",
+            "行当",
+            "蛤蟆",
+            "蘑菇",
+            "薄荷",
+            "葫芦",
+            "葡萄",
+            "萝卜",
+            "荸荠",
+            "苗条",
+            "苗头",
+            "苍蝇",
+            "芝麻",
+            "舒服",
+            "舒坦",
+            "舌头",
+            "自在",
+            "膏药",
+            "脾气",
+            "脑袋",
+            "脊梁",
+            "能耐",
+            "胳膊",
+            "胭脂",
+            "胡萝",
+            "胡琴",
+            "胡同",
+            "聪明",
+            "耽误",
+            "耽搁",
+            "耷拉",
+            "耳朵",
+            "老爷",
+            "老实",
+            "老婆",
+            "老头",
+            "老太",
+            "翻腾",
+            "罗嗦",
+            "罐头",
+            "编辑",
+            "结实",
+            "红火",
+            "累赘",
+            "糨糊",
+            "糊涂",
+            "精神",
+            "粮食",
+            "簸箕",
+            "篱笆",
+            "算计",
+            "算盘",
+            "答应",
+            "笤帚",
+            "笑语",
+            "笑话",
+            "窟窿",
+            "窝囊",
+            "窗户",
+            "稳当",
+            "稀罕",
+            "称呼",
+            "秧歌",
+            "秀气",
+            "秀才",
+            "福气",
+            "祖宗",
+            "砚台",
+            "码头",
+            "石榴",
+            "石头",
+            "石匠",
+            "知识",
+            "眼睛",
+            "眯缝",
+            "眨巴",
+            "眉毛",
+            "相声",
+            "盘算",
+            "白净",
+            "痢疾",
+            "痛快",
+            "疟疾",
+            "疙瘩",
+            "疏忽",
+            "畜生",
+            "生意",
+            "甘蔗",
+            "琵琶",
+            "琢磨",
+            "琉璃",
+            "玻璃",
+            "玫瑰",
+            "玄乎",
+            "狐狸",
+            "状元",
+            "特务",
+            "牲口",
+            "牙碜",
+            "牌楼",
+            "爽快",
+            "爱人",
+            "热闹",
+            "烧饼",
+            "烟筒",
+            "烂糊",
+            "点心",
+            "炊帚",
+            "灯笼",
+            "火候",
+            "漂亮",
+            "滑溜",
+            "溜达",
+            "温和",
+            "清楚",
+            "消息",
+            "浪头",
+            "活泼",
+            "比方",
+            "正经",
+            "欺负",
+            "模糊",
+            "槟榔",
+            "棺材",
+            "棒槌",
+            "棉花",
+            "核桃",
+            "栅栏",
+            "柴火",
+            "架势",
+            "枕头",
+            "枇杷",
+            "机灵",
+            "本事",
+            "木头",
+            "木匠",
+            "朋友",
+            "月饼",
+            "月亮",
+            "暖和",
+            "明白",
+            "时候",
+            "新鲜",
+            "故事",
+            "收拾",
+            "收成",
+            "提防",
+            "挖苦",
+            "挑剔",
+            "指甲",
+            "指头",
+            "拾掇",
+            "拳头",
+            "拨弄",
+            "招牌",
+            "招呼",
+            "抬举",
+            "护士",
+            "折腾",
+            "扫帚",
+            "打量",
+            "打算",
+            "打点",
+            "打扮",
+            "打听",
+            "打发",
+            "扎实",
+            "扁担",
+            "戒指",
+            "懒得",
+            "意识",
+            "意思",
+            "情形",
+            "悟性",
+            "怪物",
+            "思量",
+            "怎么",
+            "念头",
+            "念叨",
+            "快活",
+            "忙活",
+            "志气",
+            "心思",
+            "得罪",
+            "张罗",
+            "弟兄",
+            "开通",
+            "应酬",
+            "庄稼",
+            "干事",
+            "帮手",
+            "帐篷",
+            "希罕",
+            "师父",
+            "师傅",
+            "巴结",
+            "巴掌",
+            "差事",
+            "工夫",
+            "岁数",
+            "屁股",
+            "尾巴",
+            "少爷",
+            "小气",
+            "小伙",
+            "将就",
+            "对头",
+            "对付",
+            "寡妇",
+            "家伙",
+            "客气",
+            "实在",
+            "官司",
+            "学问",
+            "学生",
+            "字号",
+            "嫁妆",
+            "媳妇",
+            "媒人",
+            "婆家",
+            "娘家",
+            "委屈",
+            "姑娘",
+            "姐夫",
+            "妯娌",
+            "妥当",
+            "妖精",
+            "奴才",
+            "女婿",
+            "头发",
+            "太阳",
+            "大爷",
+            "大方",
+            "大意",
+            "大夫",
+            "多少",
+            "多么",
+            "外甥",
+            "壮实",
+            "地道",
+            "地方",
+            "在乎",
+            "困难",
+            "嘴巴",
+            "嘱咐",
+            "嘟囔",
+            "嘀咕",
+            "喜欢",
+            "喇嘛",
+            "喇叭",
+            "商量",
+            "唾沫",
+            "哑巴",
+            "哈欠",
+            "哆嗦",
+            "咳嗽",
+            "和尚",
+            "告诉",
+            "告示",
+            "含糊",
+            "吓唬",
+            "后头",
+            "名字",
+            "名堂",
+            "合同",
+            "吆喝",
+            "叫唤",
+            "口袋",
+            "厚道",
+            "厉害",
+            "千斤",
+            "包袱",
+            "包涵",
+            "匀称",
+            "勤快",
+            "动静",
+            "动弹",
+            "功夫",
+            "力气",
+            "前头",
+            "刺猬",
+            "刺激",
+            "别扭",
+            "利落",
+            "利索",
+            "利害",
+            "分析",
+            "出息",
+            "凑合",
+            "凉快",
+            "冷战",
+            "冤枉",
+            "冒失",
+            "养活",
+            "关系",
+            "先生",
+            "兄弟",
+            "便宜",
+            "使唤",
+            "佩服",
+            "作坊",
+            "体面",
+            "位置",
+            "似的",
+            "伙计",
+            "休息",
+            "什么",
+            "人家",
+            "亲戚",
+            "亲家",
+            "交情",
+            "云彩",
+            "事情",
+            "买卖",
+            "主意",
+            "丫头",
+            "丧气",
+            "两口",
+            "东西",
+            "东家",
+            "世故",
+            "不由",
+            "不在",
+            "下水",
+            "下巴",
+            "上头",
+            "上司",
+            "丈夫",
+            "丈人",
+            "一辈",
+            "那个",
+            "菩萨",
+            "父亲",
+            "母亲",
+            "咕噜",
+            "邋遢",
+            "费用",
+            "冤家",
+            "甜头",
+            "介绍",
+            "荒唐",
+            "大人",
+            "泥鳅",
+            "幸福",
+            "熟悉",
+            "计划",
+            "扑腾",
+            "蜡烛",
+            "姥爷",
+            "照顾",
+            "喉咙",
+            "吉他",
+            "弄堂",
+            "蚂蚱",
+            "凤凰",
+            "拖沓",
+            "寒碜",
+            "糟蹋",
+            "倒腾",
+            "报复",
+            "逻辑",
+            "盘缠",
+            "喽啰",
+            "牢骚",
+            "咖喱",
+            "扫把",
+            "惦记",
+        }
+        self.must_not_neural_tone_words = {
+            "男子",
+            "女子",
+            "分子",
+            "原子",
+            "量子",
+            "莲子",
+            "石子",
+            "瓜子",
+            "电子",
+            "人人",
+            "虎虎",
+            "幺幺",
+            "干嘛",
+            "学子",
+            "哈哈",
+            "数数",
+            "袅袅",
+            "局地",
+            "以下",
+            "娃哈哈",
+            "花花草草",
+            "留得",
+            "耕地",
+            "想想",
+            "熙熙",
+            "攘攘",
+            "卵子",
+            "死死",
+            "冉冉",
+            "恳恳",
+            "佼佼",
+            "吵吵",
+            "打打",
+            "考考",
+            "整整",
+            "莘莘",
+            "落地",
+            "算子",
+            "家家户户",
+            "青青",
+        }
+        self.punc = "：，；。？！“”‘’':,;.?!"
+
+    # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
+    # e.g.
+    # word: "家里"
+    # pos: "s"
+    # finals: ['ia1', 'i3']
+    def _neural_sandhi(self, word: str, pos: str, finals: List[str]) -> List[str]:
+        # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
+        for j, item in enumerate(word):
+            if (
+                j - 1 >= 0
+                and item == word[j - 1]
+                and pos[0] in {"n", "v", "a"}
+                and word not in self.must_not_neural_tone_words
+            ):
+                finals[j] = finals[j][:-1] + "5"
+        ge_idx = word.find("个")
+        if len(word) >= 1 and word[-1] in "吧呢哈啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
+            finals[-1] = finals[-1][:-1] + "5"
+        elif len(word) >= 1 and word[-1] in "的地得":
+            finals[-1] = finals[-1][:-1] + "5"
+        # e.g. 走了, 看着, 去过
+        elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
+            finals[-1] = finals[-1][:-1] + "5"
+        elif (
+            len(word) > 1
+            and word[-1] in "们子"
+            and pos in {"r", "n"}
+            and word not in self.must_not_neural_tone_words
+        ):
+            finals[-1] = finals[-1][:-1] + "5"
+        # e.g. 桌上, 地下, 家里
+        elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
+            finals[-1] = finals[-1][:-1] + "5"
+        # e.g. 上来, 下去
+        elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
+            finals[-1] = finals[-1][:-1] + "5"
+        # 个做量词
+        elif (
+            ge_idx >= 1
+            and (word[ge_idx - 1].isnumeric() or word[ge_idx - 1] in "几有两半多各整每做是")
+        ) or word == "个":
+            finals[ge_idx] = finals[ge_idx][:-1] + "5"
+        else:
+            if (
+                word in self.must_neural_tone_words
+                or word[-2:] in self.must_neural_tone_words
+            ):
+                finals[-1] = finals[-1][:-1] + "5"
+
+        word_list = self._split_word(word)
+        finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
+        for i, word in enumerate(word_list):
+            # conventional neural in Chinese
+            if (
+                word in self.must_neural_tone_words
+                or word[-2:] in self.must_neural_tone_words
+            ):
+                finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
+        finals = sum(finals_list, [])
+        return finals
+
+    def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
+        # e.g. 看不懂
+        if len(word) == 3 and word[1] == "不":
+            finals[1] = finals[1][:-1] + "5"
+        else:
+            for i, char in enumerate(word):
+                # "不" before tone4 should be bu2, e.g. 不怕
+                if char == "不" and i + 1 < len(word) and finals[i + 1][-1] == "4":
+                    finals[i] = finals[i][:-1] + "2"
+        return finals
+
+    def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
+        # "一" in number sequences, e.g. 一零零, 二一零
+        if word.find("一") != -1 and all(
+            [item.isnumeric() for item in word if item != "一"]
+        ):
+            return finals
+        # "一" between reduplication words shold be yi5, e.g. 看一看
+        elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
+            finals[1] = finals[1][:-1] + "5"
+        # when "一" is ordinal word, it should be yi1
+        elif word.startswith("第一"):
+            finals[1] = finals[1][:-1] + "1"
+        else:
+            for i, char in enumerate(word):
+                if char == "一" and i + 1 < len(word):
+                    # "一" before tone4 should be yi2, e.g. 一段
+                    if finals[i + 1][-1] == "4":
+                        finals[i] = finals[i][:-1] + "2"
+                    # "一" before non-tone4 should be yi4, e.g. 一天
+                    else:
+                        # "一" 后面如果是标点，还读一声
+                        if word[i + 1] not in self.punc:
+                            finals[i] = finals[i][:-1] + "4"
+        return finals
+
+    def _split_word(self, word: str) -> List[str]:
+        word_list = jieba.cut_for_search(word)
+        word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
+        first_subword = word_list[0]
+        first_begin_idx = word.find(first_subword)
+        if first_begin_idx == 0:
+            second_subword = word[len(first_subword) :]
+            new_word_list = [first_subword, second_subword]
+        else:
+            second_subword = word[: -len(first_subword)]
+            new_word_list = [second_subword, first_subword]
+        return new_word_list
+
+    def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
+        if len(word) == 2 and self._all_tone_three(finals):
+            finals[0] = finals[0][:-1] + "2"
+        elif len(word) == 3:
+            word_list = self._split_word(word)
+            if self._all_tone_three(finals):
+                #  disyllabic + monosyllabic, e.g. 蒙古/包
+                if len(word_list[0]) == 2:
+                    finals[0] = finals[0][:-1] + "2"
+                    finals[1] = finals[1][:-1] + "2"
+                #  monosyllabic + disyllabic, e.g. 纸/老虎
+                elif len(word_list[0]) == 1:
+                    finals[1] = finals[1][:-1] + "2"
+            else:
+                finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
+                if len(finals_list) == 2:
+                    for i, sub in enumerate(finals_list):
+                        # e.g. 所有/人
+                        if self._all_tone_three(sub) and len(sub) == 2:
+                            finals_list[i][0] = finals_list[i][0][:-1] + "2"
+                        # e.g. 好/喜欢
+                        elif (
+                            i == 1
+                            and not self._all_tone_three(sub)
+                            and finals_list[i][0][-1] == "3"
+                            and finals_list[0][-1][-1] == "3"
+                        ):
+                            finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
+                        finals = sum(finals_list, [])
+        # split idiom into two words who's length is 2
+        elif len(word) == 4:
+            finals_list = [finals[:2], finals[2:]]
+            finals = []
+            for sub in finals_list:
+                if self._all_tone_three(sub):
+                    sub[0] = sub[0][:-1] + "2"
+                finals += sub
+
+        return finals
+
+    def _all_tone_three(self, finals: List[str]) -> bool:
+        return all(x[-1] == "3" for x in finals)
+
+    # merge "不" and the word behind it
+    # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
+    def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        last_word = ""
+        for word, pos in seg:
+            if last_word == "不":
+                word = last_word + word
+            if word != "不":
+                new_seg.append((word, pos))
+            last_word = word[:]
+        if last_word == "不":
+            new_seg.append((last_word, "d"))
+            last_word = ""
+        return new_seg
+
+    # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
+    # function 2: merge single  "一" and the word behind it
+    # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
+    # e.g.
+    # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
+    # output seg: [['听一听', 'v']]
+    def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        # function 1
+        for i, (word, pos) in enumerate(seg):
+            if (
+                i - 1 >= 0
+                and word == "一"
+                and i + 1 < len(seg)
+                and seg[i - 1][0] == seg[i + 1][0]
+                and seg[i - 1][1] == "v"
+                and seg[i + 1][1] == "v"
+            ):
+                new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
+            else:
+                if (
+                    i - 2 >= 0
+                    and seg[i - 1][0] == "一"
+                    and seg[i - 2][0] == word
+                    and pos == "v"
+                ):
+                    continue
+                else:
+                    new_seg.append([word, pos])
+        seg = new_seg
+        new_seg = []
+        # function 2
+        for i, (word, pos) in enumerate(seg):
+            if new_seg and new_seg[-1][0] == "一":
+                new_seg[-1][0] = new_seg[-1][0] + word
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    # the first and the second words are all_tone_three
+    def _merge_continuous_three_tones(
+        self, seg: List[Tuple[str, str]]
+    ) -> List[Tuple[str, str]]:
+        new_seg = []
+        sub_finals_list = [
+            lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
+            for (word, pos) in seg
+        ]
+        assert len(sub_finals_list) == len(seg)
+        merge_last = [False] * len(seg)
+        for i, (word, pos) in enumerate(seg):
+            if (
+                i - 1 >= 0
+                and self._all_tone_three(sub_finals_list[i - 1])
+                and self._all_tone_three(sub_finals_list[i])
+                and not merge_last[i - 1]
+            ):
+                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
+                if (
+                    not self._is_reduplication(seg[i - 1][0])
+                    and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
+                ):
+                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+                    merge_last[i] = True
+                else:
+                    new_seg.append([word, pos])
+            else:
+                new_seg.append([word, pos])
+
+        return new_seg
+
+    def _is_reduplication(self, word: str) -> bool:
+        return len(word) == 2 and word[0] == word[1]
+
+    # the last char of first word and the first char of second word is tone_three
+    def _merge_continuous_three_tones_2(
+        self, seg: List[Tuple[str, str]]
+    ) -> List[Tuple[str, str]]:
+        new_seg = []
+        sub_finals_list = [
+            lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
+            for (word, pos) in seg
+        ]
+        assert len(sub_finals_list) == len(seg)
+        merge_last = [False] * len(seg)
+        for i, (word, pos) in enumerate(seg):
+            if (
+                i - 1 >= 0
+                and sub_finals_list[i - 1][-1][-1] == "3"
+                and sub_finals_list[i][0][-1] == "3"
+                and not merge_last[i - 1]
+            ):
+                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
+                if (
+                    not self._is_reduplication(seg[i - 1][0])
+                    and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
+                ):
+                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+                    merge_last[i] = True
+                else:
+                    new_seg.append([word, pos])
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        for i, (word, pos) in enumerate(seg):
+            if i - 1 >= 0 and word == "儿" and seg[i - 1][0] != "#":
+                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    def _merge_reduplication(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        new_seg = []
+        for i, (word, pos) in enumerate(seg):
+            if new_seg and word == new_seg[-1][0]:
+                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    def pre_merge_for_modify(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
+        seg = self._merge_bu(seg)
+        try:
+            seg = self._merge_yi(seg)
+        except:
+            print("_merge_yi failed")
+        seg = self._merge_reduplication(seg)
+        try:
+            seg = self._merge_continuous_three_tones(seg)
+        except:
+            print("_merge_continuous_three_tones failed")
+        try:
+            seg = self._merge_continuous_three_tones_2(seg)
+        except:
+            print("_merge_continuous_three_tones_2 failed")
+
+        seg = self._merge_er(seg)
+        return seg
+
+    def modified_tone(self, word: str, pos: str, finals: List[str]) -> List[str]:
+        finals = self._bu_sandhi(word, finals)
+        finals = self._yi_sandhi(word, finals)
+        finals = self._neural_sandhi(word, pos, finals)
+        finals = self._three_sandhi(word, finals)
+        return finals
diff --git a/utils/tts/gpt_sovits/text/zh_normalization/README.md b/utils/tts/gpt_sovits/text/zh_normalization/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..92eea9f54630dfd41dfc3ce53bc511cc7595062c
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/zh_normalization/README.md
@@ -0,0 +1,16 @@
+## Supported NSW (Non-Standard-Word) Normalization
+
+|NSW type|raw|normalized|
+|:--|:-|:-|
+|serial number|电影中梁朝伟扮演的陈永仁的编号27149|电影中梁朝伟扮演的陈永仁的编号二七一四九|
+|cardinal|这块黄金重达324.75克<br>我们班的最高总分为583分|这块黄金重达三百二十四点七五克<br>我们班的最高总分为五百八十三分|
+|numeric range |12\~23<br>-1.5\~2|十二到二十三<br>负一点五到二|
+|date|她出生于86年8月18日，她弟弟出生于1995年3月1日|她出生于八六年八月十八日， 她弟弟出生于一九九五年三月一日|
+|time|等会请在12:05请通知我|等会请在十二点零五分请通知我
+|temperature|今天的最低气温达到-10°C|今天的最低气温达到零下十度
+|fraction|现场有7/12的观众投出了赞成票|现场有十二分之七的观众投出了赞成票|
+|percentage|明天有62％的概率降雨|明天有百分之六十二的概率降雨|
+|money|随便来几个价格12块5，34.5元，20.1万|随便来几个价格十二块五，三十四点五元，二十点一万|
+|telephone|这是固话0421-33441122<br>这是手机+86 18544139121|这是固话零四二一三三四四一一二二<br>这是手机八六一八五四四一三九一二一|
+## References
+[Pull requests #658 of DeepSpeech](https://github.com/PaddlePaddle/DeepSpeech/pull/658/files)
diff --git a/utils/tts/gpt_sovits/text/zh_normalization/__init__.py b/utils/tts/gpt_sovits/text/zh_normalization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e087a80d755abe15ee90500483f6da5f02f6d4e
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/zh_normalization/__init__.py
@@ -0,0 +1,14 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from utils.tts.gpt_sovits.text.zh_normalization.text_normlization import *
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diff --git a/utils/tts/gpt_sovits/text/zh_normalization/char_convert.py b/utils/tts/gpt_sovits/text/zh_normalization/char_convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..dcf95d72861348b36e4f21c31350f47e56934fc1
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/zh_normalization/char_convert.py
@@ -0,0 +1,46 @@
+# coding=utf-8
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Traditional and simplified Chinese conversion, a simplified character may correspond to multiple traditional characters.
+"""
+simplified_charcters = 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+
+traditional_characters = 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鐔鐗馗鐙鐝鐠鐡鐦鐨鐩鐫鐬鐱鐳鐶鐻鐽鐿鑀鑅鑌鑐鑕鑚鑛鑢鑤鑥鑪鑭鑯鑱鑴鑵鑷钁钃镻閆閈閌閎閒閔閗閟閡関閤閤閧閬閲閹閺閻閼閽閿闇闉闋闐闑闒闓闘闚闞闟闠闤闥阞阢阤阨阬阯阹阼阽陁陑陔陛陜陡陥陬騭陴険陼陾隂隃隈隒隗隞隠隣隤隩隮隰顴隳隷隹雂雈雉雊雎雑雒雗雘雚雝雟雩雰雱驛霂霅霈霊霑霒霓霙霝霢霣霤霨霩霪霫霮靁靆靉靑靚靣靦靪靮靰靳靷靸靺靼靿鞀鞃鞄鞌鞗鞙鞚鞝鞞鞡鞣鞨鞫鞬鞮鞶鞹鞾韃韅韉馱韍韎韔韖韘韝韞韡韣韭韮韱韹韺頀颳頄頇頊頍頎頏頒頖頞頠頫頬顱頯頲頴頼顇顋顑顒顓顔顕顚顜顢顣顬顳颭颮颱颶颸颺颻颽颾颿飀飂飈飌飜飡飣飤飥飩飫飮飱飶餀餂餄餎餇餈餑餔餕餖餗餚餛餜餟餠餤餧餩餪餫餬餮餱餲餳餺餻餼餽餿饁饅饇饉饊饍饎饐饘饟饢馘馥馝馡馣騮騾馵馹駃駄駅駆駉駋駑駓駔駗駘駙駜駡駢駪駬駰駴駸駹駽駾騂騄騅騆騉騋騍騏驎騑騒験騕騖騠騢騣騤騧驤騵騶騸騺驀驂驃驄驆驈驊驌驍驎驏驒驔驖驙驦驩驫骺鯁骫骭骯骱骴骶骷髏骾髁髂髄髆髈髐髑髕髖髙髝髞髟髡髣髧髪髫髭髯髲髳髹髺髽髾鬁鬃鬅鬈鬋鬎鬏鬐鬑鬒鬖鬗鬘鬙鬠鬣鬪鬫鬬鬮鬯鬰鬲鬵鬷魆魈魊魋魍魎魑魖鰾魛魟魣魦魨魬魴魵魸鮀鮁鮆鮌鮎鮑鮒鮓鮚鮞鮟鱇鮠鮦鮨鮪鮭鮶鮸鮿鯀鯄鯆鯇鯈鯔鯕鯖鯗鯙鯠鯤鯥鯫鯰鯷鯸鯿鰂鰆鶼鰉鰋鰐鰒鰕鰛鰜鰣鰤鰥鰦鰨鰩鰮鰳鰶鰷鱺鰼鰽鱀鱄鱅鱆鱈鱎鱐鱓鱔鱖鱘鱟鱠鱣鱨鱭鱮鱲鱵鱻鲅鳦鳧鳯鳲鳷鳻鴂鴃鴄鴆鴈鴎鴒鴔鴗鴛鴦鴝鵒鴟鴠鴢鴣鴥鴯鶓鴳鴴鴷鴽鵀鵁鵂鵓鵖鵙鵜鶘鵞鵟鵩鵪鵫鵵鵷鵻鵾鶂鶊鶏鶒鶖鶗鶡鶤鶦鶬鶱鶲鶵鶸鶹鶺鶿鷀鷁鷃鷄鷇鷈鷉鷊鷏鷓鷕鷖鷙鷞鷟鷥鷦鷯鷩鷫鷭鷳鷴鷽鷾鷿鸂鸇鸊鸏鸑鸒鸓鸕鸛鸜鸝鹸鹹鹺麀麂麃麄麇麋麌麐麑麒麚麛麝麤麩麪麫麮麯麰麺麾黁黈黌黢黒黓黕黙黝黟黥黦黧黮黰黱黲黶黹黻黼黽黿鼂鼃鼅鼈鼉鼏鼐鼒鼕鼖鼙鼚鼛鼡鼩鼱鼪鼫鼯鼷鼽齁齆齇齈齉齌齎齏齔齕齗齙齚齜齞齟齬齠齢齣齧齩齮齯齰齱齵齾龎龑龒龔龖龘龝龡龢龤'
+
+assert len(simplified_charcters) == len(simplified_charcters)
+
+s2t_dict = {}
+t2s_dict = {}
+for i, item in enumerate(simplified_charcters):
+    s2t_dict[item] = traditional_characters[i]
+    t2s_dict[traditional_characters[i]] = item
+
+
+def tranditional_to_simplified(text: str) -> str:
+    return "".join(
+        [t2s_dict[item] if item in t2s_dict else item for item in text])
+
+
+def simplified_to_traditional(text: str) -> str:
+    return "".join(
+        [s2t_dict[item] if item in s2t_dict else item for item in text])
+
+
+if __name__ == "__main__":
+    text = "一般是指存取一個應用程式啟動時始終顯示在網站或網頁瀏覽器中的一個或多個初始網頁等畫面存在的站點"
+    print(text)
+    text_simple = tranditional_to_simplified(text)
+    print(text_simple)
+    text_traditional = simplified_to_traditional(text_simple)
+    print(text_traditional)
diff --git a/utils/tts/gpt_sovits/text/zh_normalization/chronology.py b/utils/tts/gpt_sovits/text/zh_normalization/chronology.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea4558e2a7abba4ff454656b82e67b3f5c483bf2
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/zh_normalization/chronology.py
@@ -0,0 +1,134 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import re
+
+from .num import DIGITS
+from .num import num2str
+from .num import verbalize_cardinal
+from .num import verbalize_digit
+
+
+def _time_num2str(num_string: str) -> str:
+    """A special case for verbalizing number in time."""
+    result = num2str(num_string.lstrip('0'))
+    if num_string.startswith('0'):
+        result = DIGITS['0'] + result
+    return result
+
+
+# 时刻表达式
+RE_TIME = re.compile(r'([0-1]?[0-9]|2[0-3])'
+                     r':([0-5][0-9])'
+                     r'(:([0-5][0-9]))?')
+
+# 时间范围，如8:30-12:30
+RE_TIME_RANGE = re.compile(r'([0-1]?[0-9]|2[0-3])'
+                           r':([0-5][0-9])'
+                           r'(:([0-5][0-9]))?'
+                           r'(~|-)'
+                           r'([0-1]?[0-9]|2[0-3])'
+                           r':([0-5][0-9])'
+                           r'(:([0-5][0-9]))?')
+
+
+def replace_time(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+
+    is_range = len(match.groups()) > 5
+
+    hour = match.group(1)
+    minute = match.group(2)
+    second = match.group(4)
+
+    if is_range:
+        hour_2 = match.group(6)
+        minute_2 = match.group(7)
+        second_2 = match.group(9)
+
+    result = f"{num2str(hour)}点"
+    if minute.lstrip('0'):
+        if int(minute) == 30:
+            result += "半"
+        else:
+            result += f"{_time_num2str(minute)}分"
+    if second and second.lstrip('0'):
+        result += f"{_time_num2str(second)}秒"
+
+    if is_range:
+        result += "至"
+        result += f"{num2str(hour_2)}点"
+        if minute_2.lstrip('0'):
+            if int(minute) == 30:
+                result += "半"
+            else:
+                result += f"{_time_num2str(minute_2)}分"
+        if second_2 and second_2.lstrip('0'):
+            result += f"{_time_num2str(second_2)}秒"
+
+    return result
+
+
+RE_DATE = re.compile(r'(\d{4}|\d{2})年'
+                     r'((0?[1-9]|1[0-2])月)?'
+                     r'(((0?[1-9])|((1|2)[0-9])|30|31)([日号]))?')
+
+
+def replace_date(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    year = match.group(1)
+    month = match.group(3)
+    day = match.group(5)
+    result = ""
+    if year:
+        result += f"{verbalize_digit(year)}年"
+    if month:
+        result += f"{verbalize_cardinal(month)}月"
+    if day:
+        result += f"{verbalize_cardinal(day)}{match.group(9)}"
+    return result
+
+
+# 用 / 或者 - 分隔的 YY/MM/DD 或者 YY-MM-DD 日期
+RE_DATE2 = re.compile(
+    r'(\d{4})([- /.])(0[1-9]|1[012])\2(0[1-9]|[12][0-9]|3[01])')
+
+
+def replace_date2(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    year = match.group(1)
+    month = match.group(3)
+    day = match.group(4)
+    result = ""
+    if year:
+        result += f"{verbalize_digit(year)}年"
+    if month:
+        result += f"{verbalize_cardinal(month)}月"
+    if day:
+        result += f"{verbalize_cardinal(day)}日"
+    return result
diff --git a/utils/tts/gpt_sovits/text/zh_normalization/constants.py b/utils/tts/gpt_sovits/text/zh_normalization/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..6423ad74a5c9a92df35aebf04bebdf4050a292de
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/zh_normalization/constants.py
@@ -0,0 +1,62 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import re
+import string
+
+from pypinyin.constants import SUPPORT_UCS4
+
+# 全角半角转换
+# 英文字符全角 -> 半角映射表 (num: 52)
+F2H_ASCII_LETTERS = {
+    ord(char) + 65248: ord(char)
+    for char in string.ascii_letters
+}
+
+# 英文字符半角 -> 全角映射表
+H2F_ASCII_LETTERS = {value: key for key, value in F2H_ASCII_LETTERS.items()}
+
+# 数字字符全角 -> 半角映射表 (num: 10)
+F2H_DIGITS = {ord(char) + 65248: ord(char) for char in string.digits}
+# 数字字符半角 -> 全角映射表
+H2F_DIGITS = {value: key for key, value in F2H_DIGITS.items()}
+
+# 标点符号全角 -> 半角映射表 (num: 32)
+F2H_PUNCTUATIONS = {ord(char) + 65248: ord(char) for char in string.punctuation}
+# 标点符号半角 -> 全角映射表
+H2F_PUNCTUATIONS = {value: key for key, value in F2H_PUNCTUATIONS.items()}
+
+# 空格 (num: 1)
+F2H_SPACE = {'\u3000': ' '}
+H2F_SPACE = {' ': '\u3000'}
+
+# 非"有拼音的汉字"的字符串，可用于NSW提取
+if SUPPORT_UCS4:
+    RE_NSW = re.compile(r'(?:[^'
+                        r'\u3007'  # 〇
+                        r'\u3400-\u4dbf'  # CJK扩展A:[3400-4DBF]
+                        r'\u4e00-\u9fff'  # CJK基本:[4E00-9FFF]
+                        r'\uf900-\ufaff'  # CJK兼容:[F900-FAFF]
+                        r'\U00020000-\U0002A6DF'  # CJK扩展B:[20000-2A6DF]
+                        r'\U0002A703-\U0002B73F'  # CJK扩展C:[2A700-2B73F]
+                        r'\U0002B740-\U0002B81D'  # CJK扩展D:[2B740-2B81D]
+                        r'\U0002F80A-\U0002FA1F'  # CJK兼容扩展:[2F800-2FA1F]
+                        r'])+')
+else:
+    RE_NSW = re.compile(  # pragma: no cover
+        r'(?:[^'
+        r'\u3007'  # 〇
+        r'\u3400-\u4dbf'  # CJK扩展A:[3400-4DBF]
+        r'\u4e00-\u9fff'  # CJK基本:[4E00-9FFF]
+        r'\uf900-\ufaff'  # CJK兼容:[F900-FAFF]
+        r'])+')
diff --git a/utils/tts/gpt_sovits/text/zh_normalization/num.py b/utils/tts/gpt_sovits/text/zh_normalization/num.py
new file mode 100644
index 0000000000000000000000000000000000000000..d38d5a646e176026dc5ad36182c916953b347ba8
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/zh_normalization/num.py
@@ -0,0 +1,282 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Rules to verbalize numbers into Chinese characters.
+https://zh.wikipedia.org/wiki/中文数字#現代中文
+"""
+import re
+from collections import OrderedDict
+from typing import List
+
+DIGITS = {str(i): tran for i, tran in enumerate('零一二三四五六七八九')}
+UNITS = OrderedDict({
+    1: '十',
+    2: '百',
+    3: '千',
+    4: '万',
+    8: '亿',
+})
+
+COM_QUANTIFIERS = '(封|艘|把|目|套|段|人|所|朵|匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|毫|厘|(公)分|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|小时|旬|纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块|元|(亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|美|)元|(亿|千万|百万|万|千|百|十|)吨|(亿|千万|百万|万|千|百|)块|角|毛|分)'
+
+# 分数表达式
+RE_FRAC = re.compile(r'(-?)(\d+)/(\d+)')
+
+
+def replace_frac(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    nominator = match.group(2)
+    denominator = match.group(3)
+    sign: str = "负" if sign else ""
+    nominator: str = num2str(nominator)
+    denominator: str = num2str(denominator)
+    result = f"{sign}{denominator}分之{nominator}"
+    return result
+
+
+# 百分数表达式
+RE_PERCENTAGE = re.compile(r'(-?)(\d+(\.\d+)?)%')
+
+
+def replace_percentage(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    percent = match.group(2)
+    sign: str = "负" if sign else ""
+    percent: str = num2str(percent)
+    result = f"{sign}百分之{percent}"
+    return result
+
+
+# 整数表达式
+# 带负号的整数 -10
+RE_INTEGER = re.compile(r'(-)' r'(\d+)')
+
+
+def replace_negative_num(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    number = match.group(2)
+    sign: str = "负" if sign else ""
+    number: str = num2str(number)
+    result = f"{sign}{number}"
+    return result
+
+
+# 编号-无符号整形
+# 00078
+RE_DEFAULT_NUM = re.compile(r'\d{3}\d*')
+
+
+def replace_default_num(match):
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    number = match.group(0)
+    return verbalize_digit(number, alt_one=True)
+
+
+# 加减乘除
+RE_ASMD = re.compile(
+    r'((-?)((\d+)(\.\d+)?)|(\.(\d+)))([\+\-\×÷=])((-?)((\d+)(\.\d+)?)|(\.(\d+)))')
+asmd_map = {
+    '+': '加',
+    '-': '减',
+    '×': '乘',
+    '÷': '除',
+    '=': '等于'
+}
+
+
+def replace_asmd(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    result = match.group(1) + asmd_map[match.group(8)] + match.group(9)
+    return result
+
+
+# 数字表达式
+# 纯小数
+RE_DECIMAL_NUM = re.compile(r'(-?)((\d+)(\.\d+))' r'|(\.(\d+))')
+# 正整数 + 量词
+RE_POSITIVE_QUANTIFIERS = re.compile(r"(\d+)([多余几\+])?" + COM_QUANTIFIERS)
+RE_NUMBER = re.compile(r'(-?)((\d+)(\.\d+)?)' r'|(\.(\d+))')
+
+
+def replace_positive_quantifier(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    number = match.group(1)
+    match_2 = match.group(2)
+    if match_2 == "+":
+        match_2 = "多"
+    match_2: str = match_2 if match_2 else ""
+    quantifiers: str = match.group(3)
+    number: str = num2str(number)
+    result = f"{number}{match_2}{quantifiers}"
+    return result
+
+
+def replace_number(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    number = match.group(2)
+    pure_decimal = match.group(5)
+    if pure_decimal:
+        result = num2str(pure_decimal)
+    else:
+        sign: str = "负" if sign else ""
+        number: str = num2str(number)
+        result = f"{sign}{number}"
+    return result
+
+
+# 范围表达式
+# match.group(1) and match.group(8) are copy from RE_NUMBER
+
+RE_RANGE = re.compile(
+    r"""
+    (?<![\d\+\-\×÷=])      # 使用反向前瞻以确保数字范围之前没有其他数字和操作符
+    ((-?)((\d+)(\.\d+)?))  # 匹配范围起始的负数或正数（整数或小数）
+    [-~]                   # 匹配范围分隔符
+    ((-?)((\d+)(\.\d+)?))  # 匹配范围结束的负数或正数（整数或小数）
+    (?![\d\+\-\×÷=])       # 使用正向前瞻以确保数字范围之后没有其他数字和操作符
+    """, re.VERBOSE)
+
+
+def replace_range(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    first, second = match.group(1), match.group(6)
+    first = RE_NUMBER.sub(replace_number, first)
+    second = RE_NUMBER.sub(replace_number, second)
+    result = f"{first}到{second}"
+    return result
+
+
+# ~至表达式
+RE_TO_RANGE = re.compile(
+    r'((-?)((\d+)(\.\d+)?)|(\.(\d+)))(%|°C|℃|度|摄氏度|cm2|cm²|cm3|cm³|cm|db|ds|kg|km|m2|m²|m³|m3|ml|m|mm|s)[~]((-?)((\d+)(\.\d+)?)|(\.(\d+)))(%|°C|℃|度|摄氏度|cm2|cm²|cm3|cm³|cm|db|ds|kg|km|m2|m²|m³|m3|ml|m|mm|s)')
+
+def replace_to_range(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    result = match.group(0).replace('~', '至')
+    return result
+
+
+def _get_value(value_string: str, use_zero: bool=True) -> List[str]:
+    stripped = value_string.lstrip('0')
+    if len(stripped) == 0:
+        return []
+    elif len(stripped) == 1:
+        if use_zero and len(stripped) < len(value_string):
+            return [DIGITS['0'], DIGITS[stripped]]
+        else:
+            return [DIGITS[stripped]]
+    else:
+        largest_unit = next(
+            power for power in reversed(UNITS.keys()) if power < len(stripped))
+        first_part = value_string[:-largest_unit]
+        second_part = value_string[-largest_unit:]
+        return _get_value(first_part) + [UNITS[largest_unit]] + _get_value(
+            second_part)
+
+
+def verbalize_cardinal(value_string: str) -> str:
+    if not value_string:
+        return ''
+
+    # 000 -> '零' , 0 -> '零'
+    value_string = value_string.lstrip('0')
+    if len(value_string) == 0:
+        return DIGITS['0']
+
+    result_symbols = _get_value(value_string)
+    # verbalized number starting with '一十*' is abbreviated as `十*`
+    if len(result_symbols) >= 2 and result_symbols[0] == DIGITS[
+            '1'] and result_symbols[1] == UNITS[1]:
+        result_symbols = result_symbols[1:]
+    return ''.join(result_symbols)
+
+
+def verbalize_digit(value_string: str, alt_one=False) -> str:
+    result_symbols = [DIGITS[digit] for digit in value_string]
+    result = ''.join(result_symbols)
+    if alt_one:
+        result = result.replace("一", "幺")
+    return result
+
+
+def num2str(value_string: str) -> str:
+    integer_decimal = value_string.split('.')
+    if len(integer_decimal) == 1:
+        integer = integer_decimal[0]
+        decimal = ''
+    elif len(integer_decimal) == 2:
+        integer, decimal = integer_decimal
+    else:
+        raise ValueError(
+            f"The value string: '${value_string}' has more than one point in it."
+        )
+
+    result = verbalize_cardinal(integer)
+
+    decimal = decimal.rstrip('0')
+    if decimal:
+        # '.22' is verbalized as '零点二二'
+        # '3.20' is verbalized as '三点二
+        result = result if result else "零"
+        result += '点' + verbalize_digit(decimal)
+    return result
diff --git a/utils/tts/gpt_sovits/text/zh_normalization/phonecode.py b/utils/tts/gpt_sovits/text/zh_normalization/phonecode.py
new file mode 100644
index 0000000000000000000000000000000000000000..51835112603c1a8c31052285276f138b8c5a74d6
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/zh_normalization/phonecode.py
@@ -0,0 +1,63 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import re
+
+from .num import verbalize_digit
+
+# 规范化固话/手机号码
+# 手机
+# http://www.jihaoba.com/news/show/13680
+# 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198
+# 联通：130、131、132、156、155、186、185、176
+# 电信：133、153、189、180、181、177
+RE_MOBILE_PHONE = re.compile(
+    r"(?<!\d)((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})(?!\d)")
+RE_TELEPHONE = re.compile(
+    r"(?<!\d)((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})(?!\d)")
+
+# 全国统一的号码400开头
+RE_NATIONAL_UNIFORM_NUMBER = re.compile(r"(400)(-)?\d{3}(-)?\d{4}")
+
+
+def phone2str(phone_string: str, mobile=True) -> str:
+    if mobile:
+        sp_parts = phone_string.strip('+').split()
+        result = '，'.join(
+            [verbalize_digit(part, alt_one=True) for part in sp_parts])
+        return result
+    else:
+        sil_parts = phone_string.split('-')
+        result = '，'.join(
+            [verbalize_digit(part, alt_one=True) for part in sil_parts])
+        return result
+
+
+def replace_phone(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    return phone2str(match.group(0), mobile=False)
+
+
+def replace_mobile(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    return phone2str(match.group(0))
diff --git a/utils/tts/gpt_sovits/text/zh_normalization/quantifier.py b/utils/tts/gpt_sovits/text/zh_normalization/quantifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..598030e439bd2dd419900892547a550a5df96482
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/zh_normalization/quantifier.py
@@ -0,0 +1,63 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import re
+
+from .num import num2str
+
+# 温度表达式，温度会影响负号的读法
+# -3°C 零下三度
+RE_TEMPERATURE = re.compile(r'(-?)(\d+(\.\d+)?)(°C|℃|度|摄氏度)')
+measure_dict = {
+    "cm2": "平方厘米",
+    "cm²": "平方厘米",
+    "cm3": "立方厘米",
+    "cm³": "立方厘米",
+    "cm": "厘米",
+    "db": "分贝",
+    "ds": "毫秒",
+    "kg": "千克",
+    "km": "千米",
+    "m2": "平方米",
+    "m²": "平方米",
+    "m³": "立方米",
+    "m3": "立方米",
+    "ml": "毫升",
+    "m": "米",
+    "mm": "毫米",
+    "s": "秒"
+}
+
+
+def replace_temperature(match) -> str:
+    """
+    Args:
+        match (re.Match)
+    Returns:
+        str
+    """
+    sign = match.group(1)
+    temperature = match.group(2)
+    unit = match.group(3)
+    sign: str = "零下" if sign else ""
+    temperature: str = num2str(temperature)
+    unit: str = "摄氏度" if unit == "摄氏度" else "度"
+    result = f"{sign}{temperature}{unit}"
+    return result
+
+
+def replace_measure(sentence) -> str:
+    for q_notation in measure_dict:
+        if q_notation in sentence:
+            sentence = sentence.replace(q_notation, measure_dict[q_notation])
+    return sentence
diff --git a/utils/tts/gpt_sovits/text/zh_normalization/text_normlization.py b/utils/tts/gpt_sovits/text/zh_normalization/text_normlization.py
new file mode 100644
index 0000000000000000000000000000000000000000..e852fe9ba202e7e187a0d7b67472c50934ffea96
--- /dev/null
+++ b/utils/tts/gpt_sovits/text/zh_normalization/text_normlization.py
@@ -0,0 +1,165 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import re
+from typing import List
+
+from .char_convert import tranditional_to_simplified
+from .chronology import RE_DATE
+from .chronology import RE_DATE2
+from .chronology import RE_TIME
+from .chronology import RE_TIME_RANGE
+from .chronology import replace_date
+from .chronology import replace_date2
+from .chronology import replace_time
+from .constants import F2H_ASCII_LETTERS
+from .constants import F2H_DIGITS
+from .constants import F2H_SPACE
+from .num import RE_DECIMAL_NUM
+from .num import RE_DEFAULT_NUM
+from .num import RE_FRAC
+from .num import RE_INTEGER
+from .num import RE_NUMBER
+from .num import RE_PERCENTAGE
+from .num import RE_POSITIVE_QUANTIFIERS
+from .num import RE_RANGE
+from .num import RE_TO_RANGE
+from .num import RE_ASMD
+from .num import replace_default_num
+from .num import replace_frac
+from .num import replace_negative_num
+from .num import replace_number
+from .num import replace_percentage
+from .num import replace_positive_quantifier
+from .num import replace_range
+from .num import replace_to_range
+from .num import replace_asmd
+from .phonecode import RE_MOBILE_PHONE
+from .phonecode import RE_NATIONAL_UNIFORM_NUMBER
+from .phonecode import RE_TELEPHONE
+from .phonecode import replace_mobile
+from .phonecode import replace_phone
+from .quantifier import RE_TEMPERATURE
+from .quantifier import replace_measure
+from .quantifier import replace_temperature
+
+
+class TextNormalizer():
+    def __init__(self):
+        self.SENTENCE_SPLITOR = re.compile(r'([：、，；。？！,;?!][”’]?)')
+
+    def _split(self, text: str, lang="zh") -> List[str]:
+        """Split long text into sentences with sentence-splitting punctuations.
+        Args:
+            text (str): The input text.
+        Returns:
+            List[str]: Sentences.
+        """
+        # Only for pure Chinese here
+        if lang == "zh":
+            text = text.replace(" ", "")
+            # 过滤掉特殊字符
+            text = re.sub(r'[——《》【】<>{}()（）#&@“”^_|\\]', '', text)
+        text = self.SENTENCE_SPLITOR.sub(r'\1\n', text)
+        text = text.strip()
+        sentences = [sentence.strip() for sentence in re.split(r'\n+', text)]
+        return sentences
+
+    def _post_replace(self, sentence: str) -> str:
+        sentence = sentence.replace('/', '每')
+        # sentence = sentence.replace('~', '至')
+        # sentence = sentence.replace('～', '至')
+        sentence = sentence.replace('①', '一')
+        sentence = sentence.replace('②', '二')
+        sentence = sentence.replace('③', '三')
+        sentence = sentence.replace('④', '四')
+        sentence = sentence.replace('⑤', '五')
+        sentence = sentence.replace('⑥', '六')
+        sentence = sentence.replace('⑦', '七')
+        sentence = sentence.replace('⑧', '八')
+        sentence = sentence.replace('⑨', '九')
+        sentence = sentence.replace('⑩', '十')
+        sentence = sentence.replace('α', '阿尔法')
+        sentence = sentence.replace('β', '贝塔')
+        sentence = sentence.replace('γ', '伽玛').replace('Γ', '伽玛')
+        sentence = sentence.replace('δ', '德尔塔').replace('Δ', '德尔塔')
+        sentence = sentence.replace('ε', '艾普西龙')
+        sentence = sentence.replace('ζ', '捷塔')
+        sentence = sentence.replace('η', '依塔')
+        sentence = sentence.replace('θ', '西塔').replace('Θ', '西塔')
+        sentence = sentence.replace('ι', '艾欧塔')
+        sentence = sentence.replace('κ', '喀帕')
+        sentence = sentence.replace('λ', '拉姆达').replace('Λ', '拉姆达')
+        sentence = sentence.replace('μ', '缪')
+        sentence = sentence.replace('ν', '拗')
+        sentence = sentence.replace('ξ', '克西').replace('Ξ', '克西')
+        sentence = sentence.replace('ο', '欧米克伦')
+        sentence = sentence.replace('π', '派').replace('Π', '派')
+        sentence = sentence.replace('ρ', '肉')
+        sentence = sentence.replace('ς', '西格玛').replace('Σ', '西格玛').replace(
+            'σ', '西格玛')
+        sentence = sentence.replace('τ', '套')
+        sentence = sentence.replace('υ', '宇普西龙')
+        sentence = sentence.replace('φ', '服艾').replace('Φ', '服艾')
+        sentence = sentence.replace('χ', '器')
+        sentence = sentence.replace('ψ', '普赛').replace('Ψ', '普赛')
+        sentence = sentence.replace('ω', '欧米伽').replace('Ω', '欧米伽')
+        # re filter special characters, have one more character "-" than line 68
+        sentence = re.sub(r'[-——《》【】<=>{}()（）#&@“”^_|\\]', '', sentence)
+        return sentence
+
+    def normalize_sentence(self, sentence: str) -> str:
+        # basic character conversions
+        sentence = tranditional_to_simplified(sentence)
+        sentence = sentence.translate(F2H_ASCII_LETTERS).translate(
+            F2H_DIGITS).translate(F2H_SPACE)
+
+        # number related NSW verbalization
+        sentence = RE_DATE.sub(replace_date, sentence)
+        sentence = RE_DATE2.sub(replace_date2, sentence)
+
+        # range first
+        sentence = RE_TIME_RANGE.sub(replace_time, sentence)
+        sentence = RE_TIME.sub(replace_time, sentence)
+
+        # 处理~波浪号作为至的替换
+        sentence = RE_TO_RANGE.sub(replace_to_range, sentence)
+        sentence = RE_TEMPERATURE.sub(replace_temperature, sentence)
+        sentence = replace_measure(sentence)
+        sentence = RE_FRAC.sub(replace_frac, sentence)
+        sentence = RE_PERCENTAGE.sub(replace_percentage, sentence)
+        sentence = RE_MOBILE_PHONE.sub(replace_mobile, sentence)
+
+        sentence = RE_TELEPHONE.sub(replace_phone, sentence)
+        sentence = RE_NATIONAL_UNIFORM_NUMBER.sub(replace_phone, sentence)
+
+        sentence = RE_RANGE.sub(replace_range, sentence)
+
+        # 处理加减乘除
+        while RE_ASMD.search(sentence):
+            sentence = RE_ASMD.sub(replace_asmd, sentence)
+
+        sentence = RE_INTEGER.sub(replace_negative_num, sentence)
+        sentence = RE_DECIMAL_NUM.sub(replace_number, sentence)
+        sentence = RE_POSITIVE_QUANTIFIERS.sub(replace_positive_quantifier,
+                                               sentence)
+        sentence = RE_DEFAULT_NUM.sub(replace_default_num, sentence)
+        sentence = RE_NUMBER.sub(replace_number, sentence)
+        sentence = self._post_replace(sentence)
+
+        return sentence
+
+    def normalize(self, text: str) -> List[str]:
+        sentences = self._split(text)
+        sentences = [self.normalize_sentence(sent) for sent in sentences]
+        return sentences
diff --git a/utils/tts/gpt_sovits/utils.py b/utils/tts/gpt_sovits/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..41ea86ee5cb7abcfd773541305c813d2c28c001f
--- /dev/null
+++ b/utils/tts/gpt_sovits/utils.py
@@ -0,0 +1,24 @@
+import os
+import traceback
+
+import ffmpeg
+import numpy as np
+
+
+def load_audio(file, sr):
+    try:
+        # https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
+        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
+        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
+        if os.path.exists(file) == False:
+            raise RuntimeError("You input a wrong audio path that does not exists, please fix it!")
+        out, _ = (
+            ffmpeg.input(file, threads=0)
+            .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
+            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
+        )
+    except Exception as e:
+        traceback.print_exc()
+        raise RuntimeError(f"Failed to load audio: {e}")
+
+    return np.frombuffer(out, np.float32).flatten()
diff --git a/utils/tts/sambert_hifigan/tts_sambert_hifigan.py b/utils/tts/sambert_hifigan/tts_sambert_hifigan.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed4e80b6733c42e152a1768d647f82071c4901ce
--- /dev/null
+++ b/utils/tts/sambert_hifigan/tts_sambert_hifigan.py
@@ -0,0 +1,25 @@
+import streamlit as st
+from modelscope.outputs import OutputKeys
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+# pip install kantts -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
+# pip install pytorch_wavelets tensorboardX scipy==1.12.0
+
+
+@st.cache_resource
+def get_tts_model():
+    model_id = "damo/speech_sambert-hifigan_tts_zhisha_zh-cn_16k"
+    sambert_hifigan_tts = pipeline(task=Tasks.text_to_speech, model=model_id)
+    return sambert_hifigan_tts
+
+
+def gen_tts_wav(sambert_hifigan_tts, text, wav_path):
+    # wav_path = 'output.wav'
+    # text = '哈喽哈喽，家人们好啊！今天呀，咱们这儿可是有大大的福利等着大家哦你们猜猜看是什么呢？没错啦，就是这款超级棒哒【声波电动牙刷】啦！哎呀，我知道你们肯定都用过电动牙刷的，但是这款真的是太不一样了哦让我来给你们一一揭秘吧首先呢，它的【高效清洁】功能绝对是业界领先的哦，无论是顽固牙渍还是口腔死角，都能轻松搞定，让你们的牙齿每天都亮晶晶滴而且啊，它还有【减少手动压力】的设计，再也不用担心手酸手疼啦，轻轻松松就能拥有健康洁白的好笑容哦对了，还有【定时提醒】这个功能，再也不用担心刷牙时间不够啦，让你随时随地保持口腔卫生哦最厉害的是，它还有【智能模式调节】和【无线充电】的功能呢，简直是科技感爆棚哦而且，它的【噪音低】设计，就算是深夜刷牙也不会打扰到家人啦家人们，这样的电动牙刷是不是超级心动呢？快来把它带回家吧，让你的每一天都充满活力和好心情哦'
+    print(f"gerning tts for {wav_path} ....")
+    output = sambert_hifigan_tts(input=text)
+    wav = output[OutputKeys.OUTPUT_WAV]
+    with open(wav_path, "wb") as f:
+        f.write(wav)
+    print(f"gen tts for {wav_path} done!....")
diff --git a/utils/tts/tts_worker.py b/utils/tts/tts_worker.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8dafdebef2f9800ffdd9655e801c856c4f0cff6
--- /dev/null
+++ b/utils/tts/tts_worker.py
@@ -0,0 +1,54 @@
+from datetime import datetime
+from pathlib import Path
+
+import streamlit as st
+
+# from utils.tts.sambert_hifigan.tts_sambert_hifigan import gen_tts_wav
+from utils.model_loader import TTS_HANDLER
+from utils.tts.gpt_sovits.inference_gpt_sovits import gen_tts_wav
+from utils.web_configs import WEB_CONFIGS
+
+
+def show_audio(tts_path):
+
+    if tts_path is None:
+        return
+
+    with open(tts_path, "rb") as f_wav:
+        audio_bytes = f_wav.read()
+    st.audio(audio_bytes, format="audio/wav")
+
+
+def gen_tts_in_spinner(cur_response):
+    tts_save_path = None
+    if TTS_HANDLER is not None and st.session_state.gen_tts_checkbox:
+        with st.spinner("正在生成语音，请稍等... 如果觉得生成时间太久，可以将侧边栏的【生成语音】按钮取消选中，下次则不会生成"):
+            save_tag = datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + ".wav"
+            tts_save_path = str(Path(WEB_CONFIGS.TTS_WAV_GEN_PATH).joinpath(save_tag).absolute())
+            # gen_tts_wav(st.session_state.tts_handler, cur_response, tts_save_path)
+
+            # inp_ref = r"/root/hingwen_camp/utils/tts/gpt_sovits/weights/ref_wav/【开心】处理完之前的事情，这几天甚至都有空闲来车上转转了。.wav"
+            text_language = "中英混合"
+            gen_tts_wav(
+                cur_response,
+                text_language,
+                TTS_HANDLER.bert_tokenizer,
+                TTS_HANDLER.bert_model,
+                TTS_HANDLER.ssl_model,
+                TTS_HANDLER.vq_model,
+                TTS_HANDLER.hps,
+                TTS_HANDLER.max_sec,
+                TTS_HANDLER.t2s_model,
+                TTS_HANDLER.inp_ref,
+                TTS_HANDLER.prompt_text,
+                TTS_HANDLER.prompt,
+                TTS_HANDLER.refer,
+                TTS_HANDLER.bert1,
+                TTS_HANDLER.phones1,
+                TTS_HANDLER.zero_wav,
+                tts_save_path,
+            )
+
+            show_audio(tts_save_path)
+            st.toast("生成语音成功!")
+    return tts_save_path
diff --git a/utils/web_configs.py b/utils/web_configs.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8232b23f6ae7cf8d846b15f5d2e0a0db3e6753b
--- /dev/null
+++ b/utils/web_configs.py
@@ -0,0 +1,100 @@
+from dataclasses import dataclass
+import os
+
+
+@dataclass
+class WebConfigs:
+    """
+    项目所有的配置
+    """
+
+    # ==================================================================
+    #                             LLM 模型配置
+    # ==================================================================
+    if os.getenv("USING_4BIT") == "true":
+        LLM_MODEL_NAME: str = "HinGwenWoong/streamer-sales-lelemiao-7b-4bit"
+    else:
+        LLM_MODEL_NAME: str = "HinGwenWoong/streamer-sales-lelemiao-7b"
+
+    SALES_NAME: str = "智能医导小助手"  # 启动的角色名
+
+    LLM_MODEL_DIR: str = r"./weights/llm_weights/"
+
+    # ==================================================================
+    #                               组件配置
+    # ==================================================================
+    ENABLE_RAG: bool = True  # True 启用 RAG 检索增强，False 不启用
+    ENABLE_TTS: bool = True  # True 启动 tts，False 不启用
+    ENABLE_DIGITAL_HUMAN: bool = True  # True 启动 数字人，False 不启用
+    ENABLE_AGENT: bool = os.environ.get("ENABLE_AGENT", "true") == "true"  # True 启动 Agent，False 不启用
+    ENABLE_ASR: bool = os.environ.get("ENABLE_ASR", "true") == "true"  # True 启动 语音转文字，False 不启用
+
+    DISABLE_UPLOAD: bool = os.getenv("DISABLE_UPLOAD") == "true"
+
+    CACHE_MAX_ENTRY_COUNT: float = float(
+        os.environ.get("KV_CACHE", 0.1)
+    )  # KV cache 占比，如果部署出现 OOM 降低这个配置，反之可以加大
+
+    # ==================================================================
+    #                               页面配置
+    # ==================================================================
+    PRODUCT_IMAGE_HEIGHT: int = 400  # 商品图片高度
+    EACH_CARD_OFFSET: int = 100  # 每个商品卡片比图片高度多出的距离
+    EACH_ROW_COL: int = 2  # 商品页显示多少列
+
+    # 定义用户和机器人头像路径
+    USER_AVATOR: str = "./assets/user.png"
+    ROBOT_AVATOR: str = "./assets/logo.png"
+
+    # ==================================================================
+    #                               商品配置
+    # ==================================================================
+    PRODUCT_INSTRUCTION_DIR: str = r"./product_info/instructions"
+    PRODUCT_IMAGES_DIR: str = r"./product_info/images"
+
+    PRODUCT_INFO_YAML_PATH: str = r"./product_info/product_info.yaml"
+    PRODUCT_INFO_YAML_BACKUP_PATH: str = PRODUCT_INFO_YAML_PATH + ".bk"
+
+    # ==================================================================
+    #                             配置文件路径
+    # ==================================================================
+    CONVERSATION_CFG_YAML_PATH: str = r"./configs/conversation_cfg.yaml"
+
+    # ==================================================================
+    #                               RAG 配置
+    # ==================================================================
+    RAG_CONFIG_PATH: str = r"./configs/rag_config.yaml"
+    RAG_VECTOR_DB_DIR: str = r"./work_dirs/instruction_db"
+    PRODUCT_INSTRUCTION_DIR_GEN_DB_TMP: str = r"./work_dirs/instructions_gen_db_tmp"
+    RAG_MODEL_DIR: str = r"./weights/rag_weights/"
+
+    # ==================================================================
+    #                               TTS 配置
+    # ==================================================================
+    TTS_WAV_GEN_PATH: str = r"./work_dirs/tts_wavs"
+    TTS_MODEL_DIR: str = r"./weights/gpt_sovits_weights/"
+
+    # ==================================================================
+    #                             数字人 配置
+    # ==================================================================
+    DIGITAL_HUMAN_GEN_PATH: str = r"./work_dirs/digital_human"
+    DIGITAL_HUMAN_MODEL_DIR: str = r"./weights/digital_human_weights/"
+    DIGITAL_HUMAN_BBOX_SHIFT: int = 0
+    DIGITAL_HUMAN_VIDEO_PATH: str = r"./doc/digital_human/digital_human_video.mp4"
+    DIGITAL_HUMAN_FPS: str = 25
+
+    # ==================================================================
+    #                             Agent 配置
+    # ==================================================================
+    AGENT_WEATHER_API_KEY: str | None = os.environ.get("WEATHER_API_KEY", None)  # 天气 API Key
+    AGENT_DELIVERY_TIME_API_KEY: str | None = os.environ.get("DELIVERY_TIME_API_KEY", None)  # 快递查询 API Key
+
+    # ==================================================================
+    #                              ASR 配置
+    # ==================================================================
+    ASR_WAV_SAVE_PATH: str = r"./work_dirs/asr_wavs"
+    ASR_MODEL_DIR: str = r"./weights/asr_weights/"
+
+
+# 实例化
+WEB_CONFIGS = WebConfigs()