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| import argparse | |
| import os | |
| from pathlib import Path | |
| import logging | |
| import re_matching | |
| logging.getLogger("numba").setLevel(logging.WARNING) | |
| logging.getLogger("markdown_it").setLevel(logging.WARNING) | |
| logging.getLogger("urllib3").setLevel(logging.WARNING) | |
| logging.getLogger("matplotlib").setLevel(logging.WARNING) | |
| logging.basicConfig( | |
| level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s" | |
| ) | |
| logger = logging.getLogger(__name__) | |
| import librosa | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from torch.utils.data import Dataset | |
| from torch.utils.data import DataLoader, Dataset | |
| from tqdm import tqdm | |
| from transformers import Wav2Vec2Processor | |
| from transformers.models.wav2vec2.modeling_wav2vec2 import ( | |
| Wav2Vec2Model, | |
| Wav2Vec2PreTrainedModel, | |
| ) | |
| import gradio as gr | |
| import utils | |
| from config import config | |
| import torch | |
| import commons | |
| from text import cleaned_text_to_sequence, get_bert | |
| from emo_gen import process_func, EmotionModel, Wav2Vec2Processor, Wav2Vec2Model, Wav2Vec2PreTrainedModel, RegressionHead | |
| from text.cleaner import clean_text | |
| import utils | |
| from models import SynthesizerTrn | |
| from text.symbols import symbols | |
| import sys | |
| net_g = None | |
| device = ( | |
| "cuda:0" | |
| if torch.cuda.is_available() | |
| else ( | |
| "mps" | |
| if sys.platform == "darwin" and torch.backends.mps.is_available() | |
| else "cpu" | |
| ) | |
| ) | |
| BandList = { | |
| #"PoppinParty":["香澄","有咲","たえ","りみ","沙綾"], | |
| "Afterglow":["蘭","モカ","ひまり","巴","つぐみ"], | |
| "HelloHappyWorld":["こころ","美咲","薫","花音","はぐみ"], | |
| "PastelPalettes":["彩","日菜","千聖","イヴ","麻弥"], | |
| "Roselia":["友希那","紗夜","リサ","燐子","あこ"], | |
| "RaiseASuilen":["レイヤ","ロック","ますき","チュチュ","パレオ"], | |
| "Morfonica":["ましろ","瑠唯","つくし","七深","透子"], | |
| "MyGo":["燈","愛音","そよ","立希","楽奈"], | |
| "AveMujica":["祥子","睦","海鈴","にゃむ","初華"], | |
| "圣翔音乐学园":["華戀","光","香子","雙葉","真晝","純那","克洛迪娜","真矢","奈奈"], | |
| "凛明馆女子学校":["珠緒","壘","文","悠悠子","一愛"], | |
| "弗隆提亚艺术学校":["艾露","艾露露","菈樂菲","司","靜羽"], | |
| "西克菲尔特音乐学院":["晶","未知留","八千代","栞","美帆"] | |
| } | |
| def get_net_g(model_path: str, version: str, device: str, hps): | |
| net_g = SynthesizerTrn( | |
| len(symbols), | |
| hps.data.filter_length // 2 + 1, | |
| hps.train.segment_size // hps.data.hop_length, | |
| n_speakers=hps.data.n_speakers, | |
| **hps.model, | |
| ).to(device) | |
| _ = net_g.eval() | |
| _ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True) | |
| return net_g | |
| def get_text(text, language_str, hps, device): | |
| norm_text, phone, tone, word2ph = clean_text(text, language_str) | |
| phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) | |
| #print(text) | |
| if hps.data.add_blank: | |
| phone = commons.intersperse(phone, 0) | |
| tone = commons.intersperse(tone, 0) | |
| language = commons.intersperse(language, 0) | |
| for i in range(len(word2ph)): | |
| word2ph[i] = word2ph[i] * 2 | |
| word2ph[0] += 1 | |
| bert_ori = get_bert(norm_text, word2ph, language_str, device) | |
| del word2ph | |
| assert bert_ori.shape[-1] == len(phone), phone | |
| if language_str == "ZH": | |
| bert = bert_ori | |
| ja_bert = torch.zeros(1024, len(phone)) | |
| en_bert = torch.zeros(1024, len(phone)) | |
| elif language_str == "JP": | |
| bert = torch.zeros(1024, len(phone)) | |
| ja_bert = bert_ori | |
| en_bert = torch.zeros(1024, len(phone)) | |
| elif language_str == "EN": | |
| bert = torch.zeros(1024, len(phone)) | |
| ja_bert = torch.zeros(1024, len(phone)) | |
| en_bert = bert_ori | |
| else: | |
| raise ValueError("language_str should be ZH, JP or EN") | |
| assert bert.shape[-1] == len( | |
| phone | |
| ), f"Bert seq len {bert.shape[-1]} != {len(phone)}" | |
| phone = torch.LongTensor(phone) | |
| tone = torch.LongTensor(tone) | |
| language = torch.LongTensor(language) | |
| return bert, ja_bert, en_bert, phone, tone, language | |
| def get_emo_(reference_audio, emotion): | |
| if (emotion == 10 and reference_audio): | |
| emo = torch.from_numpy(get_emo(reference_audio)) | |
| else: | |
| emo = torch.Tensor([emotion]) | |
| return emo | |
| def get_emo(path): | |
| wav, sr = librosa.load(path, 16000) | |
| device = config.bert_gen_config.device | |
| return process_func( | |
| np.expand_dims(wav, 0).astype(np.float64), | |
| sr, | |
| emotional_model, | |
| emotional_processor, | |
| device, | |
| embeddings=True, | |
| ).squeeze(0) | |
| def infer( | |
| text, | |
| sdp_ratio, | |
| noise_scale, | |
| noise_scale_w, | |
| length_scale, | |
| sid, | |
| reference_audio=None, | |
| emotion=None, | |
| ): | |
| language= 'JP' if is_japanese(text) else 'ZH' | |
| bert, ja_bert, en_bert, phones, tones, lang_ids = get_text( | |
| text, language, hps, device | |
| ) | |
| emo = get_emo_(reference_audio, emotion) | |
| with torch.no_grad(): | |
| x_tst = phones.to(device).unsqueeze(0) | |
| tones = tones.to(device).unsqueeze(0) | |
| lang_ids = lang_ids.to(device).unsqueeze(0) | |
| bert = bert.to(device).unsqueeze(0) | |
| ja_bert = ja_bert.to(device).unsqueeze(0) | |
| en_bert = en_bert.to(device).unsqueeze(0) | |
| x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) | |
| emo = emo.to(device).unsqueeze(0) | |
| print(emo) | |
| del phones | |
| speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) | |
| audio = ( | |
| net_g.infer( | |
| x_tst, | |
| x_tst_lengths, | |
| speakers, | |
| tones, | |
| lang_ids, | |
| bert, | |
| ja_bert, | |
| en_bert, | |
| emo, | |
| sdp_ratio=sdp_ratio, | |
| noise_scale=noise_scale, | |
| noise_scale_w=noise_scale_w, | |
| length_scale=length_scale, | |
| )[0][0, 0] | |
| .data.cpu() | |
| .float() | |
| .numpy() | |
| ) | |
| del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers, ja_bert, en_bert, emo | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| return (hps.data.sampling_rate,gr.processing_utils.convert_to_16_bit_wav(audio)) | |
| def is_japanese(string): | |
| for ch in string: | |
| if ord(ch) > 0x3040 and ord(ch) < 0x30FF: | |
| return True | |
| return False | |
| def loadmodel(model): | |
| _ = net_g.eval() | |
| _ = utils.load_checkpoint(model, net_g, None, skip_optimizer=True) | |
| return "success" | |
| if __name__ == "__main__": | |
| emotional_model_name = "./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim" | |
| REPO_ID = "audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim" | |
| emotional_processor = Wav2Vec2Processor.from_pretrained(emotional_model_name) | |
| emotional_model = EmotionModel.from_pretrained(emotional_model_name).to(device) | |
| languages = [ "Auto", "ZH", "JP"] | |
| modelPaths = [] | |
| for dirpath, dirnames, filenames in os.walk("Data/Bushiroad/models/"): | |
| for filename in filenames: | |
| modelPaths.append(os.path.join(dirpath, filename)) | |
| hps = utils.get_hparams_from_file('Data/Bushiroad/configs/config.json') | |
| net_g = get_net_g( | |
| model_path=modelPaths[-1], version="2.1", device=device, hps=hps | |
| ) | |
| speaker_ids = hps.data.spk2id | |
| speakers = list(speaker_ids.keys()) | |
| with gr.Blocks() as app: | |
| for band in BandList: | |
| with gr.TabItem(band): | |
| for name in BandList[band]: | |
| with gr.TabItem(name): | |
| classifiedPaths = [] | |
| for dirpath, dirnames, filenames in os.walk("Data/Bushiroad/classifedSample/"+name): | |
| for filename in filenames: | |
| classifiedPaths.append(os.path.join(dirpath, filename)) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| gr.Markdown( | |
| '<div align="center">' | |
| f'<img style="width:auto;height:400px;" src="https://mahiruoshi-bangdream-bert-vits2.hf.space/file/image/{name}.png">' | |
| '</div>' | |
| ) | |
| length_scale = gr.Slider( | |
| minimum=0.1, maximum=2, value=1, step=0.01, label="语速调节" | |
| ) | |
| emotion = gr.Slider( | |
| minimum=-10, maximum=10, value=0, step=0.1, label="Emotion参数(调至10开启音频参考,如不启动则设为0)" | |
| ) | |
| with gr.Accordion(label="参数设定", open=False): | |
| sdp_ratio = gr.Slider( | |
| minimum=0, maximum=1, value=0.2, step=0.01, label="SDP/DP混合比" | |
| ) | |
| noise_scale = gr.Slider( | |
| minimum=0.1, maximum=2, value=0.6, step=0.01, label="感情调节" | |
| ) | |
| noise_scale_w = gr.Slider( | |
| minimum=0.1, maximum=2, value=0.8, step=0.01, label="音素长度" | |
| ) | |
| speaker = gr.Dropdown( | |
| choices=speakers, value=name, label="说话人" | |
| ) | |
| with gr.Accordion(label="切换模型", open=False): | |
| modelstrs = gr.Dropdown(label = "模型", choices = modelPaths, value = modelPaths[0], type = "value") | |
| btnMod = gr.Button("载入模型") | |
| statusa = gr.TextArea() | |
| btnMod.click(loadmodel, inputs=[modelstrs], outputs = [statusa]) | |
| with gr.Column(): | |
| text = gr.TextArea( | |
| label="输入纯日语或者中文", | |
| placeholder="输入纯日语或者中文", | |
| value="为什么要演奏春日影!", | |
| ) | |
| try: | |
| reference_audio = gr.Dropdown(label = "情感参考", choices = classifiedPaths, value = classifiedPaths[0], type = "value") | |
| except: | |
| reference_audio = gr.Audio(label="情感参考音频）", type="filepath") | |
| btn = gr.Button("点击生成", variant="primary") | |
| audio_output = gr.Audio(label="Output Audio") | |
| ''' | |
| btntran = gr.Button("快速中翻日") | |
| translateResult = gr.TextArea("从这复制翻译后的文本") | |
| btntran.click(translate, inputs=[text], outputs = [translateResult]) | |
| ''' | |
| btn.click( | |
| infer, | |
| inputs=[ | |
| text, | |
| sdp_ratio, | |
| noise_scale, | |
| noise_scale_w, | |
| length_scale, | |
| speaker, | |
| reference_audio, | |
| emotion, | |
| ], | |
| outputs=[audio_output], | |
| ) | |
| print("推理页面已开启!") | |
| app.launch(share=True) |