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| import gradio as gr | |
| # import matplotlib.pyplot as plt | |
| import logging | |
| # logger = logging.getLogger(__name__) | |
| import os | |
| import json | |
| import math | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from torch.utils.data import DataLoader | |
| import commons | |
| import utils | |
| from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate | |
| from models import SynthesizerTrn | |
| from text.symbols import symbols | |
| from text import text_to_sequence | |
| import time | |
| def get_text(text, hps): | |
| # text_norm = requests.post("http://121.5.171.42:39001/texttosequence?text="+text).json()["text_norm"] | |
| text_norm = text_to_sequence(text, hps.data.text_cleaners) | |
| # print(hps.data.text_cleaners) | |
| # print(text_norm) | |
| if hps.data.add_blank: | |
| text_norm = commons.intersperse(text_norm, 0) | |
| text_norm = torch.LongTensor(text_norm) | |
| return text_norm | |
| def load_model(config_path, pth_path): | |
| global dev, hps_ms, net_g | |
| dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| hps_ms = utils.get_hparams_from_file(config_path) | |
| net_g = SynthesizerTrn( | |
| len(symbols), | |
| hps_ms.data.filter_length // 2 + 1, | |
| hps_ms.train.segment_size // hps_ms.data.hop_length, | |
| **hps_ms.model).to(dev) | |
| _ = net_g.eval() | |
| _ = utils.load_checkpoint(pth_path, net_g) | |
| return f"{pth_path}加载成功!" | |
| def infer(c_id, text): | |
| stn_tst = get_text(text, hps_ms) | |
| with torch.no_grad(): | |
| x_tst = stn_tst.to(dev).unsqueeze(0) | |
| x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(dev) | |
| sid = torch.LongTensor([c_id]).to(dev) | |
| audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy() | |
| return audio | |
| pth_path = "model/G_70000.pth" | |
| config_path = "configs/config.json" | |
| character_dict = { | |
| "十香": 1, | |
| "折纸": 2, | |
| "狂三": 3, | |
| "四糸乃": 4, | |
| "琴里": 5, | |
| "夕弦": 6, | |
| "耶俱矢": 7, | |
| "美九": 8, | |
| "凛祢": 9, | |
| "凛绪": 10, | |
| "鞠亚": 11, | |
| "鞠奈": 12, | |
| "真那": 13, | |
| } | |
| app = gr.Blocks() | |
| with app: | |
| gr.HTML(""" | |
| <div | |
| style="width: 100%;padding-top:116px;background-image: url('https://huggingface.co/spaces/tumuyan/vits-miki/resolve/main/bg.webp');;background-size:cover"> | |
| <div> | |
| <div> | |
| <h4 class="h-sign" style="font-size: 12px;"> | |
| 这是一个使用<a href="https://github.com/thesupersonic16/DALTools" target="_blank">thesupersonic16/DALTools</a>提供的解包音频作为数据集， | |
| 使用<a href="https://github.com/jaywalnut310/vits" target="_blank">VITS</a>技术训练的语音合成demo。 | |
| </h4> | |
| </div> | |
| </div> | |
| </div> | |
| """) | |
| tmp = gr.Markdown("") | |
| with gr.Tabs(): | |
| with gr.TabItem("Basic"): | |
| with gr.Raw(): | |
| model_submit = gr.Button("加载/重载模型", variant="primary") | |
| output_1 = gr.Markdown("") | |
| with gr.Raw(): | |
| tts_input1 = gr.TextArea( | |
| label="请输入文本(仅支持日语)", value="你好,世界!") | |
| tts_input2 = gr.Dropdown(choices=[character_dict.keys], type="index",label="选择角色", optional=False) | |
| tts_submit = gr.Button("用文本合成", variant="primary") | |
| tts_output2 = gr.Audio(label="Output") | |
| model_submit.click(load_model, [config_path, pth_path], [output_1]) | |
| tts_submit.click(infer, [tts_input2+1, tts_input1], [tts_output2]) | |
| gr.HTML(""" | |
| <div style="text-align:center"> | |
| 仅供学习交流，不可用于商业或非法用途 | |
| <br/> | |
| 使用本项目模型直接或间接生成的音频，必须声明由AI技术或VITS技术合成 | |
| </div> | |
| """) | |
| app.launch() |