# flake8: noqa: E402 import re import sys, os 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 torch import argparse import commons import utils from models import SynthesizerTrn from text.symbols import symbols from text import cleaned_text_to_sequence, get_bert from text.cleaner import clean_text import gradio as gr import webbrowser import numpy as np net_g = None if sys.platform == "darwin" and torch.backends.mps.is_available(): device = "mps" os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" else: device = "cuda" def get_text(text, language_str, hps): norm_text, phone, tone, word2ph = clean_text(text, language_str) phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) 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 = get_bert(norm_text, word2ph, language_str, device) del word2ph assert bert.shape[-1] == len(phone), phone if language_str == "ZH": bert = bert ja_bert = torch.zeros(768, len(phone)) elif language_str == "JP": ja_bert = bert bert = torch.zeros(1024, len(phone)) else: bert = torch.zeros(1024, len(phone)) ja_bert = torch.zeros(768, len(phone)) 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, phone, tone, language def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language): global net_g bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps) 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) x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) 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, 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 torch.cuda.empty_cache() return audio def generate_audio(slices, sdp_ratio, noise_scale, noise_scale_w, length_scale, speaker, language): audio_list = [] silence = np.zeros(hps.data.sampling_rate // 2) with torch.no_grad(): for piece in slices: audio = infer( piece, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker, language=language, ) audio_list.append(audio) audio_list.append(silence) # 将静音添加到列表中 return audio_list def tts_fn(text: str, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language): audio_list = [] if language == "mix": bool_valid, str_valid = re_matching.validate_text(text) if not bool_valid: return str_valid, (hps.data.sampling_rate, np.concatenate([np.zeros(hps.data.sampling_rate // 2)])) result = re_matching.text_matching(text) for one in result: _speaker = one.pop() for lang, content in one: audio_list.extend( generate_audio(content.split("|"), sdp_ratio, noise_scale, noise_scale_w, length_scale, _speaker+'_'+lang.lower(), lang) ) else: audio_list.extend( generate_audio(text.split("|"), sdp_ratio, noise_scale, noise_scale_w, length_scale, speaker, language) ) audio_concat = np.concatenate(audio_list) return "Success", (hps.data.sampling_rate, audio_concat) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "-m", "--model", default="./logs/maolei/G_4800.pth", help="path of your model" ) parser.add_argument( "-c", "--config", default="./configs/config.json", help="path of your config file", ) parser.add_argument( "--share", default=False, help="make link public", action="store_true" ) parser.add_argument( "-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log" ) args = parser.parse_args() if args.debug: logger.info("Enable DEBUG-LEVEL log") logging.basicConfig(level=logging.DEBUG) hps = utils.get_hparams_from_file(args.config) device = ( "cuda:0" if torch.cuda.is_available() else ( "mps" if sys.platform == "darwin" and torch.backends.mps.is_available() else "cpu" ) ) 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(args.model, net_g, None, skip_optimizer=True) speaker_ids = hps.data.spk2id speakers = list(speaker_ids.keys()) languages = ["ZH", "JP", "mix"] with gr.Blocks() as app: with gr.Row(): with gr.Column(): gr.Markdown(value=""" bert-vits-v1.1.1整合包作者：@spicysama\n 整合包b站链接：https://www.bilibili.com/video/BV1hu4y1W7dW\n 模型作者：RUSHB-喵咪\n 声音归属：@猫雷NyaRu_Official\n Bert-VITS2项目：https://github.com/Stardust-minus/Bert-VITS2\n 猫雷的B站账号:https://space.bilibili.com/697091119 发布二创作品请标注本项目作者及链接、作品使用Bert-VITS2 AI生成！\n """) text = gr.TextArea( label="输入文本内容", placeholder=""" 如果你选择语言为\'mix\'，必须按照格式输入，否则报错: 格式举例(zh是中文，jp是日语，不区分大小写；说话人举例:gongzi): [说话人1]你好，こんにちは！ こんにちは，世界。 [说话人2]你好吗？元気ですか？ [说话人3]谢谢。どういたしまして。 ... 另外，所有的语言选项都可以用'|'分割长段实现分句生成。 """ ) speaker = gr.Dropdown( choices=speakers, value=speakers[0], label="选择说话人" ) sdp_ratio = gr.Slider( minimum=0, maximum=1, value=0.2, step=0.1, label="SDP/DP混合比" ) noise_scale = gr.Slider( minimum=0.1, maximum=2, value=0.2, step=0.1, label="感情" ) noise_scale_w = gr.Slider( minimum=0.1, maximum=2, value=0.9, step=0.1, label="音素长度" ) length_scale = gr.Slider( minimum=0.1, maximum=2, value=0.8, step=0.1, label="语速" ) language = gr.Dropdown( choices=languages, value=languages[0], label="选择语言(该模型mix混合效果不好，先别用)" ) btn = gr.Button("生成音频！", variant="primary") with gr.Column(): text_output = gr.Textbox(label="状态信息") audio_output = gr.Audio(label="输出音频") btn.click( tts_fn, inputs=[ text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language, ], outputs=[text_output, audio_output], ) app.launch(show_error=True)