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| # flake8: noqa: E402 | |
| import sys, os | |
| import logging | |
| 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 tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language): | |
| slices = text.split("|") | |
| audio_list = [] | |
| with torch.no_grad(): | |
| for slice in slices: | |
| audio = infer(slice, 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) | |
| silence = np.zeros(hps.data.sampling_rate) # 生成1秒的静音 | |
| audio_list.append(silence) # 将静音添加到列表中 | |
| 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/illi/G_25000.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"] | |
| with gr.Blocks() as app: | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown(value=""" | |
| 🤖 【AI以里illi】在线语音合成 Bert-Vits2 🤖\n | |
| 📝 作者：Rayzggz 📰博客 https://roi.moe 📺B站 https://space.bilibili.com/10501326 📝\n | |
| 🎤 声音来源：以里illi https://space.bilibili.com/3035038 🎤\n | |
| 🔗 Bert-VITS2：https://github.com/fishaudio/Bert-VITS2 🔗\n | |
| ✅ 使用本模型请遵守中华人民共和国和美利坚合众国法律 ✅\n | |
| 🏷️ 使用基于本模型的所有生成内容均需标注「使用Bert-VITS2 AI生成」、「本项目地址」、「作者名称」和「声音来源」 🏷️\n | |
| """) | |
| text = gr.TextArea( | |
| label="Text", | |
| placeholder="Input Text Here", | |
| value="野生的门卫室魔法师出现了！", | |
| ) | |
| speaker = gr.Dropdown( | |
| choices=speakers, value=speakers[0], label="Speaker" | |
| ) | |
| sdp_ratio = gr.Slider( | |
| minimum=0, maximum=1, value=0.2, step=0.1, label="SDP Ratio" | |
| ) | |
| noise_scale = gr.Slider( | |
| minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise Scale" | |
| ) | |
| noise_scale_w = gr.Slider( | |
| minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise Scale W" | |
| ) | |
| length_scale = gr.Slider( | |
| minimum=0.1, maximum=2, value=1, step=0.1, label="Length Scale" | |
| ) | |
| language = gr.Dropdown( | |
| choices=languages, value=languages[0], label="Language" | |
| ) | |
| btn = gr.Button("Generate 生成!", variant="primary") | |
| with gr.Column(): | |
| text_output = gr.Textbox(label="Message") | |
| audio_output = gr.Audio(label="Output Audio") | |
| gr.Markdown(value=""" | |
| 👏 鸣谢： 👏\n | |
| 👤 团子是咸鱼 https://space.bilibili.com/10685437 👤\n | |
| 👤 领航员未鸟 https://space.bilibili.com/2403955 👤\n | |
| 👤 Xz乔希 https://space.bilibili.com/5859321 👤\n | |
| 👤 怎么好就怎么来 https://space.bilibili.com/259582714 👤\n | |
| 🧠 Google Colab https://colab.research.google.com/ 🧠\n | |
| 📧 如果你是“以里illi”，并且希望对此模型主张权利，请通过上方“作者”部分的联系方式联系，我将积极配合处理。📧 \n | |
| """) | |
| 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) | |