| import sys, os | |
| if sys.platform == "darwin": | |
| os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" | |
| 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 | |
| net_g = None | |
| 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) | |
| del word2ph | |
| assert bert.shape[-1] == len(phone) | |
| phone = torch.LongTensor(phone) | |
| tone = torch.LongTensor(tone) | |
| language = torch.LongTensor(language) | |
| return bert, phone, tone, language | |
| import soundfile as sf | |
| def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid): | |
| global net_g | |
| bert, phones, tones, lang_ids = get_text(text, "ZH", 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) | |
| 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, 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 | |
| sf.write("tmp.wav", audio, 44100) | |
| return audio | |
| def convert_wav_to_ogg(wav_file): | |
| os.makedirs('out', exist_ok=True) | |
| filename = os.path.splitext(os.path.basename(wav_file.name))[0] | |
| output_path_ogg = os.path.join('out', f"out.ogg") | |
| renamed_input_path = os.path.join('in', f"in.wav") | |
| os.makedirs('in', exist_ok=True) | |
| os.rename(wav_file.name, renamed_input_path) | |
| command = ["ffmpeg", "-i", renamed_input_path, "-acodec", "libopus", "-y", output_path_ogg] | |
| os.system(" ".join(command)) | |
| return output_path_ogg | |
| def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale): | |
| with torch.no_grad(): | |
| audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker) | |
| with open('tmp.wav', 'rb') as wav_file: | |
| newogg = convert_wav_to_ogg(wav_file) | |
| return "Success", (hps.data.sampling_rate, audio),newogg | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--model_dir", default="./logs/3da/3da.pth", help="path of your model") | |
| parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file") | |
| parser.add_argument("--share", default=False, help="make link public") | |
| 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_dir) | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| ''' | |
| 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_dir, net_g, None, skip_optimizer=True) | |
| speaker_ids = hps.data.spk2id | |
| speakers = list(speaker_ids.keys()) | |
| with gr.Blocks() as app: | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown(value=""" | |
| 星瞳 常规直播 ver e Bert-Vits2在线语音生成\n | |
| 1、模型作者：数字星瞳企划 https://t.me/xingtong25680 \n | |
| \n | |
| 2、原项目地址：https://github.com/Stardust-minus/Bert-VITS2\n | |
| 3、使用此模型进行二创请注明AI生成，以及该项目地址。\n | |
| 4、如果想生成超长txt文本的音频请使用colab。 https://colab.research.google.com/drive/13ek8_j1aknr-pbjj3NXxSM4vBIsracU3?usp=drive_link\n | |
| """) | |
| text = gr.TextArea(label="Text", placeholder="Input Text Here", | |
| value="这里是数字星瞳企画，请在电报搜索星瞳全拼加二五六八零，获取最新更新进展。") | |
| speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker') | |
| sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label='语调变化') | |
| noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.6, step=0.01, label='感情变化') | |
| noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.8, step=0.01, label='音节发音长度变化') | |
| length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='语速') | |
| btn = gr.Button("开启AI语音之旅吧!", variant="primary") | |
| with gr.Column(): | |
| text_output = gr.Textbox(label="Message") | |
| audio_output = gr.Audio(label="Output Audio") | |
| ogg_output = gr.File(label="Converted OGG file") | |
| gr.Markdown(value=""" | |
| 模型汇总：\n | |
| 星瞳整合 https://huggingface.co/spaces/digitalxingtong/Xingtong-All-in-One\n | |
| 甜甜叫花鸡 https://huggingface.co/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2 \n | |
| 七海 https://huggingface.co/spaces/digitalxingtong/Nanami-Bert-Vits2 \n | |
| 东雪莲 https://huggingface.co/spaces/digitalxingtong/Azuma-Bert-Vits2 \n | |
| 嘉然 https://huggingface.co/spaces/digitalxingtong/Jiaran-Bert-Vits2 \n | |
| 乃琳 https://huggingface.co/spaces/digitalxingtong/Eileen-Bert-Vits2 \n | |
| 恬豆 https://huggingface.co/spaces/digitalxingtong/Dou-Bert-Vits2 \n | |
| 奶绿 杂谈 https://huggingface.co/spaces/digitalxingtong/Nailv-Bert-Vits2 \n | |
| 奶绿 朗读 https://huggingface.co/spaces/digitalxingtong/Nailv-read-Bert-Vits2 \n | |
| 露早 https://huggingface.co/spaces/digitalxingtong/Luzao-Bert-Vits2 \n | |
| 柚恩 https://huggingface.co/spaces/digitalxingtong/Un-Bert-Vits2 \n | |
| 米诺 https://huggingface.co/spaces/digitalxingtong/Minuo-Bert-Vits2 \n | |
| 扇宝 https://huggingface.co/spaces/digitalxingtong/Shanbao-Bert-Vits2 \n | |
| 牧牧白 https://huggingface.co/spaces/digitalxingtong/Miiu-Bert-Vits2 \n | |
| 吉诺儿kino https://huggingface.co/spaces/digitalxingtong/Kino-Bert-Vits2 \n | |
| 九夏 https://huggingface.co/spaces/digitalxingtong/Jiuxia-Bert-Vits2 \n | |
| 卡缇娅 https://huggingface.co/spaces/digitalxingtong/Yaya-Bert-Vits2 \n | |
| 理想_ideal https://huggingface.co/spaces/digitalxingtong/Lixiang-Bert-Vits2 \n | |
| 阿梓 https://huggingface.co/spaces/digitalxingtong/Azusa-Bert-Vits2 \n | |
| 鹿鸣 https://huggingface.co/spaces/digitalxingtong/Luming-Bert-Vits2 \n | |
| 永雏塔菲 https://huggingface.co/spaces/digitalxingtong/Taffy-Bert-VITS2 \n | |
| """) | |
| btn.click(tts_fn, | |
| inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale], | |
| outputs=[text_output, audio_output,ogg_output]) | |
| app.launch(show_error=True) | |