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import sys, os |
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if sys.platform == "darwin": |
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" |
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import logging |
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logging.getLogger("numba").setLevel(logging.WARNING) |
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logging.getLogger("markdown_it").setLevel(logging.WARNING) |
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logging.getLogger("urllib3").setLevel(logging.WARNING) |
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logging.getLogger("matplotlib").setLevel(logging.WARNING) |
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logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s") |
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logger = logging.getLogger(__name__) |
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import torch |
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import argparse |
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import commons |
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import utils |
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from models import SynthesizerTrn |
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from text.symbols import symbols |
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from text import cleaned_text_to_sequence, get_bert |
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from text.cleaner import clean_text |
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import gradio as gr |
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import webbrowser |
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net_g = None |
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def get_text(text, language_str, hps): |
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norm_text, phone, tone, word2ph = clean_text(text, language_str) |
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phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) |
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if hps.data.add_blank: |
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phone = commons.intersperse(phone, 0) |
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tone = commons.intersperse(tone, 0) |
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language = commons.intersperse(language, 0) |
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for i in range(len(word2ph)): |
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word2ph[i] = word2ph[i] * 2 |
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word2ph[0] += 1 |
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bert = get_bert(norm_text, word2ph, language_str) |
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del word2ph |
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assert bert.shape[-1] == len(phone) |
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phone = torch.LongTensor(phone) |
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tone = torch.LongTensor(tone) |
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language = torch.LongTensor(language) |
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return bert, phone, tone, language |
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import soundfile as sf |
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def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid): |
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global net_g |
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bert, phones, tones, lang_ids = get_text(text, "ZH", hps) |
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with torch.no_grad(): |
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x_tst=phones.to(device).unsqueeze(0) |
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tones=tones.to(device).unsqueeze(0) |
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lang_ids=lang_ids.to(device).unsqueeze(0) |
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bert = bert.to(device).unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) |
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del phones |
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speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) |
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audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio |
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, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy() |
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del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers |
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sf.write("tmp.wav", audio, 44100) |
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return audio |
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def convert_wav_to_ogg(wav_file): |
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os.makedirs('out', exist_ok=True) |
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filename = os.path.splitext(os.path.basename(wav_file.name))[0] |
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output_path_ogg = os.path.join('out', f"out.ogg") |
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renamed_input_path = os.path.join('in', f"in.wav") |
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os.makedirs('in', exist_ok=True) |
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os.rename(wav_file.name, renamed_input_path) |
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command = ["ffmpeg", "-i", renamed_input_path, "-acodec", "libopus", "-y", output_path_ogg] |
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os.system(" ".join(command)) |
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return output_path_ogg |
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def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale): |
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with torch.no_grad(): |
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audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker) |
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with open('tmp.wav', 'rb') as wav_file: |
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newogg = convert_wav_to_ogg(wav_file) |
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return "Success", (hps.data.sampling_rate, audio),newogg |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--model_dir", default="./logs/2dd/2dd.pth", help="path of your model") |
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parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file") |
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parser.add_argument("--share", default=False, help="make link public") |
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parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log") |
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args = parser.parse_args() |
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if args.debug: |
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logger.info("Enable DEBUG-LEVEL log") |
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logging.basicConfig(level=logging.DEBUG) |
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hps = utils.get_hparams_from_file(args.config_dir) |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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''' |
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device = ( |
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"cuda:0" |
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if torch.cuda.is_available() |
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else ( |
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"mps" |
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if sys.platform == "darwin" and torch.backends.mps.is_available() |
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else "cpu" |
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) |
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) |
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''' |
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net_g = SynthesizerTrn( |
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len(symbols), |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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n_speakers=hps.data.n_speakers, |
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**hps.model).to(device) |
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_ = net_g.eval() |
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_ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True) |
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speaker_ids = hps.data.spk2id |
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speakers = list(speaker_ids.keys()) |
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with gr.Blocks() as app: |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown(value=""" |
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星瞳 常规直播 ver d Bert-Vits2在线语音生成\n |
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1、模型作者:数字星瞳企划 https://t.me/xingtong25680 \n |
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\n |
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2、原项目地址:https://github.com/Stardust-minus/Bert-VITS2\n |
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3、使用此模型进行二创请注明AI生成,以及该项目地址。\n |
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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 |
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""") |
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text = gr.TextArea(label="Text", placeholder="Input Text Here", |
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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。") |
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speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker') |
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sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label='语调变化') |
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noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.6, step=0.01, label='感情变化') |
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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') |
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length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='语速') |
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btn = gr.Button("开启AI语音之旅吧!", variant="primary") |
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with gr.Column(): |
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text_output = gr.Textbox(label="Message") |
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audio_output = gr.Audio(label="Output Audio") |
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ogg_output = gr.File(label="Converted OGG file") |
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gr.Markdown(value=""" |
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模型汇总:\n |
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星瞳整合 https://huggingface.co/spaces/digitalxingtong/Xingtong-All-in-One\n |
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甜甜叫花鸡 https://huggingface.co/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2 \n |
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七海 https://huggingface.co/spaces/digitalxingtong/Nanami-Bert-Vits2 \n |
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东雪莲 https://huggingface.co/spaces/digitalxingtong/Azuma-Bert-Vits2 \n |
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嘉然 https://huggingface.co/spaces/digitalxingtong/Jiaran-Bert-Vits2 \n |
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乃琳 https://huggingface.co/spaces/digitalxingtong/Eileen-Bert-Vits2 \n |
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恬豆 https://huggingface.co/spaces/digitalxingtong/Dou-Bert-Vits2 \n |
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奶绿 杂谈 https://huggingface.co/spaces/digitalxingtong/Nailv-Bert-Vits2 \n |
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奶绿 朗读 https://huggingface.co/spaces/digitalxingtong/Nailv-read-Bert-Vits2 \n |
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露早 https://huggingface.co/spaces/digitalxingtong/Luzao-Bert-Vits2 \n |
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柚恩 https://huggingface.co/spaces/digitalxingtong/Un-Bert-Vits2 \n |
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米诺 https://huggingface.co/spaces/digitalxingtong/Minuo-Bert-Vits2 \n |
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扇宝 https://huggingface.co/spaces/digitalxingtong/Shanbao-Bert-Vits2 \n |
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牧牧白 https://huggingface.co/spaces/digitalxingtong/Miiu-Bert-Vits2 \n |
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吉诺儿kino https://huggingface.co/spaces/digitalxingtong/Kino-Bert-Vits2 \n |
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九夏 https://huggingface.co/spaces/digitalxingtong/Jiuxia-Bert-Vits2 \n |
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卡缇娅 https://huggingface.co/spaces/digitalxingtong/Yaya-Bert-Vits2 \n |
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理想_ideal https://huggingface.co/spaces/digitalxingtong/Lixiang-Bert-Vits2 \n |
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阿梓 https://huggingface.co/spaces/digitalxingtong/Azusa-Bert-Vits2 \n |
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鹿鸣 https://huggingface.co/spaces/digitalxingtong/Luming-Bert-Vits2 \n |
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永雏塔菲 https://huggingface.co/spaces/digitalxingtong/Taffy-Bert-VITS2 \n |
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""") |
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btn.click(tts_fn, |
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inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale], |
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outputs=[text_output, audio_output,ogg_output]) |
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app.launch(show_error=True) |
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