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| import os | |
| import gradio as gr | |
| import edge_tts | |
| from pathlib import Path | |
| import inference.infer_tool as infer_tool | |
| import utils | |
| from inference.infer_tool import Svc | |
| import logging | |
| import webbrowser | |
| import argparse | |
| import asyncio | |
| import librosa | |
| import soundfile | |
| import gradio.processing_utils as gr_processing_utils | |
| 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) | |
| limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces | |
| audio_postprocess_ori = gr.Audio.postprocess | |
| def audio_postprocess(self, y): | |
| data = audio_postprocess_ori(self, y) | |
| if data is None: | |
| return None | |
| return gr_processing_utils.encode_url_or_file_to_base64(data["name"]) | |
| gr.Audio.postprocess = audio_postprocess | |
| def create_vc_fn(model, sid): | |
| def vc_fn(input_audio, vc_transform, auto_f0, slice_db, noise_scale, pad_seconds, tts_text, tts_voice, tts_mode): | |
| if tts_mode: | |
| if len(tts_text) > 300 and limitation: | |
| return "Text is too long", None | |
| if tts_text is None or tts_voice is None: | |
| return "You need to enter text and select a voice", None | |
| asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3")) | |
| audio, sr = librosa.load("tts.mp3") | |
| soundfile.write("tts.wav", audio, 24000, format="wav") | |
| wav_path = "tts.wav" | |
| else: | |
| if input_audio is None: | |
| return "You need to select an audio", None | |
| raw_audio_path = f"raw/{input_audio}" | |
| if "." not in raw_audio_path: | |
| raw_audio_path += ".wav" | |
| infer_tool.format_wav(raw_audio_path) | |
| wav_path = Path(raw_audio_path).with_suffix('.wav') | |
| _audio = model.slice_inference( | |
| wav_path, sid, vc_transform, slice_db, | |
| cluster_infer_ratio=0, | |
| auto_predict_f0=auto_f0, | |
| noice_scale=noise_scale, | |
| pad_seconds=pad_seconds) | |
| model.clear_empty() | |
| return "Success", (44100, _audio) | |
| return vc_fn | |
| def refresh_raw_wav(): | |
| return gr.Dropdown.update(choices=os.listdir("raw")) | |
| def change_to_tts_mode(tts_mode): | |
| if tts_mode: | |
| return gr.Audio.update(visible=False), gr.Button.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True) | |
| else: | |
| return gr.Audio.update(visible=True), gr.Button.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False) | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--device', type=str, default='cpu') | |
| parser.add_argument('--api', action="store_true", default=False) | |
| parser.add_argument("--share", action="store_true", default=False, help="share gradio app") | |
| parser.add_argument("--colab", action="store_true", default=False, help="share gradio app") | |
| args = parser.parse_args() | |
| hubert_model = utils.get_hubert_model().to(args.device) | |
| models = [] | |
| voices = [] | |
| tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) | |
| for r in tts_voice_list: | |
| voices.append(f"{r['ShortName']}-{r['Gender']}") | |
| raw = os.listdir("raw") | |
| for f in os.listdir("models"): | |
| name = f | |
| model = Svc(fr"models/{f}/{f}.pth", f"models/{f}/config.json", device=args.device) | |
| cover = f"models/{f}/cover.png" if os.path.exists(f"models/{f}/cover.png") else None | |
| models.append((name, cover, create_vc_fn(model, name))) | |
| with gr.Blocks() as app: | |
| gr.Markdown( | |
| "# <center> Sovits Anime\n" | |
| "## <center> The input audio should be clean and pure voice without background music.\n" | |
| "<center> Original gradio code from: https://huggingface.co/spaces/zomehwh/sovits-models\n\n" | |
| "#\n\n" | |
| "[](https://colab.research.google.com/drive/1FvgQCzmWGBQ26V33Z-rNgJJKzLnYe8mH?usp=sharing)\n\n" | |
| "[](https://huggingface.co/spaces/pitawat02/sovits-anime?duplicate=true)\n\n" | |
| "[](https://github.com/svc-develop-team/so-vits-svc)" | |
| ) | |
| with gr.Tabs(): | |
| for (name, cover, vc_fn) in models: | |
| with gr.TabItem(name): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| vc_input = gr.Dropdown(label="Input audio", choices=raw) | |
| vc_refresh = gr.Button("🔁", variant="primary") | |
| vc_transform = gr.Number(label="vc_transform", value=0) | |
| slice_db = gr.Number(label="slice_db", value=-40) | |
| noise_scale = gr.Number(label="noise_scale", value=0.4) | |
| pad_seconds = gr.Number(label="pad_seconds", value=0.5) | |
| auto_f0 = gr.Checkbox(label="auto_f0", value=False) | |
| tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False) | |
| tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text") | |
| tts_voice = gr.Dropdown(choices=voices, visible=False) | |
| vc_submit = gr.Button("Generate", variant="primary") | |
| vc_output1 = gr.Textbox(label="Output Message") | |
| vc_output2 = gr.Audio(label="Output Audio") | |
| gr.Markdown( | |
| f"## <center> {name}\n" | |
| '<div align="center">' | |
| f'<img style="width:300px;height:auto;" src="file/{cover}">' if cover else "" | |
| '</div>' | |
| ) | |
| vc_submit.click(vc_fn, [vc_input, vc_transform, auto_f0, slice_db, noise_scale, pad_seconds, tts_text, tts_voice, tts_mode], [vc_output1, vc_output2]) | |
| vc_refresh.click(refresh_raw_wav, [], [vc_input]) | |
| tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, vc_refresh, tts_text, tts_voice]) | |
| if args.colab: | |
| webbrowser.open("http://127.0.0.1:7860") | |
| app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share) |