| import os |
| import io |
| import gradio as gr |
| import librosa |
| import numpy as np |
| import utils |
| from inference.infer_tool import Svc |
| import logging |
| import soundfile |
| import asyncio |
| import argparse |
| import edge_tts |
| 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" |
|
|
| 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, tts_text, tts_voice, tts_mode): |
| if tts_mode: |
| if len(tts_text) > 100 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", sr=16000, mono=True) |
| raw_path = io.BytesIO() |
| soundfile.write(raw_path, audio, 16000, format="wav") |
| raw_path.seek(0) |
| out_audio, out_sr = model.infer(sid, vc_transform, raw_path, |
| auto_predict_f0=auto_f0, |
| ) |
| return "Success", (44100, out_audio.cpu().numpy()) |
| if input_audio is None: |
| return "You need to upload an audio", None |
| sampling_rate, audio = input_audio |
| duration = audio.shape[0] / sampling_rate |
| if duration > 300 and limitation: |
| return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None |
| audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) |
| if len(audio.shape) > 1: |
| audio = librosa.to_mono(audio.transpose(1, 0)) |
| if sampling_rate != 16000: |
| audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) |
| raw_path = io.BytesIO() |
| soundfile.write(raw_path, audio, 16000, format="wav") |
| raw_path.seek(0) |
| out_audio, out_sr = model.infer(sid, vc_transform, raw_path, |
| auto_predict_f0=auto_f0, |
| ) |
| return "Success", (44100, out_audio.cpu().numpy()) |
| return vc_fn |
|
|
| def change_to_tts_mode(tts_mode): |
| if tts_mode: |
| return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True), gr.Checkbox.update(value=True) |
| else: |
| return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False), gr.Checkbox.update(value=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") |
| args = parser.parse_args() |
| hubert_model = utils.get_hubert_model().to(args.device) |
| models = [] |
| others = { |
| "rudolf": "https://huggingface.co/spaces/sayashi/sovits-rudolf", |
| "teio": "https://huggingface.co/spaces/sayashi/sovits-teio", |
| "goldship": "https://huggingface.co/spaces/sayashi/sovits-goldship", |
| "tannhauser": "https://huggingface.co/spaces/sayashi/sovits-tannhauser" |
| } |
| 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']}") |
| 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 Models\n" |
| "## <center> The input audio should be clean and pure voice without background music.\n" |
| "\n\n" |
| "[](https://colab.research.google.com/drive/1wfsBbMzmtLflOJeqc5ZnJiLY7L239hJW?usp=share_link)\n\n" |
| "[](https://huggingface.co/spaces/sayashi/sovits-models?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(): |
| gr.Markdown( |
| '<div align="center">' |
| f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "" |
| '</div>' |
| ) |
| with gr.Row(): |
| with gr.Column(): |
| vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '') |
| vc_transform = gr.Number(label="vc_transform", value=0) |
| 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") |
| with gr.Column(): |
| vc_output1 = gr.Textbox(label="Output Message") |
| vc_output2 = gr.Audio(label="Output Audio") |
| vc_submit.click(vc_fn, [vc_input, vc_transform, auto_f0, tts_text, tts_voice, tts_mode], [vc_output1, vc_output2]) |
| tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice, auto_f0]) |
| for category, link in others.items(): |
| with gr.TabItem(category): |
| gr.Markdown( |
| f''' |
| <center> |
| <h2>Click to Go</h2> |
| <a href="{link}"> |
| <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-xl-dark.svg" |
| </a> |
| </center> |
| ''' |
| ) |
| app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share) |
|
|
