#Add a comment to get your space restarted. This comment has no effect on behavior. 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" # 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, 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 > 20 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( "#
Sovits Models\n" "##
The input audio should be clean and pure voice without background music.\n" "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=sayashi.Sovits-Umamusume)\n\n" "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wfsBbMzmtLflOJeqc5ZnJiLY7L239hJW?usp=share_link)\n\n" "[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/sayashi/sovits-models?duplicate=true)\n\n" "[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](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( '
' f'' if cover else "" '
' ) 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'''

Click to Go

''' ) app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)