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", "alice": "https://huggingface.co/spaces/sayashi/sovits-models", "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( "#