import io import os import gradio as gr import librosa import numpy as np import soundfile from inference.infer_tool import Svc import logging import webbrowser import argparse 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 def create_vc_fn(model, sid): def vc_fn(input_audio, vc_transform, auto_f0): if input_audio is None: return "You need to upload an audio", None sampling_rate, audio = input_audio # print(audio.shape,sampling_rate) duration = audio.shape[0] / sampling_rate if duration > 45 and limitation: return "Please upload an audio file that is less than 45 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) out_wav_path = "temp.wav" soundfile.write(out_wav_path, audio, 16000, format="wav") out_audio, out_sr = model.infer(sid, vc_transform, out_wav_path, auto_predict_f0=auto_f0, ) return "Success", (44100, out_audio.cpu().numpy()) return vc_fn 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() models = [] 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 Umamusume\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" "[Open In Colab](https://colab.research.google.com/drive/1wfsBbMzmtLflOJeqc5ZnJiLY7L239hJW?usp=share_link)" "\n\n" "[Original Repo](https://github.com/innnky/so-vits-svc/tree/4.0)" ) 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 45 seconds)' if limitation else '') vc_transform = gr.Number(label="vc_transform", value=0) auto_f0 = gr.Checkbox(label="auto_f0", value=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], [vc_output1, vc_output2]) if args.colab: webbrowser.open("http://127.0.0.1:7860") app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)