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| 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 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): | |
| 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 | |
| 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 = [] | |
| voices = [] | |
| 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))) | |
| # !svc infer {NAME}.wav -c config.json -m G_riri_220.pth | |
| # display(Audio(f"{NAME}.out.wav", autoplay=True)) | |
| with gr.Blocks() as app: | |
| with gr.Tabs(): | |
| for (name, cover, vc_fn) in models: | |
| with gr.TabItem(name): | |
| 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) | |
| 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]) | |
| app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share) | |