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| import os | |
| import io | |
| import gradio as gr | |
| import librosa | |
| import numpy as np | |
| import logging | |
| import soundfile | |
| import torchaudio | |
| import asyncio | |
| import argparse | |
| import subprocess | |
| 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 | |
| def unused_vc_fn(input_audio, vc_transform, voice): | |
| 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=True, | |
| ) | |
| return "Success", (44100, out_audio.cpu().numpy()) | |
| def run_inference(input_audio, speaker): | |
| 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) | |
| #TODO edit from GUI | |
| cluster_ratio = 1 | |
| noise_scale = 2 | |
| is_pitch_prediction_enabled = True | |
| f0_method = "dio" | |
| transpose = 0 | |
| model_path = f"./models/{speaker}/{speaker}.pth" | |
| config_path = f"./models/{speaker}/config.json" | |
| cluster_path = "" | |
| raw_path = 'tmp.wav' | |
| soundfile.write(raw_path, audio, 16000, format="wav") | |
| inference_cmd = f"svc infer {raw_path} -m {model_path} -c {config_path} {f'-k {cluster_path} -r {cluster_ratio}' if cluster_path != '' and cluster_ratio > 0 else ''} -t {transpose} --f0-method {f0_method} -n {noise_scale} -o out.wav {'' if is_pitch_prediction_enabled else '--no-auto-predict-f0'}" | |
| print(inference_cmd) | |
| result = subprocess.run( | |
| inference_cmd.split(), | |
| stdout=subprocess.PIPE, | |
| stderr=subprocess.STDOUT, | |
| text=True | |
| ) | |
| audio, sr = torchaudio.load('out.wav') | |
| out_audio = audio.cpu().numpy()[0] | |
| print(out_audio) | |
| return 'out.wav' # (sr, out_audio) | |
| 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() | |
| speakers = ["chapaev", "petka", "anka", "narrator", "floppa"] | |
| models = [] | |
| voices = [] | |
| # !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: | |
| gr.Markdown( | |
| "# <center> Sovits Chapay\n" | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '') | |
| speaker = gr.Dropdown(label="Speaker", choices=speakers, visible=True) | |
| vc_submit = gr.Button("Generate", variant="primary") | |
| with gr.Column(): | |
| vc_output = gr.Audio(label="Output Audio") | |
| vc_submit.click(run_inference, [vc_input, speaker], [vc_output]) | |
| app.queue(concurrency_count=1, api_open=True).launch(show_api=True, show_error=True) | |