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Runtime error
| import io | |
| import os | |
| os.system("wget -P hubert/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt") | |
| import gradio as gr | |
| import librosa | |
| import numpy as np | |
| import soundfile | |
| from inference.infer_tool import Svc | |
| import logging | |
| 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) | |
| model = Svc("logs/44k/G_199200.pth", "logs/44k/config.json", cluster_model_path="logs/44k/kmeans_10000.pt") | |
| def predict(input_audio, not_singing): | |
| 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 > 45: | |
| return "Please upload audio less than 45 seconds", 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) | |
| print(audio.shape) | |
| out_wav_path = "temp.wav" | |
| soundfile.write(out_wav_path, audio, 16000, format="wav") | |
| out_audio, out_sr = model.infer("aimodel", 0, out_wav_path, | |
| cluster_infer_ratio=0, | |
| auto_predict_f0=not_singing, | |
| noice_scale=0.4 | |
| ) | |
| return (44100, out_audio.numpy()) | |
| audio_input = gr.Audio(label="Upload Audio") | |
| not_singing = gr.Checkbox(label="Check this box if this audio is not singing", value=False) | |
| audio_output = gr.Audio(label="Output Audio") | |
| demo = gr.Interface(predict, inputs=[audio_input, not_singing], outputs=[audio_output]) | |
| # app = gr.Blocks() | |
| # with app: | |
| # audio_input = gr.Audio(label="Upload Audio") | |
| # not_singing = gr.Checkbox(label="Check this box if this audio is not singing", value=False) | |
| # audio_output = gr.Audio(label="Output Audio") | |
| # submit_btn = gr.Button("Submit", variant="primary") | |
| # submit_btn.click(predict, [audio_input, not_singing], [audio_output], api_name="predict") | |
| demo.launch() | |