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| from gyraudio.audio_separation.data.dataset import AudioDataset | |
| import logging | |
| import torch | |
| import torchaudio | |
| from typing import Tuple | |
| class MixedAudioDataset(AudioDataset): | |
| def load_data(self): | |
| self.folder_list = sorted(list(self.data_path.iterdir())) | |
| self.file_list = [ | |
| [ | |
| list(folder.glob("mix*.wav"))[0], | |
| folder/"voice.wav", | |
| folder/"noise.wav" | |
| ] for folder in self.folder_list | |
| ] | |
| snr_list = [float(file[0].stem.split("_")[-1]) for file in self.file_list] | |
| self.file_list = [files for snr, files in zip(snr_list, self.file_list) if self.filter_data(snr)] | |
| if self.debug: | |
| logging.info(f"Available SNR {set(snr_list)}") | |
| print(f"Available SNR {set(snr_list)}") | |
| print("Filtered", len(self.file_list), self.snr_filter) | |
| self.sampling_rate = None | |
| def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: | |
| mixed_audio_path, signal_path, noise_path = self.file_list[idx] | |
| assert mixed_audio_path.exists() | |
| assert signal_path.exists() | |
| assert noise_path.exists() | |
| mixed_audio_signal, sampling_rate = torchaudio.load(str(mixed_audio_path)) | |
| clean_audio_signal, sampling_rate = torchaudio.load(str(signal_path)) | |
| noise_audio_signal, sampling_rate = torchaudio.load(str(noise_path)) | |
| self.sampling_rate = sampling_rate | |
| mixed_audio_signal, clean_audio_signal, noise_audio_signal = self.augment_data(mixed_audio_signal, clean_audio_signal, noise_audio_signal) | |
| if self.debug: | |
| logging.debug(f"{mixed_audio_signal.shape}") | |
| logging.debug(f"{clean_audio_signal.shape}") | |
| logging.debug(f"{noise_audio_signal.shape}") | |
| return mixed_audio_signal, clean_audio_signal, noise_audio_signal | |