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| from gyraudio.audio_separation.data.dataset import AudioDataset | |
| from typing import Tuple | |
| import logging | |
| from torch import Tensor | |
| import torch | |
| import torchaudio | |
| class RemixedAudioDataset(AudioDataset): | |
| def generate_snr_list(self): | |
| self.snr_list = None | |
| def load_data(self): | |
| self.folder_list = sorted(list(self.data_path.iterdir())) | |
| self.file_list = [ | |
| [ | |
| folder/"voice.wav", | |
| folder/"noise.wav" | |
| ] for folder in self.folder_list | |
| ] | |
| self.sampling_rate = None | |
| self.min_snr, self.max_snr = -4, 4 | |
| self.generate_snr_list() | |
| if self.debug: | |
| print("Not filtered", len(self.file_list), self.snr_filter) | |
| print(self.snr_list) | |
| def get_idx_noise(self, idx): | |
| raise NotImplementedError("get_idx_noise method must be implemented") | |
| def get_snr(self, idx): | |
| raise NotImplementedError("get_snr method must be implemented") | |
| def __getitem__(self, idx: int) -> Tuple[Tensor, Tensor, Tensor]: | |
| signal_path = self.file_list[idx][0] | |
| idx_noise = self.get_idx_noise(idx) | |
| noise_path = self.file_list[idx_noise][1] | |
| assert signal_path.exists() | |
| assert noise_path.exists() | |
| clean_audio_signal, sampling_rate = torchaudio.load(str(signal_path)) | |
| noise_audio_signal, sampling_rate = torchaudio.load(str(noise_path)) | |
| snr = self.get_snr(idx) | |
| alpha = 10 ** (-snr / 20) * torch.norm(clean_audio_signal) / torch.norm(noise_audio_signal) | |
| mixed_audio_signal = clean_audio_signal + alpha*noise_audio_signal | |
| 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 | |