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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
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