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| import os | |
| import torch | |
| from torch.utils.data import Dataset | |
| import pandas as pd | |
| import torchaudio | |
| class VoiceDataset(Dataset): | |
| def __init__( | |
| self, | |
| data_directory, | |
| transformation, | |
| device, | |
| target_sample_rate=48000, | |
| time_limit_in_secs=5, | |
| ): | |
| # file processing | |
| self._data_path = os.path.join(data_directory) | |
| self._labels = os.listdir(self._data_path) | |
| self.label_mapping = {label: i for i, label in enumerate(self._labels)} | |
| self.audio_files_labels = self._join_audio_files() | |
| self.device = device | |
| # audio processing | |
| self.transformation = transformation | |
| self.target_sample_rate = target_sample_rate | |
| self.num_samples = time_limit_in_secs * self.target_sample_rate | |
| def __len__(self): | |
| return len(self.audio_files_labels) | |
| def __getitem__(self, index): | |
| # get file | |
| file, label = self.audio_files_labels[index] | |
| filepath = os.path.join(self._data_path, label, file) | |
| # load wav | |
| wav, sr = torchaudio.load(filepath, normalize=True) | |
| # modify wav file, if necessary | |
| wav = wav.to(self.device) | |
| wav = self._resample(wav, sr) | |
| wav = self._mix_down(wav) | |
| wav = self._cut_or_pad(wav) | |
| # apply transformation | |
| wav = self.transformation(wav) | |
| # return wav and integer representation of the label | |
| return wav, self.label_mapping[label] | |
| def _join_audio_files(self): | |
| """Join all the audio file names and labels into one single dimenional array""" | |
| audio_files_labels = [] | |
| for label in self._labels: | |
| label_path = os.path.join(self._data_path, label) | |
| for f in os.listdir(label_path): | |
| audio_files_labels.append((f, label)) | |
| return audio_files_labels | |
| def _resample(self, wav, current_sample_rate): | |
| """Resample audio to the target sample rate, if necessary""" | |
| if current_sample_rate != self.target_sample_rate: | |
| resampler = torchaudio.transforms.Resample(current_sample_rate, self.target_sample_rate) | |
| wav = resampler(wav) | |
| return wav | |
| def _mix_down(self, wav): | |
| """Mix down audio to a single channel, if necessary""" | |
| if wav.shape[0] > 1: | |
| wav = torch.mean(wav, dim=0, keepdim=True) | |
| return wav | |
| def _cut_or_pad(self, wav): | |
| """Modify audio if number of samples != target number of samples of the dataset. | |
| If there are too many samples, cut the audio. | |
| If there are not enough samples, pad the audio with zeros. | |
| """ | |
| length_signal = wav.shape[1] | |
| if length_signal > self.num_samples: | |
| wav = wav[:, :self.num_samples] | |
| elif length_signal < self.num_samples: | |
| num_of_missing_samples = self.num_samples - length_signal | |
| pad = (0, num_of_missing_samples) | |
| wav = torch.nn.functional.pad(wav, pad) | |
| return wav | |