| import torch |
| import numpy |
| import os |
| from dataloader.audio.preprocess_vgg.vggish_input import waveform_to_examples |
| import soundfile |
|
|
|
|
| class Audio(torch.utils.data.Dataset): |
| def __init__(self, augmentation, directory_path, split): |
| |
| self.augmentation = augmentation |
| self.directory_path = directory_path |
| self.split = split |
|
|
| def load_audio_wave(self, file_index, file_index_mix): |
| audio_path = os.path.join(file_index, 'audio.wav') |
| wav_data, sample_rate = soundfile.read(audio_path, dtype='int16') |
| assert wav_data.dtype == numpy.int16, 'Bad sample type: %r' % wav_data.dtype |
|
|
| if file_index_mix is not None: |
| audio_path2 = os.path.join(file_index_mix, 'audio.wav') |
| wav_data2, _ = soundfile.read(audio_path2, dtype='int16') |
| mix_lambda = numpy.random.beta(10, 10) |
| min_length = min(wav_data.shape[0], wav_data2.shape[0]) |
| wav_data = wav_data[:min_length] * mix_lambda + wav_data2[:min_length] * (1-mix_lambda) |
|
|
| wav_data = self.augmentation(wav_data, sample_rate, self.split) |
| audio_log_mel = torch.cat([waveform_to_examples(wav_data[:, 0], sample_rate, True).detach(), |
| waveform_to_examples(wav_data[:, 1], sample_rate, True).detach()], dim=1) |
|
|
| |
| if audio_log_mel.shape[0] < 5: |
| audio_log_mel = torch.cat([audio_log_mel, |
| audio_log_mel[-1].unsqueeze(0).repeat(5-audio_log_mel.shape[0], 1, 1, 1)]) |
| return audio_log_mel |
|
|
| def __len__(self): |
| return len(self.audio_list) |
|
|
