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| """ | |
| Util functions to process any incoming audio data to be processable by the model | |
| """ | |
| import torch | |
| import torchaudio | |
| def process_from_filename(filename, target_sample_rate=4800, wav_length=5): | |
| wav, sample_rate = torchaudio.load(filename) | |
| wav = process_raw_wav(wav, sample_rate, target_sample_rate, wav_length) | |
| spec = _wav_to_spec(wav, target_sample_rate) | |
| return spec | |
| def process_raw_wav(wav, sample_rate, target_sample_rate=4800, wav_length=5): | |
| num_samples = wav_length * target_sample_rate | |
| wav = _resample(wav, sample_rate, target_sample_rate) | |
| wav = _mix_down(wav) | |
| wav = _cut(wav, num_samples) | |
| wav = _pad(wav, num_samples) | |
| return wav | |
| def _wav_to_spec(wav, target_sample_rate): | |
| mel_spectrogram = torchaudio.transforms.MelSpectrogram( | |
| sample_rate=target_sample_rate, | |
| n_fft=1024, | |
| hop_length=512, | |
| n_mels=64 | |
| ) | |
| return mel_spectrogram(wav) | |
| def _resample(wav, sample_rate, target_sample_rate): | |
| if sample_rate != target_sample_rate: | |
| resampler = torchaudio.transforms.Resample(sample_rate, target_sample_rate) | |
| wav = resampler(wav) | |
| return wav | |
| def _mix_down(wav): | |
| if wav.shape[0] > 1: | |
| wav = torch.mean(wav, dim=0, keepdim=True) | |
| return wav | |
| def _cut(wav, num_samples): | |
| if wav.shape[1] > num_samples: | |
| wav = wav[:, :num_samples] | |
| return wav | |
| def _pad(wav, num_samples): | |
| if wav.shape[1] < num_samples: | |
| missing_samples = num_samples - wav.shape[1] | |
| pad = (0, missing_samples) | |
| wav = torch.nn.function.pad(wav, pad) | |
| return wav |