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| import argparse | |
| import torch | |
| import torch.utils.data | |
| import numpy as np | |
| import librosa | |
| from omegaconf import OmegaConf | |
| from librosa.filters import mel as librosa_mel_fn | |
| MAX_WAV_VALUE = 32768.0 | |
| def load_wav_to_torch(full_path, sample_rate): | |
| wav, _ = librosa.load(full_path, sr=sample_rate) | |
| wav = wav / np.abs(wav).max() * 0.6 | |
| return torch.FloatTensor(wav) | |
| def dynamic_range_compression(x, C=1, clip_val=1e-5): | |
| return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) | |
| def dynamic_range_decompression(x, C=1): | |
| return np.exp(x) / C | |
| def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): | |
| return torch.log(torch.clamp(x, min=clip_val) * C) | |
| def dynamic_range_decompression_torch(x, C=1): | |
| return torch.exp(x) / C | |
| def spectral_normalize_torch(magnitudes): | |
| output = dynamic_range_compression_torch(magnitudes) | |
| return output | |
| def spectral_de_normalize_torch(magnitudes): | |
| output = dynamic_range_decompression_torch(magnitudes) | |
| return output | |
| mel_basis = {} | |
| hann_window = {} | |
| def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): | |
| if torch.min(y) < -1.: | |
| print('min value is ', torch.min(y)) | |
| if torch.max(y) > 1.: | |
| print('max value is ', torch.max(y)) | |
| global mel_basis, hann_window | |
| if fmax not in mel_basis: | |
| mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) | |
| mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device) | |
| hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) | |
| y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') | |
| y = y.squeeze(1) | |
| # complex tensor as default, then use view_as_real for future pytorch compatibility | |
| spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)], | |
| center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True) | |
| spec = torch.view_as_real(spec) | |
| spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9)) | |
| spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec) | |
| spec = spectral_normalize_torch(spec) | |
| return spec | |
| def mel_spectrogram_file(path, hps): | |
| audio = load_wav_to_torch(path, hps.data.sampling_rate) | |
| audio = audio.unsqueeze(0) | |
| # match audio length to self.hop_length * n for evaluation | |
| if (audio.size(1) % hps.data.hop_length) != 0: | |
| audio = audio[:, :-(audio.size(1) % hps.data.hop_length)] | |
| mel = mel_spectrogram(audio, hps.data.filter_length, hps.data.mel_channels, hps.data.sampling_rate, | |
| hps.data.hop_length, hps.data.win_length, hps.data.mel_fmin, hps.data.mel_fmax, center=False) | |
| return mel | |
| def print_mel(mel, path="mel.png"): | |
| import matplotlib.pyplot as plt | |
| fig = plt.figure(figsize=(12, 4)) | |
| if isinstance(mel, torch.Tensor): | |
| mel = mel.cpu().numpy() | |
| plt.pcolor(mel) | |
| plt.savefig(path, format="png") | |
| plt.close(fig) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("-w", "--wav", help="wav", dest="wav") | |
| parser.add_argument("-m", "--mel", help="mel", dest="mel") # csv for excel | |
| args = parser.parse_args() | |
| print(args.wav) | |
| print(args.mel) | |
| hps = OmegaConf.load(f"./configs/base.yaml") | |
| mel = mel_spectrogram_file(args.wav, hps) | |
| # TODO | |
| mel = torch.squeeze(mel, 0) | |
| # [100, length] | |
| torch.save(mel, args.mel) | |
| print_mel(mel, "debug.mel.png") | |