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Running
on
Zero
| from functools import partial | |
| import torch | |
| import numpy as np | |
| import librosa | |
| from librosa.filters import mel as librosa_mel_fn | |
| from torchaudio.functional import resample as ta_resample_fn | |
| MAX_WAV_VALUE = 32767.0 # NOTE: 32768.0 -1 to prevent int16 overflow (results in popping sound in corner cases) | |
| 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 | |
| ): | |
| 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 | |
| ) | |
| str_key_mel_basis = str(fmax) + "_" + str(y.device) | |
| mel_basis[str_key_mel_basis] = 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_key_mel_basis], spec) | |
| spec = spectral_normalize_torch(spec) | |
| return spec | |
| kaiser_best_resampling_fn = partial( | |
| ta_resample_fn, | |
| resampling_method="sinc_interp_kaiser", # DO NOT CHANGE! | |
| rolloff=0.917347, # DO NOT CHANGE! | |
| beta=12.9846, # DO NOT CHANGE! | |
| lowpass_filter_width=50, # DO NOT CHANGE! | |
| ) | |
| class MelSpectrogramExtractor(object): | |
| def __init__( | |
| self, | |
| n_fft=1024, | |
| win_size=1024, | |
| num_mels=100, | |
| hop_size=160, | |
| sampling_rate=16000, | |
| fmin=0, | |
| fmax=None, | |
| ): | |
| self.n_fft = n_fft | |
| self.win_size = win_size | |
| self.num_mels = num_mels | |
| self.hop_size = hop_size | |
| self.sampling_rate = sampling_rate | |
| self.fmin = fmin | |
| self.fmax = fmax | |
| def __call__(self, wav_path) -> np.ndarray: | |
| wav_data, wav_sr = librosa.load(wav_path, sr=None, mono=True) | |
| wav_data = torch.from_numpy(wav_data.copy()).unsqueeze(0) | |
| # for 16k wavs, up-downsample to reduce artifects | |
| if wav_sr == self.sampling_rate: | |
| wav_data = kaiser_best_resampling_fn(wav_data, orig_freq=wav_sr, new_freq=24000) | |
| wav_data = kaiser_best_resampling_fn(wav_data, orig_freq=24000, new_freq=self.sampling_rate) | |
| else: | |
| wav_data = kaiser_best_resampling_fn(wav_data, orig_freq=wav_sr, new_freq=self.sampling_rate) | |
| # (1, num_mels, t) | |
| mel = mel_spectrogram( | |
| wav_data, | |
| self.n_fft, | |
| self.num_mels, | |
| self.sampling_rate, | |
| self.hop_size, | |
| self.win_size, | |
| self.fmin, | |
| self.fmax, | |
| ) | |
| mel = mel.squeeze(0).transpose(1, 0) | |
| return mel # (t, num_mels) | |