import torch import torch.utils.data from librosa.filters import mel as librosa_mel_fn import logging logger = logging.getLogger(__name__) MAX_WAV_VALUE = 32768.0 def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): """ PARAMS ------ C: compression factor """ return torch.log(torch.clamp(x, min=clip_val) * C) def dynamic_range_decompression_torch(x, C=1): """ PARAMS ------ C: compression factor used to compress """ return torch.exp(x) / C def spectral_normalize_torch(magnitudes): return dynamic_range_compression_torch(magnitudes) def spectral_de_normalize_torch(magnitudes): return dynamic_range_decompression_torch(magnitudes) # Reusable banks mel_basis = {} hann_window = {} def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): """Convert waveform into Linear-frequency Linear-amplitude spectrogram. Args: y :: (B, T) - Audio waveforms n_fft sampling_rate hop_size win_size center Returns: :: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram """ # Validation if torch.min(y) < -1.07: logger.debug("min value is %s", str(torch.min(y))) if torch.max(y) > 1.07: logger.debug("max value is %s", str(torch.max(y))) # Window - Cache if needed global hann_window dtype_device = str(y.dtype) + "_" + str(y.device) wnsize_dtype_device = str(win_size) + "_" + dtype_device if wnsize_dtype_device not in hann_window: hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to( dtype=y.dtype, device=y.device ) # Padding 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 Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2) spec = torch.stft( y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], center=center, pad_mode="reflect", normalized=False, onesided=True, return_complex=False, ) # Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame) spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) return spec def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): # MelBasis - Cache if needed global mel_basis dtype_device = str(spec.dtype) + "_" + str(spec.device) fmax_dtype_device = str(fmax) + "_" + dtype_device if fmax_dtype_device 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[fmax_dtype_device] = torch.from_numpy(mel).to( dtype=spec.dtype, device=spec.device ) # Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame) melspec = torch.matmul(mel_basis[fmax_dtype_device], spec) melspec = spectral_normalize_torch(melspec) return melspec def mel_spectrogram_torch( y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False ): """Convert waveform into Mel-frequency Log-amplitude spectrogram. Args: y :: (B, T) - Waveforms Returns: melspec :: (B, Freq, Frame) - Mel-frequency Log-amplitude spectrogram """ # Linear-frequency Linear-amplitude spectrogram :: (B, T) -> (B, Freq, Frame) spec = spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center) # Mel-frequency Log-amplitude spectrogram :: (B, Freq, Frame) -> (B, Freq=num_mels, Frame) melspec = spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax) return melspec