import torch import torch.utils.data import librosa from librosa.filters import mel as librosa_mel_fn 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): 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 spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): if torch.min(y) < -1.1: print("min value is ", torch.min(y)) if torch.max(y) > 1.1: print("max value is ", torch.max(y)) 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 ) 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) 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, ) spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) return spec def spectrogram_torch_conv(y, n_fft, sampling_rate, hop_size, win_size, center=False): 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) y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') # ******************** original ************************# # y = y.squeeze(1) # spec1 = 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) # ******************** ConvSTFT ************************# freq_cutoff = n_fft // 2 + 1 fourier_basis = torch.view_as_real(torch.fft.fft(torch.eye(n_fft))) forward_basis = fourier_basis[:freq_cutoff].permute(2, 0, 1).reshape(-1, 1, fourier_basis.shape[1]) forward_basis = forward_basis * torch.as_tensor(librosa.util.pad_center(torch.hann_window(win_size), size=n_fft)).float() import torch.nn.functional as F # if center: # signal = F.pad(y[:, None, None, :], (n_fft // 2, n_fft // 2, 0, 0), mode = 'reflect').squeeze(1) assert center is False forward_transform_squared = F.conv1d(y, forward_basis.to(y.device), stride = hop_size) spec2 = torch.stack([forward_transform_squared[:, :freq_cutoff, :], forward_transform_squared[:, freq_cutoff:, :]], dim = -1) # ******************** Verification ************************# spec1 = torch.stft(y.squeeze(1), 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) assert torch.allclose(spec1, spec2, atol=1e-4) spec = torch.sqrt(spec2.pow(2).sum(-1) + 1e-6) return spec def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): 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 ) spec = torch.matmul(mel_basis[fmax_dtype_device], spec) spec = spectral_normalize_torch(spec) return spec def mel_spectrogram_torch( y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False ): global mel_basis, hann_window dtype_device = str(y.dtype) + "_" + str(y.device) fmax_dtype_device = str(fmax) + "_" + dtype_device wnsize_dtype_device = str(win_size) + "_" + 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=y.dtype, device=y.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 ) 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) 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, ) spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) spec = torch.matmul(mel_basis[fmax_dtype_device], spec) spec = spectral_normalize_torch(spec) return spec