import torch import torch.nn.functional as F import numpy as np from scipy.signal import get_window from librosa.util import pad_center, tiny, normalize, pad_center from librosa.filters import mel as librosa_mel_fn def dynamic_range_compression(x, normalize_fun=torch.log, C=1, clip_val=1e-5): """ Parameters ---------- C: compression factor """ return normalize_fun(torch.clamp(x, min=clip_val) * C) def dynamic_range_decompression(x, C=1): """ Parameters ---------- C: compression factor used to compress """ return torch.exp(x) / C def window_sumsquare( window, n_frames, hop_length, win_length, n_fft, dtype=np.float32, norm=None, ): """ # from librosa 0.6 Compute the sum-square envelope of a window function at a given hop length. This is used to estimate modulation effects induced by windowing observations in short-time fourier transforms. Parameters ---------- window : string, tuple, number, callable, or list-like Window specification, as in `get_window` n_frames : int > 0 The number of analysis frames hop_length : int > 0 The number of samples to advance between frames win_length : [optional] The length of the window function. By default, this matches `n_fft`. n_fft : int > 0 The length of each analysis frame. dtype : np.dtype The data type of the output Returns ------- wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))` The sum-squared envelope of the window function """ if win_length is None: win_length = n_fft n = n_fft + hop_length * (n_frames - 1) x = np.zeros(n, dtype=dtype) # Compute the squared window at the desired length win_sq = get_window(window, win_length, fftbins=True) win_sq = normalize(win_sq, norm=norm) ** 2 win_sq = pad_center(win_sq, n_fft) # Fill the envelope for i in range(n_frames): sample = i * hop_length x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))] return x class STFT(torch.nn.Module): """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft""" def __init__(self, filter_length, hop_length, win_length, window="hann"): super(STFT, self).__init__() self.filter_length = filter_length self.hop_length = hop_length self.win_length = win_length self.window = window self.forward_transform = None scale = self.filter_length / self.hop_length fourier_basis = np.fft.fft(np.eye(self.filter_length)) cutoff = int((self.filter_length / 2 + 1)) fourier_basis = np.vstack( [np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])] ) forward_basis = torch.FloatTensor(fourier_basis[:, None, :]) inverse_basis = torch.FloatTensor( np.linalg.pinv(scale * fourier_basis).T[:, None, :] ) if window is not None: assert filter_length >= win_length # get window and zero center pad it to filter_length fft_window = get_window(window, win_length, fftbins=True) fft_window = pad_center(fft_window, size=filter_length) fft_window = torch.from_numpy(fft_window).float() # window the bases forward_basis *= fft_window inverse_basis *= fft_window self.register_buffer("forward_basis", forward_basis.float()) self.register_buffer("inverse_basis", inverse_basis.float()) def transform(self, input_data): device = self.forward_basis.device input_data = input_data.to(device) num_batches = input_data.size(0) num_samples = input_data.size(1) self.num_samples = num_samples # similar to librosa, reflect-pad the input input_data = input_data.view(num_batches, 1, num_samples) input_data = F.pad( input_data.unsqueeze(1), (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0), mode="reflect", ) input_data = input_data.squeeze(1) forward_transform = F.conv1d( input_data, torch.autograd.Variable(self.forward_basis, requires_grad=False), stride=self.hop_length, padding=0, ) cutoff = int((self.filter_length / 2) + 1) real_part = forward_transform[:, :cutoff, :] imag_part = forward_transform[:, cutoff:, :] magnitude = torch.sqrt(real_part**2 + imag_part**2) phase = torch.autograd.Variable(torch.atan2(imag_part.data, real_part.data)) return magnitude, phase def inverse(self, magnitude, phase): device = self.forward_basis.device magnitude, phase = magnitude.to(device), phase.to(device) recombine_magnitude_phase = torch.cat( [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1 ) inverse_transform = F.conv_transpose1d( recombine_magnitude_phase, torch.autograd.Variable(self.inverse_basis, requires_grad=False), stride=self.hop_length, padding=0, ) if self.window is not None: window_sum = window_sumsquare( self.window, magnitude.size(-1), hop_length=self.hop_length, win_length=self.win_length, n_fft=self.filter_length, dtype=np.float32, ) # remove modulation effects approx_nonzero_indices = torch.from_numpy( np.where(window_sum > tiny(window_sum))[0] ) window_sum = torch.autograd.Variable( torch.from_numpy(window_sum), requires_grad=False ) window_sum = window_sum inverse_transform[:, :, approx_nonzero_indices] /= window_sum[ approx_nonzero_indices ] # scale by hop ratio inverse_transform *= float(self.filter_length) / self.hop_length inverse_transform = inverse_transform[:, :, int(self.filter_length / 2) :] inverse_transform = inverse_transform[:, :, : -int(self.filter_length / 2) :] return inverse_transform def forward(self, input_data): self.magnitude, self.phase = self.transform(input_data) reconstruction = self.inverse(self.magnitude, self.phase) return reconstruction class TacotronSTFT(torch.nn.Module): def __init__( self, filter_length, hop_length, win_length, n_mel_channels, sampling_rate, mel_fmin, mel_fmax, ): super(TacotronSTFT, self).__init__() self.n_mel_channels = n_mel_channels self.sampling_rate = sampling_rate self.stft_fn = STFT(filter_length, hop_length, win_length) mel_basis = librosa_mel_fn( sr=sampling_rate, n_fft=filter_length, n_mels=n_mel_channels, fmin=mel_fmin, fmax=mel_fmax ) mel_basis = torch.from_numpy(mel_basis).float() self.register_buffer("mel_basis", mel_basis) def spectral_normalize(self, magnitudes, normalize_fun): output = dynamic_range_compression(magnitudes, normalize_fun) return output def spectral_de_normalize(self, magnitudes): output = dynamic_range_decompression(magnitudes) return output def mel_spectrogram(self, y, normalize_fun=torch.log): """Computes mel-spectrograms from a batch of waves PARAMS ------ y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1] RETURNS ------- mel_output: torch.FloatTensor of shape (B, n_mel_channels, T) """ assert torch.min(y.data) >= -1, torch.min(y.data) assert torch.max(y.data) <= 1, torch.max(y.data) magnitudes, phases = self.stft_fn.transform(y) magnitudes = magnitudes.data mel_output = torch.matmul(self.mel_basis, magnitudes) mel_output = self.spectral_normalize(mel_output, normalize_fun) energy = torch.norm(magnitudes, dim=1) log_magnitudes = self.spectral_normalize(magnitudes, normalize_fun) return mel_output, log_magnitudes, energy