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| import torch | |
| import numpy as np | |
| import torch.nn.functional as F | |
| from torch.autograd import Variable | |
| from scipy.signal import get_window | |
| import librosa.util as librosa_util | |
| from librosa.util import pad_center, tiny | |
| # from audio_processing import window_sumsquare | |
| def window_sumsquare(window, n_frames, hop_length=512, win_length=1024, | |
| n_fft=1024, 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 = librosa_util.normalize(win_sq, norm=norm)**2 | |
| win_sq = librosa_util.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=1024, hop_length=512, win_length=1024, | |
| 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, 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): | |
| 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, | |
| 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 # [batch_size, F(513), T(1251)] | |
| def inverse(self, magnitude, phase): | |
| recombine_magnitude_phase = torch.cat( | |
| [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1) | |
| inverse_transform = F.conv_transpose1d( | |
| recombine_magnitude_phase, | |
| 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.cuda() if magnitude.is_cuda else 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 #[batch_size, 1, sample_num] | |
| def forward(self, input_data): | |
| self.magnitude, self.phase = self.transform(input_data) | |
| reconstruction = self.inverse(self.magnitude, self.phase) | |
| return reconstruction | |
| if __name__ == '__main__': | |
| a = torch.randn(4, 320000) | |
| stft = STFT() | |
| mag, phase = stft.transform(a) | |
| # rec_a = stft.inverse(mag, phase) | |
| print(mag.shape) | |