Spaces:
Runtime error
Runtime error
| import librosa | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| def _stft(y, hop_size, win_size, fft_size): | |
| return librosa.stft(y=y, n_fft=fft_size, hop_length=hop_size, win_length=win_size, pad_mode='constant') | |
| def _istft(y, hop_size, win_size): | |
| return librosa.istft(y, hop_length=hop_size, win_length=win_size) | |
| def griffin_lim(S, hop_size, win_size, fft_size, angles=None, n_iters=30): | |
| angles = np.exp(2j * np.pi * np.random.rand(*S.shape)) if angles is None else angles | |
| S_complex = np.abs(S).astype(np.complex) | |
| y = _istft(S_complex * angles, hop_size, win_size) | |
| for i in range(n_iters): | |
| angles = np.exp(1j * np.angle(_stft(y, hop_size, win_size, fft_size))) | |
| y = _istft(S_complex * angles, hop_size, win_size) | |
| return y | |
| def istft(amp, ang, hop_size, win_size, fft_size, pad=False, window=None): | |
| spec = amp * torch.exp(1j * ang) | |
| spec_r = spec.real | |
| spec_i = spec.imag | |
| spec = torch.stack([spec_r, spec_i], -1) | |
| if window is None: | |
| window = torch.hann_window(win_size).to(amp.device) | |
| if pad: | |
| spec = F.pad(spec, [0, 0, 0, 1], mode='reflect') | |
| wav = torch.istft(spec, fft_size, hop_size, win_size) | |
| return wav | |
| def griffin_lim_torch(S, hop_size, win_size, fft_size, angles=None, n_iters=30): | |
| """ | |
| Examples: | |
| >>> x_stft = librosa.stft(wav, n_fft=fft_size, hop_length=hop_size, win_length=win_length, pad_mode="constant") | |
| >>> x_stft = x_stft[None, ...] | |
| >>> amp = np.abs(x_stft) | |
| >>> angle_init = np.exp(2j * np.pi * np.random.rand(*x_stft.shape)) | |
| >>> amp = torch.FloatTensor(amp) | |
| >>> wav = griffin_lim_torch(amp, angle_init, hparams) | |
| :param amp: [B, n_fft, T] | |
| :param ang: [B, n_fft, T] | |
| :return: [B, T_wav] | |
| """ | |
| angles = torch.exp(2j * np.pi * torch.rand(*S.shape)) if angles is None else angles | |
| window = torch.hann_window(win_size).to(S.device) | |
| y = istft(S, angles, hop_size, win_size, fft_size, window=window) | |
| for i in range(n_iters): | |
| x_stft = torch.stft(y, fft_size, hop_size, win_size, window) | |
| x_stft = x_stft[..., 0] + 1j * x_stft[..., 1] | |
| angles = torch.angle(x_stft) | |
| y = istft(S, angles, hop_size, win_size, fft_size, window=window) | |
| return y | |
| # Conversions | |
| _mel_basis = None | |
| _inv_mel_basis = None | |
| def _build_mel_basis(audio_sample_rate, fft_size, audio_num_mel_bins, fmin, fmax): | |
| assert fmax <= audio_sample_rate // 2 | |
| return librosa.filters.mel(audio_sample_rate, fft_size, n_mels=audio_num_mel_bins, fmin=fmin, fmax=fmax) | |
| def _linear_to_mel(spectogram, audio_sample_rate, fft_size, audio_num_mel_bins, fmin, fmax): | |
| global _mel_basis | |
| if _mel_basis is None: | |
| _mel_basis = _build_mel_basis(audio_sample_rate, fft_size, audio_num_mel_bins, fmin, fmax) | |
| return np.dot(_mel_basis, spectogram) | |
| def _mel_to_linear(mel_spectrogram, audio_sample_rate, fft_size, audio_num_mel_bins, fmin, fmax): | |
| global _inv_mel_basis | |
| if _inv_mel_basis is None: | |
| _inv_mel_basis = np.linalg.pinv(_build_mel_basis(audio_sample_rate, fft_size, audio_num_mel_bins, fmin, fmax)) | |
| return np.maximum(1e-10, np.dot(_inv_mel_basis, mel_spectrogram)) | |