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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))
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