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util.py
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util.py
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import librosa
import numpy as np
def specgram(audio,
n_fft=512,
hop_length=None,
mask=True,
log_mag=True,
re_im=False,
dphase=True,
mag_only=False):
"""Spectrogram using librosa.
Args:
audio: 1-D array of float32 sound samples.
n_fft: Size of the FFT.
hop_length: Stride of FFT. Defaults to n_fft/2.
mask: Mask the phase derivative by the magnitude.
log_mag: Use the logamplitude.
re_im: Output Real and Imag. instead of logMag and dPhase.
dphase: Use derivative of phase instead of phase.
mag_only: Don't return phase.
Returns:
specgram: [n_fft/2 + 1, audio.size / hop_length, 2]. The first channel is
the logamplitude and the second channel is the derivative of phase.
"""
if not hop_length:
hop_length = int(n_fft / 2.)
fft_config = dict(
n_fft=n_fft, win_length=n_fft, hop_length=hop_length, center=True)
spec = librosa.stft(audio, **fft_config)
if re_im:
re = spec.real[:, :, np.newaxis]
im = spec.imag[:, :, np.newaxis]
spec_real = np.concatenate((re, im), axis=2)
else:
mag, phase = librosa.core.magphase(spec)
phase_angle = np.angle(phase)
# Magnitudes, scaled 0-1
if log_mag:
mag = (librosa.logamplitude(
mag**2, amin=1e-13, top_db=120., ref_power=np.max) / 120.) + 1
else:
mag /= mag.max()
if dphase:
# Derivative of phase
phase_unwrapped = np.unwrap(phase_angle)
p = phase_unwrapped[:, 1:] - phase_unwrapped[:, :-1]
p = np.concatenate([phase_unwrapped[:, 0:1], p], axis=1) / np.pi
else:
# Normal phase
p = phase_angle / np.pi
# Mask the phase
if log_mag and mask:
p = mag * p
# Return Mag and Phase
p = p.astype(np.float32)[:, :, np.newaxis]
mag = mag.astype(np.float32)[:, :, np.newaxis]
if mag_only:
spec_real = mag[:, :, np.newaxis]
else:
spec_real = np.concatenate((mag, p), axis=2)
return spec_real
def inv_magphase(mag, phase_angle):
phase = np.cos(phase_angle) + 1.j * np.sin(phase_angle)
return mag * phase
def griffin_lim(mag, phase_angle, n_fft, hop, num_iters):
"""Iterative algorithm for phase retrival from a magnitude spectrogram.
Args:
mag: Magnitude spectrogram.
phase_angle: Initial condition for phase.
n_fft: Size of the FFT.
hop: Stride of FFT. Defaults to n_fft/2.
num_iters: Griffin-Lim iterations to perform.
Returns:
audio: 1-D array of float32 sound samples.
"""
fft_config = dict(n_fft=n_fft, win_length=n_fft, hop_length=hop, center=True)
ifft_config = dict(win_length=n_fft, hop_length=hop, center=True)
complex_specgram = inv_magphase(mag, phase_angle)
for i in range(num_iters):
audio = librosa.istft(complex_specgram, **ifft_config)
if i != num_iters - 1:
complex_specgram = librosa.stft(audio, **fft_config)
_, phase = librosa.magphase(complex_specgram)
phase_angle = np.angle(phase)
complex_specgram = inv_magphase(mag, phase_angle)
return audio
def ispecgram(spec,
n_fft=512,
hop_length=None,
mask=True,
log_mag=True,
re_im=False,
dphase=True,
mag_only=True,
num_iters=1000):
"""Inverse Spectrogram using librosa.
Args:
spec: 3-D specgram array [freqs, time, (mag_db, dphase)].
n_fft: Size of the FFT.
hop_length: Stride of FFT. Defaults to n_fft/2.
mask: Reverse the mask of the phase derivative by the magnitude.
log_mag: Use the logamplitude.
re_im: Output Real and Imag. instead of logMag and dPhase.
dphase: Use derivative of phase instead of phase.
mag_only: Specgram contains no phase.
num_iters: Number of griffin-lim iterations for mag_only.
Returns:
audio: 1-D array of sound samples. Peak normalized to 1.
"""
if not hop_length:
hop_length = n_fft // 2
ifft_config = dict(win_length=n_fft, hop_length=hop_length, center=True)
if mag_only:
mag = spec[:, :, 0]
phase_angle = np.pi * np.random.rand(*mag.shape)
elif re_im:
spec_real = spec[:, :, 0] + 1.j * spec[:, :, 1]
else:
mag, p = spec[:, :, 0], spec[:, :, 1]
if mask and log_mag:
p /= (mag + 1e-13 * np.random.randn(*mag.shape))
if dphase:
# Roll up phase
phase_angle = np.cumsum(p * np.pi, axis=1)
else:
phase_angle = p * np.pi
# Magnitudes
if log_mag:
mag = (mag - 1.0) * 120.0
mag = 10**(mag / 20.0)
phase = np.cos(phase_angle) + 1.j * np.sin(phase_angle)
spec_real = mag * phase
if mag_only:
audio = griffin_lim(
mag, phase_angle, n_fft, hop_length, num_iters=num_iters)
else:
audio = librosa.core.istft(spec_real, **ifft_config)
return np.squeeze(audio / audio.max())
def parse_speaker_info(speaker_info_path):
header = None
speaker_info = {}
with open(speaker_info_path) as f:
for line in f:
if header is None:
header = line
else:
parts = line.split(' ')
speaker_id = parts[0]
gender = parts[2]
speaker_info['p' + speaker_id] = gender
return speaker_info
if __name__ == '__main__':
x, fs = librosa.load("/hdd/cs599/VCTK-Corpus/wav48/p225/p225_039.wav")
spectro = specgram(x)
print(spectro.shape)
audio = ispecgram(spectro)
librosa.output.write_wav('/hdd/cs599/output/test.wav', x, fs)