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'''
Copied from espnet: https://github.com/espnet/espnet/blob/master/espnet/transform/spectrogram.py
'''
import librosa
import numpy as np
# for sing + acc.
def AWeightingLoudness_SingAcc(x, sr, n_fft, n_shift, win_length=None, window='hann', center=True, pad_mode='reflect'):
assert x.ndim == 1, 'The audio has %d channels, but so far we only support single-channel audios.' %(x.ndim)
# [Freq, Time]
mag_stft = np.abs(stft(x, n_fft, n_shift, win_length, window, center, pad_mode).T)
freq_axis = librosa.fft_frequencies(sr=sr, n_fft=n_fft)
perceptual_stft = librosa.perceptual_weighting(mag_stft**2, freq_axis, ref=1)
perceptual_loudness = np.log10(np.sum(np.power(10, perceptual_stft/10), axis=0)+1e-5)
return perceptual_loudness
# Extract Log-Scaled A-Weighting Loudness from Audio. Added by Xu Li.
def AWeightingLoudness(x, sr, n_fft, n_shift, win_length=None, window='hann', center=True, pad_mode='reflect'):
assert x.ndim == 1, 'The audio has %d channels, but so far we only support single-channel audios.' %(x.ndim)
# [Freq, Time]
mag_stft = np.abs(stft(x, n_fft, n_shift, win_length, window, center, pad_mode).T)
freq_axis = librosa.fft_frequencies(sr=sr, n_fft=n_fft)
perceptual_stft = librosa.perceptual_weighting(mag_stft**2, freq_axis, ref=1)
perceptual_loudness = np.log10(np.mean(np.power(10, perceptual_stft/10), axis=0)+1e-5)
return perceptual_loudness
def VoicedAreaDetection(x, sr, n_fft, n_shift, win_length=None, window='hann', center=True, pad_mode='reflect', hi_freq=1000, energy_thres=0.5):
assert x.ndim == 1, 'The audio has %d channels, but so far we only support single-channel audios.' %(x.ndim)
# [Freq, Time]
mag_stft = np.abs(stft(x, n_fft, n_shift, win_length, window, center, pad_mode).T)
freq_axis = librosa.fft_frequencies(sr=sr, n_fft=n_fft)
filtered_mag_stft = mag_stft[freq_axis <= hi_freq]
loudness = np.log10(np.mean(np.power(10, filtered_mag_stft/10), axis=0)+1e-5)
return loudness >= energy_thres
def stft(
x, n_fft, n_shift, win_length=None, window="hann", center=True, pad_mode="reflect"
):
# x: [Time, Channel]
if x.ndim == 1:
single_channel = True
# x: [Time] -> [Time, Channel]
x = x[:, None]
else:
single_channel = False
x = x.astype(np.float32)
# FIXME(kamo): librosa.stft can't use multi-channel?
# x: [Time, Channel, Freq]
x = np.stack(
[
librosa.stft(
x[:, ch],
n_fft=n_fft,
hop_length=n_shift,
win_length=win_length,
window=window,
center=center,
pad_mode=pad_mode,
).T
for ch in range(x.shape[1])
],
axis=1,
)
if single_channel:
# x: [Time, Channel, Freq] -> [Time, Freq]
x = x[:, 0]
return x
def istft(x, n_shift, win_length=None, window="hann", center=True):
# x: [Time, Channel, Freq]
if x.ndim == 2:
single_channel = True
# x: [Time, Freq] -> [Time, Channel, Freq]
x = x[:, None, :]
else:
single_channel = False
# x: [Time, Channel]
x = np.stack(
[
librosa.istft(
x[:, ch].T, # [Time, Freq] -> [Freq, Time]
hop_length=n_shift,
win_length=win_length,
window=window,
center=center,
)
for ch in range(x.shape[1])
],
axis=1,
)
if single_channel:
# x: [Time, Channel] -> [Time]
x = x[:, 0]
return x
def stft2logmelspectrogram(x_stft, fs, n_mels, n_fft, fmin=None, fmax=None, eps=1e-10):
# x_stft: (Time, Channel, Freq) or (Time, Freq)
fmin = 0 if fmin is None else fmin
fmax = fs / 2 if fmax is None else fmax
# spc: (Time, Channel, Freq) or (Time, Freq)
spc = np.abs(x_stft)
# mel_basis: (Mel_freq, Freq)
mel_basis = librosa.filters.mel(sr=fs, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
# lmspc: (Time, Channel, Mel_freq) or (Time, Mel_freq)
lmspc = np.log10(np.maximum(eps, np.dot(spc, mel_basis.T)))
return lmspc
def spectrogram(x, n_fft, n_shift, win_length=None, window="hann"):
# x: (Time, Channel) -> spc: (Time, Channel, Freq)
spc = np.abs(stft(x, n_fft, n_shift, win_length, window=window))
return spc
def logmelspectrogram(
x,
fs,
n_mels,
n_fft,
n_shift,
win_length=None,
window="hann",
fmin=None,
fmax=None,
eps=1e-10,
pad_mode="reflect",
):
# stft: (Time, Channel, Freq) or (Time, Freq)
x_stft = stft(
x,
n_fft=n_fft,
n_shift=n_shift,
win_length=win_length,
window=window,
pad_mode=pad_mode,
)
return stft2logmelspectrogram(
x_stft, fs=fs, n_mels=n_mels, n_fft=n_fft, fmin=fmin, fmax=fmax, eps=eps
)
class Spectrogram(object):
def __init__(self, n_fft, n_shift, win_length=None, window="hann"):
self.n_fft = n_fft
self.n_shift = n_shift
self.win_length = win_length
self.window = window
def __repr__(self):
return (
"{name}(n_fft={n_fft}, n_shift={n_shift}, "
"win_length={win_length}, window={window})".format(
name=self.__class__.__name__,
n_fft=self.n_fft,
n_shift=self.n_shift,
win_length=self.win_length,
window=self.window,
)
)
def __call__(self, x):
return spectrogram(
x,
n_fft=self.n_fft,
n_shift=self.n_shift,
win_length=self.win_length,
window=self.window,
)
class LogMelSpectrogram(object):
def __init__(
self,
fs,
n_mels,
n_fft,
n_shift,
win_length=None,
window="hann",
fmin=None,
fmax=None,
eps=1e-10,
):
self.fs = fs
self.n_mels = n_mels
self.n_fft = n_fft
self.n_shift = n_shift
self.win_length = win_length
self.window = window
self.fmin = fmin
self.fmax = fmax
self.eps = eps
def __repr__(self):
return (
"{name}(fs={fs}, n_mels={n_mels}, n_fft={n_fft}, "
"n_shift={n_shift}, win_length={win_length}, window={window}, "
"fmin={fmin}, fmax={fmax}, eps={eps}))".format(
name=self.__class__.__name__,
fs=self.fs,
n_mels=self.n_mels,
n_fft=self.n_fft,
n_shift=self.n_shift,
win_length=self.win_length,
window=self.window,
fmin=self.fmin,
fmax=self.fmax,
eps=self.eps,
)
)
def __call__(self, x):
return logmelspectrogram(
x,
fs=self.fs,
n_mels=self.n_mels,
n_fft=self.n_fft,
n_shift=self.n_shift,
win_length=self.win_length,
window=self.window,
)
class Stft2LogMelSpectrogram(object):
def __init__(self, fs, n_mels, n_fft, fmin=None, fmax=None, eps=1e-10):
self.fs = fs
self.n_mels = n_mels
self.n_fft = n_fft
self.fmin = fmin
self.fmax = fmax
self.eps = eps
def __repr__(self):
return (
"{name}(fs={fs}, n_mels={n_mels}, n_fft={n_fft}, "
"fmin={fmin}, fmax={fmax}, eps={eps}))".format(
name=self.__class__.__name__,
fs=self.fs,
n_mels=self.n_mels,
n_fft=self.n_fft,
fmin=self.fmin,
fmax=self.fmax,
eps=self.eps,
)
)
def __call__(self, x):
return stft2logmelspectrogram(
x,
fs=self.fs,
n_mels=self.n_mels,
n_fft=self.n_fft,
fmin=self.fmin,
fmax=self.fmax,
)
class Stft(object):
def __init__(
self,
n_fft,
n_shift,
win_length=None,
window="hann",
center=True,
pad_mode="reflect",
):
self.n_fft = n_fft
self.n_shift = n_shift
self.win_length = win_length
self.window = window
self.center = center
self.pad_mode = pad_mode
def __repr__(self):
return (
"{name}(n_fft={n_fft}, n_shift={n_shift}, "
"win_length={win_length}, window={window},"
"center={center}, pad_mode={pad_mode})".format(
name=self.__class__.__name__,
n_fft=self.n_fft,
n_shift=self.n_shift,
win_length=self.win_length,
window=self.window,
center=self.center,
pad_mode=self.pad_mode,
)
)
def __call__(self, x):
return stft(
x,
self.n_fft,
self.n_shift,
win_length=self.win_length,
window=self.window,
center=self.center,
pad_mode=self.pad_mode,
)
class IStft(object):
def __init__(self, n_shift, win_length=None, window="hann", center=True):
self.n_shift = n_shift
self.win_length = win_length
self.window = window
self.center = center
def __repr__(self):
return (
"{name}(n_shift={n_shift}, "
"win_length={win_length}, window={window},"
"center={center})".format(
name=self.__class__.__name__,
n_shift=self.n_shift,
win_length=self.win_length,
window=self.window,
center=self.center,
)
)
def __call__(self, x):
return istft(
x,
self.n_shift,
win_length=self.win_length,
window=self.window,
center=self.center,
)
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