# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from torchaudio(https://github.com/pytorch/audio)
import math
from typing import Tuple
import paddle
from paddle import Tensor
from ..functional import create_dct
from ..functional.window import get_window
__all__ = [
'spectrogram',
'fbank',
'mfcc',
]
# window types
HANNING = 'hann'
HAMMING = 'hamming'
POVEY = 'povey'
RECTANGULAR = 'rect'
BLACKMAN = 'blackman'
def _get_epsilon(dtype):
return paddle.to_tensor(1e-07, dtype=dtype)
def _next_power_of_2(x: int) -> int:
return 1 if x == 0 else 2**(x - 1).bit_length()
def _get_strided(waveform: Tensor,
window_size: int,
window_shift: int,
snip_edges: bool) -> Tensor:
assert waveform.dim() == 1
num_samples = waveform.shape[0]
if snip_edges:
if num_samples < window_size:
return paddle.empty((0, 0), dtype=waveform.dtype)
else:
m = 1 + (num_samples - window_size) // window_shift
else:
reversed_waveform = paddle.flip(waveform, [0])
m = (num_samples + (window_shift // 2)) // window_shift
pad = window_size // 2 - window_shift // 2
pad_right = reversed_waveform
if pad > 0:
pad_left = reversed_waveform[-pad:]
waveform = paddle.concat((pad_left, waveform, pad_right), axis=0)
else:
waveform = paddle.concat((waveform[-pad:], pad_right), axis=0)
return paddle.signal.frame(waveform, window_size, window_shift)[:, :m].T
def _feature_window_function(
window_type: str,
window_size: int,
blackman_coeff: float,
dtype: int, ) -> Tensor:
if window_type == HANNING:
return get_window('hann', window_size, fftbins=False, dtype=dtype)
elif window_type == HAMMING:
return get_window('hamming', window_size, fftbins=False, dtype=dtype)
elif window_type == POVEY:
return get_window(
'hann', window_size, fftbins=False, dtype=dtype).pow(0.85)
elif window_type == RECTANGULAR:
return paddle.ones([window_size], dtype=dtype)
elif window_type == BLACKMAN:
a = 2 * math.pi / (window_size - 1)
window_function = paddle.arange(window_size, dtype=dtype)
return (blackman_coeff - 0.5 * paddle.cos(a * window_function) +
(0.5 - blackman_coeff) * paddle.cos(2 * a * window_function)
).astype(dtype)
else:
raise Exception('Invalid window type ' + window_type)
def _get_log_energy(strided_input: Tensor, epsilon: Tensor,
energy_floor: float) -> Tensor:
log_energy = paddle.maximum(strided_input.pow(2).sum(1), epsilon).log()
if energy_floor == 0.0:
return log_energy
return paddle.maximum(
log_energy,
paddle.to_tensor(math.log(energy_floor), dtype=strided_input.dtype))
def _get_waveform_and_window_properties(
waveform: Tensor,
channel: int,
sr: int,
frame_shift: float,
frame_length: float,
round_to_power_of_two: bool,
preemphasis_coefficient: float) -> Tuple[Tensor, int, int, int]:
channel = max(channel, 0)
assert channel < waveform.shape[0], (
'Invalid channel {} for size {}'.format(channel, waveform.shape[0]))
waveform = waveform[channel, :] # size (n)
window_shift = int(
sr * frame_shift *
0.001) # pass frame_shift and frame_length in milliseconds
window_size = int(sr * frame_length * 0.001)
padded_window_size = _next_power_of_2(
window_size) if round_to_power_of_two else window_size
assert 2 <= window_size <= len(waveform), (
'choose a window size {} that is [2, {}]'.format(window_size,
len(waveform)))
assert 0 < window_shift, '`window_shift` must be greater than 0'
assert padded_window_size % 2 == 0, 'the padded `window_size` must be divisible by two.' \
' use `round_to_power_of_two` or change `frame_length`'
assert 0. <= preemphasis_coefficient <= 1.0, '`preemphasis_coefficient` must be between [0,1]'
assert sr > 0, '`sr` must be greater than zero'
return waveform, window_shift, window_size, padded_window_size
def _get_window(waveform: Tensor,
padded_window_size: int,
window_size: int,
window_shift: int,
window_type: str,
blackman_coeff: float,
snip_edges: bool,
raw_energy: bool,
energy_floor: float,
dither: float,
remove_dc_offset: bool,
preemphasis_coefficient: float) -> Tuple[Tensor, Tensor]:
dtype = waveform.dtype
epsilon = _get_epsilon(dtype)
# (m, window_size)
strided_input = _get_strided(waveform, window_size, window_shift,
snip_edges)
if dither != 0.0:
x = paddle.maximum(epsilon,
paddle.rand(strided_input.shape, dtype=dtype))
rand_gauss = paddle.sqrt(-2 * x.log()) * paddle.cos(2 * math.pi * x)
strided_input = strided_input + rand_gauss * dither
if remove_dc_offset:
row_means = paddle.mean(strided_input, axis=1).unsqueeze(1) # (m, 1)
strided_input = strided_input - row_means
if raw_energy:
signal_log_energy = _get_log_energy(strided_input, epsilon,
energy_floor) # (m)
if preemphasis_coefficient != 0.0:
offset_strided_input = paddle.nn.functional.pad(
strided_input.unsqueeze(0), (1, 0),
data_format='NCL',
mode='replicate').squeeze(0) # (m, window_size + 1)
strided_input = strided_input - preemphasis_coefficient * offset_strided_input[:, :
-1]
window_function = _feature_window_function(
window_type, window_size, blackman_coeff,
dtype).unsqueeze(0) # (1, window_size)
strided_input = strided_input * window_function # (m, window_size)
# (m, padded_window_size)
if padded_window_size != window_size:
padding_right = padded_window_size - window_size
strided_input = paddle.nn.functional.pad(
strided_input.unsqueeze(0), (0, padding_right),
data_format='NCL',
mode='constant',
value=0).squeeze(0)
if not raw_energy:
signal_log_energy = _get_log_energy(strided_input, epsilon,
energy_floor) # size (m)
return strided_input, signal_log_energy
def _subtract_column_mean(tensor: Tensor, subtract_mean: bool) -> Tensor:
if subtract_mean:
col_means = paddle.mean(tensor, axis=0).unsqueeze(0)
tensor = tensor - col_means
return tensor
def spectrogram(waveform: Tensor,
blackman_coeff: float=0.42,
channel: int=-1,
dither: float=0.0,
energy_floor: float=1.0,
frame_length: float=25.0,
frame_shift: float=10.0,
preemphasis_coefficient: float=0.97,
raw_energy: bool=True,
remove_dc_offset: bool=True,
round_to_power_of_two: bool=True,
sr: int=16000,
snip_edges: bool=True,
subtract_mean: bool=False,
window_type: str=POVEY) -> Tensor:
"""Compute and return a spectrogram from a waveform. The output is identical to Kaldi's.
Args:
waveform (Tensor): A waveform tensor with shape `(C, T)`.
blackman_coeff (float, optional): Coefficient for Blackman window.. Defaults to 0.42.
channel (int, optional): Select the channel of waveform. Defaults to -1.
dither (float, optional): Dithering constant . Defaults to 0.0.
energy_floor (float, optional): Floor on energy of the output Spectrogram. Defaults to 1.0.
frame_length (float, optional): Frame length in milliseconds. Defaults to 25.0.
frame_shift (float, optional): Shift between adjacent frames in milliseconds. Defaults to 10.0.
preemphasis_coefficient (float, optional): Preemphasis coefficient for input waveform. Defaults to 0.97.
raw_energy (bool, optional): Whether to compute before preemphasis and windowing. Defaults to True.
remove_dc_offset (bool, optional): Whether to subtract mean from waveform on frames. Defaults to True.
round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
to FFT. Defaults to True.
sr (int, optional): Sample rate of input waveform. Defaults to 16000.
snip_edges (bool, optional): Drop samples in the end of waveform that cann't fit a singal frame when it
is set True. Otherwise performs reflect padding to the end of waveform. Defaults to True.
subtract_mean (bool, optional): Whether to subtract mean of feature files. Defaults to False.
window_type (str, optional): Choose type of window for FFT computation. Defaults to POVEY.
Returns:
Tensor: A spectrogram tensor with shape `(m, padded_window_size // 2 + 1)` where m is the number of frames
depends on frame_length and frame_shift.
"""
dtype = waveform.dtype
epsilon = _get_epsilon(dtype)
waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties(
waveform, channel, sr, frame_shift, frame_length, round_to_power_of_two,
preemphasis_coefficient)
strided_input, signal_log_energy = _get_window(
waveform, padded_window_size, window_size, window_shift, window_type,
blackman_coeff, snip_edges, raw_energy, energy_floor, dither,
remove_dc_offset, preemphasis_coefficient)
# (m, padded_window_size // 2 + 1, 2)
fft = paddle.fft.rfft(strided_input)
power_spectrum = paddle.maximum(
fft.abs().pow(2.), epsilon).log() # (m, padded_window_size // 2 + 1)
power_spectrum[:, 0] = signal_log_energy
power_spectrum = _subtract_column_mean(power_spectrum, subtract_mean)
return power_spectrum
def _inverse_mel_scale_scalar(mel_freq: float) -> float:
return 700.0 * (math.exp(mel_freq / 1127.0) - 1.0)
def _inverse_mel_scale(mel_freq: Tensor) -> Tensor:
return 700.0 * ((mel_freq / 1127.0).exp() - 1.0)
def _mel_scale_scalar(freq: float) -> float:
return 1127.0 * math.log(1.0 + freq / 700.0)
def _mel_scale(freq: Tensor) -> Tensor:
return 1127.0 * (1.0 + freq / 700.0).log()
def _vtln_warp_freq(vtln_low_cutoff: float,
vtln_high_cutoff: float,
low_freq: float,
high_freq: float,
vtln_warp_factor: float,
freq: Tensor) -> Tensor:
assert vtln_low_cutoff > low_freq, 'be sure to set the vtln_low option higher than low_freq'
assert vtln_high_cutoff < high_freq, 'be sure to set the vtln_high option lower than high_freq [or negative]'
l = vtln_low_cutoff * max(1.0, vtln_warp_factor)
h = vtln_high_cutoff * min(1.0, vtln_warp_factor)
scale = 1.0 / vtln_warp_factor
Fl = scale * l
Fh = scale * h
assert l > low_freq and h < high_freq
scale_left = (Fl - low_freq) / (l - low_freq)
scale_right = (high_freq - Fh) / (high_freq - h)
res = paddle.empty_like(freq)
outside_low_high_freq = paddle.less_than(freq, paddle.to_tensor(low_freq)) \
| paddle.greater_than(freq, paddle.to_tensor(high_freq))
before_l = paddle.less_than(freq, paddle.to_tensor(l))
before_h = paddle.less_than(freq, paddle.to_tensor(h))
after_h = paddle.greater_equal(freq, paddle.to_tensor(h))
res[after_h] = high_freq + scale_right * (freq[after_h] - high_freq)
res[before_h] = scale * freq[before_h]
res[before_l] = low_freq + scale_left * (freq[before_l] - low_freq)
res[outside_low_high_freq] = freq[outside_low_high_freq]
return res
def _vtln_warp_mel_freq(vtln_low_cutoff: float,
vtln_high_cutoff: float,
low_freq,
high_freq: float,
vtln_warp_factor: float,
mel_freq: Tensor) -> Tensor:
return _mel_scale(
_vtln_warp_freq(vtln_low_cutoff, vtln_high_cutoff, low_freq, high_freq,
vtln_warp_factor, _inverse_mel_scale(mel_freq)))
def _get_mel_banks(num_bins: int,
window_length_padded: int,
sample_freq: float,
low_freq: float,
high_freq: float,
vtln_low: float,
vtln_high: float,
vtln_warp_factor: float) -> Tuple[Tensor, Tensor]:
assert num_bins > 3, 'Must have at least 3 mel bins'
assert window_length_padded % 2 == 0
num_fft_bins = window_length_padded / 2
nyquist = 0.5 * sample_freq
if high_freq <= 0.0:
high_freq += nyquist
assert (0.0 <= low_freq < nyquist) and (0.0 < high_freq <= nyquist) and (low_freq < high_freq), \
('Bad values in options: low-freq {} and high-freq {} vs. nyquist {}'.format(low_freq, high_freq, nyquist))
fft_bin_width = sample_freq / window_length_padded
mel_low_freq = _mel_scale_scalar(low_freq)
mel_high_freq = _mel_scale_scalar(high_freq)
mel_freq_delta = (mel_high_freq - mel_low_freq) / (num_bins + 1)
if vtln_high < 0.0:
vtln_high += nyquist
assert vtln_warp_factor == 1.0 or ((low_freq < vtln_low < high_freq) and
(0.0 < vtln_high < high_freq) and (vtln_low < vtln_high)), \
('Bad values in options: vtln-low {} and vtln-high {}, versus '
'low-freq {} and high-freq {}'.format(vtln_low, vtln_high, low_freq, high_freq))
bin = paddle.arange(num_bins).unsqueeze(1)
left_mel = mel_low_freq + bin * mel_freq_delta # (num_bins, 1)
center_mel = mel_low_freq + (bin + 1.0) * mel_freq_delta # (num_bins, 1)
right_mel = mel_low_freq + (bin + 2.0) * mel_freq_delta # (num_bins, 1)
if vtln_warp_factor != 1.0:
left_mel = _vtln_warp_mel_freq(vtln_low, vtln_high, low_freq, high_freq,
vtln_warp_factor, left_mel)
center_mel = _vtln_warp_mel_freq(vtln_low, vtln_high, low_freq,
high_freq, vtln_warp_factor,
center_mel)
right_mel = _vtln_warp_mel_freq(vtln_low, vtln_high, low_freq,
high_freq, vtln_warp_factor, right_mel)
center_freqs = _inverse_mel_scale(center_mel) # (num_bins)
# (1, num_fft_bins)
mel = _mel_scale(fft_bin_width * paddle.arange(num_fft_bins)).unsqueeze(0)
# (num_bins, num_fft_bins)
up_slope = (mel - left_mel) / (center_mel - left_mel)
down_slope = (right_mel - mel) / (right_mel - center_mel)
if vtln_warp_factor == 1.0:
bins = paddle.maximum(
paddle.zeros([1]), paddle.minimum(up_slope, down_slope))
else:
bins = paddle.zeros_like(up_slope)
up_idx = paddle.greater_than(mel, left_mel) & paddle.less_than(
mel, center_mel)
down_idx = paddle.greater_than(mel, center_mel) & paddle.less_than(
mel, right_mel)
bins[up_idx] = up_slope[up_idx]
bins[down_idx] = down_slope[down_idx]
return bins, center_freqs
def fbank(waveform: Tensor,
blackman_coeff: float=0.42,
channel: int=-1,
dither: float=0.0,
energy_floor: float=1.0,
frame_length: float=25.0,
frame_shift: float=10.0,
high_freq: float=0.0,
htk_compat: bool=False,
low_freq: float=20.0,
n_mels: int=23,
preemphasis_coefficient: float=0.97,
raw_energy: bool=True,
remove_dc_offset: bool=True,
round_to_power_of_two: bool=True,
sr: int=16000,
snip_edges: bool=True,
subtract_mean: bool=False,
use_energy: bool=False,
use_log_fbank: bool=True,
use_power: bool=True,
vtln_high: float=-500.0,
vtln_low: float=100.0,
vtln_warp: float=1.0,
window_type: str=POVEY) -> Tensor:
"""Compute and return filter banks from a waveform. The output is identical to Kaldi's.
Args:
waveform (Tensor): A waveform tensor with shape `(C, T)`.
blackman_coeff (float, optional): Coefficient for Blackman window.. Defaults to 0.42.
channel (int, optional): Select the channel of waveform. Defaults to -1.
dither (float, optional): Dithering constant . Defaults to 0.0.
energy_floor (float, optional): Floor on energy of the output Spectrogram. Defaults to 1.0.
frame_length (float, optional): Frame length in milliseconds. Defaults to 25.0.
frame_shift (float, optional): Shift between adjacent frames in milliseconds. Defaults to 10.0.
high_freq (float, optional): The upper cut-off frequency. Defaults to 0.0.
htk_compat (bool, optional): Put energy to the last when it is set True. Defaults to False.
low_freq (float, optional): The lower cut-off frequency. Defaults to 20.0.
n_mels (int, optional): Number of output mel bins. Defaults to 23.
preemphasis_coefficient (float, optional): Preemphasis coefficient for input waveform. Defaults to 0.97.
raw_energy (bool, optional): Whether to compute before preemphasis and windowing. Defaults to True.
remove_dc_offset (bool, optional): Whether to subtract mean from waveform on frames. Defaults to True.
round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
to FFT. Defaults to True.
sr (int, optional): Sample rate of input waveform. Defaults to 16000.
snip_edges (bool, optional): Drop samples in the end of waveform that cann't fit a singal frame when it
is set True. Otherwise performs reflect padding to the end of waveform. Defaults to True.
subtract_mean (bool, optional): Whether to subtract mean of feature files. Defaults to False.
use_energy (bool, optional): Add an dimension with energy of spectrogram to the output. Defaults to False.
use_log_fbank (bool, optional): Return log fbank when it is set True. Defaults to True.
use_power (bool, optional): Whether to use power instead of magnitude. Defaults to True.
vtln_high (float, optional): High inflection point in piecewise linear VTLN warping function. Defaults to -500.0.
vtln_low (float, optional): Low inflection point in piecewise linear VTLN warping function. Defaults to 100.0.
vtln_warp (float, optional): Vtln warp factor. Defaults to 1.0.
window_type (str, optional): Choose type of window for FFT computation. Defaults to POVEY.
Returns:
Tensor: A filter banks tensor with shape `(m, n_mels)`.
"""
dtype = waveform.dtype
waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties(
waveform, channel, sr, frame_shift, frame_length, round_to_power_of_two,
preemphasis_coefficient)
strided_input, signal_log_energy = _get_window(
waveform, padded_window_size, window_size, window_shift, window_type,
blackman_coeff, snip_edges, raw_energy, energy_floor, dither,
remove_dc_offset, preemphasis_coefficient)
# (m, padded_window_size // 2 + 1)
spectrum = paddle.fft.rfft(strided_input).abs()
if use_power:
spectrum = spectrum.pow(2.)
# (n_mels, padded_window_size // 2)
mel_energies, _ = _get_mel_banks(n_mels, padded_window_size, sr, low_freq,
high_freq, vtln_low, vtln_high, vtln_warp)
mel_energies = mel_energies.astype(dtype)
# (n_mels, padded_window_size // 2 + 1)
mel_energies = paddle.nn.functional.pad(
mel_energies.unsqueeze(0), (0, 1),
data_format='NCL',
mode='constant',
value=0).squeeze(0)
# (m, n_mels)
mel_energies = paddle.mm(spectrum, mel_energies.T)
if use_log_fbank:
mel_energies = paddle.maximum(mel_energies, _get_epsilon(dtype)).log()
if use_energy:
signal_log_energy = signal_log_energy.unsqueeze(1)
if htk_compat:
mel_energies = paddle.concat(
(mel_energies, signal_log_energy), axis=1)
else:
mel_energies = paddle.concat(
(signal_log_energy, mel_energies), axis=1)
# (m, n_mels + 1)
mel_energies = _subtract_column_mean(mel_energies, subtract_mean)
return mel_energies
def _get_dct_matrix(n_mfcc: int, n_mels: int) -> Tensor:
dct_matrix = create_dct(n_mels, n_mels, 'ortho')
dct_matrix[:, 0] = math.sqrt(1 / float(n_mels))
dct_matrix = dct_matrix[:, :n_mfcc] # (n_mels, n_mfcc)
return dct_matrix
def _get_lifter_coeffs(n_mfcc: int, cepstral_lifter: float) -> Tensor:
i = paddle.arange(n_mfcc)
return 1.0 + 0.5 * cepstral_lifter * paddle.sin(math.pi * i /
cepstral_lifter)
def mfcc(waveform: Tensor,
blackman_coeff: float=0.42,
cepstral_lifter: float=22.0,
channel: int=-1,
dither: float=0.0,
energy_floor: float=1.0,
frame_length: float=25.0,
frame_shift: float=10.0,
high_freq: float=0.0,
htk_compat: bool=False,
low_freq: float=20.0,
n_mfcc: int=13,
n_mels: int=23,
preemphasis_coefficient: float=0.97,
raw_energy: bool=True,
remove_dc_offset: bool=True,
round_to_power_of_two: bool=True,
sr: int=16000,
snip_edges: bool=True,
subtract_mean: bool=False,
use_energy: bool=False,
vtln_high: float=-500.0,
vtln_low: float=100.0,
vtln_warp: float=1.0,
window_type: str=POVEY) -> Tensor:
"""Compute and return mel frequency cepstral coefficients from a waveform. The output is
identical to Kaldi's.
Args:
waveform (Tensor): A waveform tensor with shape `(C, T)`.
blackman_coeff (float, optional): Coefficient for Blackman window.. Defaults to 0.42.
cepstral_lifter (float, optional): Scaling of output mfccs. Defaults to 22.0.
channel (int, optional): Select the channel of waveform. Defaults to -1.
dither (float, optional): Dithering constant . Defaults to 0.0.
energy_floor (float, optional): Floor on energy of the output Spectrogram. Defaults to 1.0.
frame_length (float, optional): Frame length in milliseconds. Defaults to 25.0.
frame_shift (float, optional): Shift between adjacent frames in milliseconds. Defaults to 10.0.
high_freq (float, optional): The upper cut-off frequency. Defaults to 0.0.
htk_compat (bool, optional): Put energy to the last when it is set True. Defaults to False.
low_freq (float, optional): The lower cut-off frequency. Defaults to 20.0.
n_mfcc (int, optional): Number of cepstra in MFCC. Defaults to 13.
n_mels (int, optional): Number of output mel bins. Defaults to 23.
preemphasis_coefficient (float, optional): Preemphasis coefficient for input waveform. Defaults to 0.97.
raw_energy (bool, optional): Whether to compute before preemphasis and windowing. Defaults to True.
remove_dc_offset (bool, optional): Whether to subtract mean from waveform on frames. Defaults to True.
round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
to FFT. Defaults to True.
sr (int, optional): Sample rate of input waveform. Defaults to 16000.
snip_edges (bool, optional): Drop samples in the end of waveform that cann't fit a singal frame when it
is set True. Otherwise performs reflect padding to the end of waveform. Defaults to True.
subtract_mean (bool, optional): Whether to subtract mean of feature files. Defaults to False.
use_energy (bool, optional): Add an dimension with energy of spectrogram to the output. Defaults to False.
vtln_high (float, optional): High inflection point in piecewise linear VTLN warping function. Defaults to -500.0.
vtln_low (float, optional): Low inflection point in piecewise linear VTLN warping function. Defaults to 100.0.
vtln_warp (float, optional): Vtln warp factor. Defaults to 1.0.
window_type (str, optional): Choose type of window for FFT computation. Defaults to POVEY.
Returns:
Tensor: A mel frequency cepstral coefficients tensor with shape `(m, n_mfcc)`.
"""
assert n_mfcc <= n_mels, 'n_mfcc cannot be larger than n_mels: %d vs %d' % (
n_mfcc, n_mels)
dtype = waveform.dtype
# (m, n_mels + use_energy)
feature = fbank(
waveform=waveform,
blackman_coeff=blackman_coeff,
channel=channel,
dither=dither,
energy_floor=energy_floor,
frame_length=frame_length,
frame_shift=frame_shift,
high_freq=high_freq,
htk_compat=htk_compat,
low_freq=low_freq,
n_mels=n_mels,
preemphasis_coefficient=preemphasis_coefficient,
raw_energy=raw_energy,
remove_dc_offset=remove_dc_offset,
round_to_power_of_two=round_to_power_of_two,
sr=sr,
snip_edges=snip_edges,
subtract_mean=False,
use_energy=use_energy,
use_log_fbank=True,
use_power=True,
vtln_high=vtln_high,
vtln_low=vtln_low,
vtln_warp=vtln_warp,
window_type=window_type)
if use_energy:
# (m)
signal_log_energy = feature[:, n_mels if htk_compat else 0]
mel_offset = int(not htk_compat)
feature = feature[:, mel_offset:(n_mels + mel_offset)]
# (n_mels, n_mfcc)
dct_matrix = _get_dct_matrix(n_mfcc, n_mels).astype(dtype=dtype)
# (m, n_mfcc)
feature = feature.matmul(dct_matrix)
if cepstral_lifter != 0.0:
# (1, n_mfcc)
lifter_coeffs = _get_lifter_coeffs(n_mfcc, cepstral_lifter).unsqueeze(0)
feature *= lifter_coeffs.astype(dtype=dtype)
if use_energy:
feature[:, 0] = signal_log_energy
if htk_compat:
energy = feature[:, 0].unsqueeze(1) # (m, 1)
feature = feature[:, 1:] # (m, n_mfcc - 1)
if not use_energy:
energy *= math.sqrt(2)
feature = paddle.concat((feature, energy), axis=1)
feature = _subtract_column_mean(feature, subtract_mean)
return feature