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PaddleSpeech/tests/unit/audio/features/test_librosa.py

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# 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.
import unittest
import librosa
import numpy as np
import paddle
import paddlespeech.audio
from .base import FeatTest
from paddlespeech.audio.functional.window import get_window
class TestLibrosa(FeatTest):
def initParmas(self):
self.n_fft = 512
self.hop_length = 128
self.n_mels = 40
self.n_mfcc = 20
self.fmin = 0.0
self.window_str = 'hann'
self.pad_mode = 'reflect'
self.top_db = 80.0
def test_stft(self):
if len(self.waveform.shape) == 2: # (C, T)
self.waveform = self.waveform.squeeze(
0) # 1D input for librosa.feature.melspectrogram
feature_librosa = librosa.core.stft(
y=self.waveform,
n_fft=self.n_fft,
hop_length=self.hop_length,
win_length=None,
window=self.window_str,
center=True,
dtype=None,
pad_mode=self.pad_mode, )
x = paddle.to_tensor(self.waveform).unsqueeze(0)
window = get_window(self.window_str, self.n_fft, dtype=x.dtype)
feature_paddle = paddle.signal.stft(
x=x,
n_fft=self.n_fft,
hop_length=self.hop_length,
win_length=None,
window=window,
center=True,
pad_mode=self.pad_mode,
normalized=False,
onesided=True, ).squeeze(0)
np.testing.assert_array_almost_equal(
feature_librosa, feature_paddle, decimal=5)
def test_istft(self):
if len(self.waveform.shape) == 2: # (C, T)
self.waveform = self.waveform.squeeze(
0) # 1D input for librosa.feature.melspectrogram
# Get stft result from librosa.
stft_matrix = librosa.core.stft(
y=self.waveform,
n_fft=self.n_fft,
hop_length=self.hop_length,
win_length=None,
window=self.window_str,
center=True,
pad_mode=self.pad_mode, )
feature_librosa = librosa.core.istft(
stft_matrix=stft_matrix,
hop_length=self.hop_length,
win_length=None,
window=self.window_str,
center=True,
dtype=None,
length=None, )
x = paddle.to_tensor(stft_matrix).unsqueeze(0)
window = get_window(
self.window_str,
self.n_fft,
dtype=paddle.to_tensor(self.waveform).dtype)
feature_paddle = paddle.signal.istft(
x=x,
n_fft=self.n_fft,
hop_length=self.hop_length,
win_length=None,
window=window,
center=True,
normalized=False,
onesided=True,
length=None,
return_complex=False, ).squeeze(0)
np.testing.assert_array_almost_equal(
feature_librosa, feature_paddle, decimal=5)
def test_mel(self):
feature_librosa = librosa.filters.mel(
sr=self.sr,
n_fft=self.n_fft,
n_mels=self.n_mels,
fmin=self.fmin,
fmax=None,
htk=False,
norm='slaney',
dtype=self.waveform.dtype, )
feature_compliance = paddlespeech.audio.compliance.librosa.compute_fbank_matrix(
sr=self.sr,
n_fft=self.n_fft,
n_mels=self.n_mels,
fmin=self.fmin,
fmax=None,
htk=False,
norm='slaney',
dtype=self.waveform.dtype, )
x = paddle.to_tensor(self.waveform)
feature_functional = paddlespeech.audio.functional.compute_fbank_matrix(
sr=self.sr,
n_fft=self.n_fft,
n_mels=self.n_mels,
f_min=self.fmin,
f_max=None,
htk=False,
norm='slaney',
dtype=x.dtype, )
np.testing.assert_array_almost_equal(feature_librosa,
feature_compliance)
np.testing.assert_array_almost_equal(feature_librosa,
feature_functional)
def test_melspect(self):
if len(self.waveform.shape) == 2: # (C, T)
self.waveform = self.waveform.squeeze(
0) # 1D input for librosa.feature.melspectrogram
# librosa:
feature_librosa = librosa.feature.melspectrogram(
y=self.waveform,
sr=self.sr,
n_fft=self.n_fft,
hop_length=self.hop_length,
n_mels=self.n_mels,
fmin=self.fmin)
# paddlespeech.audio.compliance.librosa:
feature_compliance = paddlespeech.audio.compliance.librosa.melspectrogram(
x=self.waveform,
sr=self.sr,
window_size=self.n_fft,
hop_length=self.hop_length,
n_mels=self.n_mels,
fmin=self.fmin,
to_db=False)
# paddlespeech.audio.features.layer
x = paddle.to_tensor(
self.waveform, dtype=paddle.float64).unsqueeze(0) # Add batch dim.
feature_extractor = paddlespeech.audio.features.MelSpectrogram(
sr=self.sr,
n_fft=self.n_fft,
hop_length=self.hop_length,
n_mels=self.n_mels,
f_min=self.fmin,
dtype=x.dtype)
feature_layer = feature_extractor(x).squeeze(0).numpy()
np.testing.assert_array_almost_equal(
feature_librosa, feature_compliance, decimal=5)
np.testing.assert_array_almost_equal(
feature_librosa, feature_layer, decimal=5)
def test_log_melspect(self):
if len(self.waveform.shape) == 2: # (C, T)
self.waveform = self.waveform.squeeze(
0) # 1D input for librosa.feature.melspectrogram
# librosa:
feature_librosa = librosa.feature.melspectrogram(
y=self.waveform,
sr=self.sr,
n_fft=self.n_fft,
hop_length=self.hop_length,
n_mels=self.n_mels,
fmin=self.fmin)
feature_librosa = librosa.power_to_db(feature_librosa, top_db=None)
# paddlespeech.audio.compliance.librosa:
feature_compliance = paddlespeech.audio.compliance.librosa.melspectrogram(
x=self.waveform,
sr=self.sr,
window_size=self.n_fft,
hop_length=self.hop_length,
n_mels=self.n_mels,
fmin=self.fmin)
# paddlespeech.audio.features.layer
x = paddle.to_tensor(
self.waveform, dtype=paddle.float64).unsqueeze(0) # Add batch dim.
feature_extractor = paddlespeech.audio.features.LogMelSpectrogram(
sr=self.sr,
n_fft=self.n_fft,
hop_length=self.hop_length,
n_mels=self.n_mels,
f_min=self.fmin,
dtype=x.dtype)
feature_layer = feature_extractor(x).squeeze(0).numpy()
np.testing.assert_array_almost_equal(
feature_librosa, feature_compliance, decimal=5)
np.testing.assert_array_almost_equal(
feature_librosa, feature_layer, decimal=4)
def test_mfcc(self):
if len(self.waveform.shape) == 2: # (C, T)
self.waveform = self.waveform.squeeze(
0) # 1D input for librosa.feature.melspectrogram
# librosa:
feature_librosa = librosa.feature.mfcc(
y=self.waveform,
sr=self.sr,
S=None,
n_mfcc=self.n_mfcc,
dct_type=2,
norm='ortho',
lifter=0,
n_fft=self.n_fft,
hop_length=self.hop_length,
n_mels=self.n_mels,
fmin=self.fmin)
# paddlespeech.audio.compliance.librosa:
feature_compliance = paddlespeech.audio.compliance.librosa.mfcc(
x=self.waveform,
sr=self.sr,
n_mfcc=self.n_mfcc,
dct_type=2,
norm='ortho',
lifter=0,
window_size=self.n_fft,
hop_length=self.hop_length,
n_mels=self.n_mels,
fmin=self.fmin,
top_db=self.top_db)
# paddlespeech.audio.features.layer
x = paddle.to_tensor(
self.waveform, dtype=paddle.float64).unsqueeze(0) # Add batch dim.
feature_extractor = paddlespeech.audio.features.MFCC(
sr=self.sr,
n_mfcc=self.n_mfcc,
n_fft=self.n_fft,
hop_length=self.hop_length,
n_mels=self.n_mels,
f_min=self.fmin,
top_db=self.top_db,
dtype=x.dtype)
feature_layer = feature_extractor(x).squeeze(0).numpy()
np.testing.assert_array_almost_equal(
feature_librosa, feature_compliance, decimal=4)
np.testing.assert_array_almost_equal(
feature_librosa, feature_layer, decimal=4)
if __name__ == '__main__':
unittest.main()