Add fbank and mfcc unittest.

pull/1548/head
KP 3 years ago
parent e7575ff434
commit 395e8d1bba

@ -0,0 +1,81 @@
# 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 numpy as np
import paddle
import torch
import torchaudio
import paddleaudio
from .base import FeatTest
class TestKaldi(FeatTest):
def initParmas(self):
self.window_size = 1024
self.dtype = 'float32'
def test_window(self):
t_hann_window = torch.hann_window(
self.window_size, periodic=False, dtype=eval(f'torch.{self.dtype}'))
t_hamm_window = torch.hamming_window(
self.window_size,
periodic=False,
alpha=0.54,
beta=0.46,
dtype=eval(f'torch.{self.dtype}'))
t_povey_window = torch.hann_window(
self.window_size, periodic=False,
dtype=eval(f'torch.{self.dtype}')).pow(0.85)
p_hann_window = paddleaudio.functional.window.get_window(
'hann',
self.window_size,
fftbins=False,
dtype=eval(f'paddle.{self.dtype}'))
p_hamm_window = paddleaudio.functional.window.get_window(
'hamming',
self.window_size,
fftbins=False,
dtype=eval(f'paddle.{self.dtype}'))
p_povey_window = paddleaudio.functional.window.get_window(
'hann',
self.window_size,
fftbins=False,
dtype=eval(f'paddle.{self.dtype}')).pow(0.85)
np.testing.assert_array_almost_equal(t_hann_window, p_hann_window)
np.testing.assert_array_almost_equal(t_hamm_window, p_hamm_window)
np.testing.assert_array_almost_equal(t_povey_window, p_povey_window)
def test_fbank(self):
ta_features = torchaudio.compliance.kaldi.fbank(
torch.from_numpy(self.waveform.astype(self.dtype)))
pa_features = paddleaudio.compliance.kaldi.fbank(
paddle.to_tensor(self.waveform.astype(self.dtype)))
np.testing.assert_array_almost_equal(
ta_features, pa_features, decimal=4)
def test_mfcc(self):
ta_features = torchaudio.compliance.kaldi.mfcc(
torch.from_numpy(self.waveform.astype(self.dtype)))
pa_features = paddleaudio.compliance.kaldi.mfcc(
paddle.to_tensor(self.waveform.astype(self.dtype)))
np.testing.assert_array_almost_equal(
ta_features, pa_features, decimal=4)
if __name__ == '__main__':
unittest.main()

@ -27,9 +27,11 @@ class TestLibrosa(FeatTest):
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)
@ -222,6 +224,58 @@ class TestLibrosa(FeatTest):
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)
# paddleaudio.compliance.librosa:
feature_compliance = paddleaudio.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)
# paddleaudio.features.layer
x = paddle.to_tensor(
self.waveform, dtype=paddle.float64).unsqueeze(0) # Add batch dim.
feature_extractor = paddleaudio.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()

Loading…
Cancel
Save