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82 lines
2.9 KiB
82 lines
2.9 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import paddle
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import paddleaudio
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import torch
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import torchaudio
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from base import FeatTest
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class TestKaldi(FeatTest):
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def initParmas(self):
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self.window_size = 1024
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self.dtype = 'float32'
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def test_window(self):
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t_hann_window = torch.hann_window(
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self.window_size, periodic=False, dtype=eval(f'torch.{self.dtype}'))
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t_hamm_window = torch.hamming_window(
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self.window_size,
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periodic=False,
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alpha=0.54,
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beta=0.46,
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dtype=eval(f'torch.{self.dtype}'))
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t_povey_window = torch.hann_window(
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self.window_size, periodic=False,
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dtype=eval(f'torch.{self.dtype}')).pow(0.85)
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p_hann_window = paddleaudio.functional.window.get_window(
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'hann',
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self.window_size,
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fftbins=False,
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dtype=eval(f'paddle.{self.dtype}'))
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p_hamm_window = paddleaudio.functional.window.get_window(
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'hamming',
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self.window_size,
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fftbins=False,
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dtype=eval(f'paddle.{self.dtype}'))
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p_povey_window = paddleaudio.functional.window.get_window(
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'hann',
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self.window_size,
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fftbins=False,
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dtype=eval(f'paddle.{self.dtype}')).pow(0.85)
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np.testing.assert_array_almost_equal(t_hann_window, p_hann_window)
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np.testing.assert_array_almost_equal(t_hamm_window, p_hamm_window)
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np.testing.assert_array_almost_equal(t_povey_window, p_povey_window)
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def test_fbank(self):
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ta_features = torchaudio.compliance.kaldi.fbank(
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torch.from_numpy(self.waveform.astype(self.dtype)))
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pa_features = paddleaudio.compliance.kaldi.fbank(
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paddle.to_tensor(self.waveform.astype(self.dtype)))
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np.testing.assert_array_almost_equal(
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ta_features, pa_features, decimal=4)
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def test_mfcc(self):
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ta_features = torchaudio.compliance.kaldi.mfcc(
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torch.from_numpy(self.waveform.astype(self.dtype)))
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pa_features = paddleaudio.compliance.kaldi.mfcc(
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paddle.to_tensor(self.waveform.astype(self.dtype)))
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np.testing.assert_array_almost_equal(
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ta_features, pa_features, decimal=4)
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if __name__ == '__main__':
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unittest.main()
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