KP
2 years ago
parent
169040b4a2
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# 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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# 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 os
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import unittest
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import urllib.request
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mono_channel_wav = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
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multi_channels_wav = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/cat.wav'
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class BackendTest(unittest.TestCase):
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def setUp(self):
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self.initWavInput()
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def initWavInput(self):
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self.files = []
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for url in [mono_channel_wav, multi_channels_wav]:
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if not os.path.isfile(os.path.basename(url)):
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urllib.request.urlretrieve(url, os.path.basename(url))
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self.files.append(os.path.basename(url))
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def initParmas(self):
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raise NotImplementedError
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# 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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# 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 filecmp
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import os
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import unittest
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import numpy as np
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import soundfile as sf
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import paddleaudio
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from ..base import BackendTest
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class TestIO(BackendTest):
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def test_load_mono_channel(self):
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sf_data, sf_sr = sf.read(self.files[0])
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pa_data, pa_sr = paddleaudio.load(
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self.files[0], normal=False, dtype='float64')
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self.assertEqual(sf_data.dtype, pa_data.dtype)
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self.assertEqual(sf_sr, pa_sr)
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np.testing.assert_array_almost_equal(sf_data, pa_data)
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def test_load_multi_channels(self):
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sf_data, sf_sr = sf.read(self.files[1])
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sf_data = sf_data.T # Channel dim first
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pa_data, pa_sr = paddleaudio.load(
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self.files[1], mono=False, normal=False, dtype='float64')
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self.assertEqual(sf_data.dtype, pa_data.dtype)
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self.assertEqual(sf_sr, pa_sr)
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np.testing.assert_array_almost_equal(sf_data, pa_data)
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def test_save_mono_channel(self):
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waveform, sr = np.random.randint(
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low=-32768, high=32768, size=(48000), dtype=np.int16), 16000
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sf_tmp_file = 'sf_tmp.wav'
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pa_tmp_file = 'pa_tmp.wav'
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sf.write(sf_tmp_file, waveform, sr)
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paddleaudio.save(waveform, sr, pa_tmp_file)
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self.assertTrue(filecmp.cmp(sf_tmp_file, pa_tmp_file))
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for file in [sf_tmp_file, pa_tmp_file]:
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os.remove(file)
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def test_save_multi_channels(self):
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waveform, sr = np.random.randint(
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low=-32768, high=32768, size=(2, 48000), dtype=np.int16), 16000
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sf_tmp_file = 'sf_tmp.wav'
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pa_tmp_file = 'pa_tmp.wav'
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sf.write(sf_tmp_file, waveform.T, sr)
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paddleaudio.save(waveform.T, sr, pa_tmp_file)
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self.assertTrue(filecmp.cmp(sf_tmp_file, pa_tmp_file))
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for file in [sf_tmp_file, pa_tmp_file]:
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os.remove(file)
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if __name__ == '__main__':
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unittest.main()
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# 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 librosa
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import numpy as np
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import paddle
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import paddleaudio
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from .base import FeatTest
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from paddleaudio.functional.window import get_window
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class TestLibrosa(FeatTest):
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def initParmas(self):
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self.n_fft = 512
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self.hop_length = 128
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self.n_mels = 40
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self.fmin = 0.0
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self.window_str = 'hann'
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self.pad_mode = 'reflect'
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def test_stft(self):
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if len(self.waveform.shape) == 2: # (C, T)
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self.waveform = self.waveform.squeeze(
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0) # 1D input for librosa.feature.melspectrogram
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feature_librosa = librosa.core.stft(
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y=self.waveform,
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n_fft=self.n_fft,
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hop_length=self.hop_length,
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win_length=None,
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window=self.window_str,
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center=True,
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dtype=None,
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pad_mode=self.pad_mode, )
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x = paddle.to_tensor(self.waveform).unsqueeze(0)
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window = get_window(self.window_str, self.n_fft, dtype=x.dtype)
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feature_paddle = paddle.signal.stft(
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x=x,
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n_fft=self.n_fft,
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hop_length=self.hop_length,
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win_length=None,
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window=window,
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center=True,
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pad_mode=self.pad_mode,
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normalized=False,
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onesided=True, ).squeeze(0)
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_paddle, decimal=5)
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def test_istft(self):
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if len(self.waveform.shape) == 2: # (C, T)
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self.waveform = self.waveform.squeeze(
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0) # 1D input for librosa.feature.melspectrogram
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# Get stft result from librosa.
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stft_matrix = librosa.core.stft(
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y=self.waveform,
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n_fft=self.n_fft,
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hop_length=self.hop_length,
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win_length=None,
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window=self.window_str,
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center=True,
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pad_mode=self.pad_mode, )
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feature_librosa = librosa.core.istft(
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stft_matrix=stft_matrix,
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hop_length=self.hop_length,
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win_length=None,
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window=self.window_str,
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center=True,
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dtype=None,
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length=None, )
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x = paddle.to_tensor(stft_matrix).unsqueeze(0)
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window = get_window(
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self.window_str,
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self.n_fft,
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dtype=paddle.to_tensor(self.waveform).dtype)
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feature_paddle = paddle.signal.istft(
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x=x,
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n_fft=self.n_fft,
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hop_length=self.hop_length,
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win_length=None,
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window=window,
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center=True,
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normalized=False,
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onesided=True,
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length=None,
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return_complex=False, ).squeeze(0)
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_paddle, decimal=5)
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def test_mel(self):
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feature_librosa = librosa.filters.mel(
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sr=self.sr,
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n_fft=self.n_fft,
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n_mels=self.n_mels,
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fmin=self.fmin,
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fmax=None,
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htk=False,
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norm='slaney',
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dtype=self.waveform.dtype, )
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feature_compliance = paddleaudio.compliance.librosa.compute_fbank_matrix(
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sr=self.sr,
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n_fft=self.n_fft,
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n_mels=self.n_mels,
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fmin=self.fmin,
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fmax=None,
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htk=False,
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norm='slaney',
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dtype=self.waveform.dtype, )
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x = paddle.to_tensor(self.waveform)
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feature_functional = paddleaudio.functional.compute_fbank_matrix(
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sr=self.sr,
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n_fft=self.n_fft,
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n_mels=self.n_mels,
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f_min=self.fmin,
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f_max=None,
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htk=False,
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norm='slaney',
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dtype=x.dtype, )
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np.testing.assert_array_almost_equal(feature_librosa,
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feature_compliance)
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np.testing.assert_array_almost_equal(feature_librosa,
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feature_functional)
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def test_melspect(self):
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if len(self.waveform.shape) == 2: # (C, T)
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self.waveform = self.waveform.squeeze(
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0) # 1D input for librosa.feature.melspectrogram
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# librosa:
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feature_librosa = librosa.feature.melspectrogram(
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y=self.waveform,
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sr=self.sr,
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n_fft=self.n_fft,
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hop_length=self.hop_length,
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n_mels=self.n_mels,
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fmin=self.fmin)
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# paddleaudio.compliance.librosa:
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feature_compliance = paddleaudio.compliance.librosa.melspectrogram(
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x=self.waveform,
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sr=self.sr,
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window_size=self.n_fft,
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hop_length=self.hop_length,
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n_mels=self.n_mels,
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fmin=self.fmin,
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to_db=False)
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# paddleaudio.features.layer
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x = paddle.to_tensor(
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self.waveform, dtype=paddle.float64).unsqueeze(0) # Add batch dim.
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feature_extractor = paddleaudio.features.MelSpectrogram(
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sr=self.sr,
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n_fft=self.n_fft,
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hop_length=self.hop_length,
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n_mels=self.n_mels,
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f_min=self.fmin,
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dtype=x.dtype)
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feature_layer = feature_extractor(x).squeeze(0).numpy()
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_compliance, decimal=5)
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_layer, decimal=5)
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def test_log_melspect(self):
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if len(self.waveform.shape) == 2: # (C, T)
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self.waveform = self.waveform.squeeze(
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0) # 1D input for librosa.feature.melspectrogram
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# librosa:
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feature_librosa = librosa.feature.melspectrogram(
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y=self.waveform,
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sr=self.sr,
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n_fft=self.n_fft,
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hop_length=self.hop_length,
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n_mels=self.n_mels,
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fmin=self.fmin)
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feature_librosa = librosa.power_to_db(feature_librosa, top_db=None)
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# paddleaudio.compliance.librosa:
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feature_compliance = paddleaudio.compliance.librosa.melspectrogram(
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x=self.waveform,
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sr=self.sr,
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window_size=self.n_fft,
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hop_length=self.hop_length,
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n_mels=self.n_mels,
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fmin=self.fmin)
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# paddleaudio.features.layer
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x = paddle.to_tensor(
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self.waveform, dtype=paddle.float64).unsqueeze(0) # Add batch dim.
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feature_extractor = paddleaudio.features.LogMelSpectrogram(
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sr=self.sr,
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n_fft=self.n_fft,
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hop_length=self.hop_length,
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n_mels=self.n_mels,
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f_min=self.fmin,
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dtype=x.dtype)
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feature_layer = feature_extractor(x).squeeze(0).numpy()
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_compliance, decimal=5)
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_layer, decimal=4)
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if __name__ == '__main__':
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unittest.main()
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Reference in new issue