wehub-resource-sync
123 lines
3.6 KiB
Python
123 lines
3.6 KiB
Python
# 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 itertools
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import unittest
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import librosa
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import numpy as np
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from parameterized import parameterized
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import paddle
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import paddle.audio
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def parameterize(*params):
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return parameterized.expand(list(itertools.product(*params)))
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class TestFeatures(unittest.TestCase):
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def setUp(self):
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self.initParams()
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def initParams(self):
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def get_wav_data(dtype: str, num_channels: int, num_frames: int):
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dtype_ = getattr(paddle, dtype)
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base = paddle.linspace(-1.0, 1.0, num_frames, dtype=dtype_) * 0.1
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data = base.tile([num_channels, 1])
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return data
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self.hop_length = 128
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self.duration = 0.5
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self.num_channels = 1
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self.sr = 16000
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self.dtype = "float32"
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waveform_tensor = get_wav_data(
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self.dtype, self.num_channels, num_frames=self.duration * self.sr
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)
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self.waveform = waveform_tensor.numpy()
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@parameterize(
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[8000], [128, 256], [64, 32], [0.0, 1.0], ['float32', 'float64']
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)
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def test_mel(
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self, sr: int, n_fft: int, n_mels: int, fmin: float, dtype: str
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):
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feature_librosa = librosa.filters.mel(
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sr=sr,
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n_fft=n_fft,
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n_mels=n_mels,
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fmin=fmin,
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fmax=None,
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htk=False,
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norm='slaney',
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dtype=np.dtype(dtype),
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)
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paddle_dtype = getattr(paddle, dtype)
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feature_functional = paddle.audio.functional.compute_fbank_matrix(
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sr=sr,
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n_fft=n_fft,
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n_mels=n_mels,
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f_min=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=paddle_dtype,
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)
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_functional
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)
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@parameterize([8000, 16000], [128, 256], [64, 82], [40, 80], [False, True])
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def test_melspect(
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self, sr: int, n_fft: int, hop_length: int, n_mels: int, htk: bool
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):
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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
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) # 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=sr,
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n_fft=n_fft,
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hop_length=hop_length,
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n_mels=n_mels,
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htk=htk,
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fmin=50.0,
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)
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# paddle.audio.features.layer
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x = paddle.to_tensor(self.waveform, dtype=paddle.float64).unsqueeze(
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0
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) # Add batch dim.
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feature_extractor = paddle.audio.features.MelSpectrogram(
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sr=sr,
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n_fft=n_fft,
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hop_length=hop_length,
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n_mels=n_mels,
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htk=htk,
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dtype=x.dtype,
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)
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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_layer, decimal=5
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)
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if __name__ == '__main__':
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unittest.main()
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