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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 urllib.request
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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 torch
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import torchaudio
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import paddleaudio
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wav_url = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
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if not os.path.isfile(os.path.basename(wav_url)):
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urllib.request.urlretrieve(wav_url, os.path.basename(wav_url))
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waveform, sr = paddleaudio.load(os.path.abspath(os.path.basename(wav_url)))
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waveform_tensor = paddle.to_tensor(waveform).unsqueeze(0)
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waveform_tensor_torch = torch.from_numpy(waveform).unsqueeze(0)
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# Feature conf
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mel_conf = {
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'sr': sr,
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'n_fft': 512,
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'hop_length': 128,
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'n_mels': 40,
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}
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mel_conf_torchaudio = {
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'sample_rate': sr,
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'n_fft': 512,
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'hop_length': 128,
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'n_mels': 40,
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'norm': 'slaney',
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'mel_scale': 'slaney',
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}
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def enable_cpu_device():
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paddle.set_device('cpu')
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def enable_gpu_device():
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paddle.set_device('gpu')
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mel_extractor = paddleaudio.features.MelSpectrogram(
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**mel_conf, f_min=0.0, dtype=waveform_tensor.dtype)
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def melspectrogram():
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return mel_extractor(waveform_tensor).squeeze(0)
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def test_melspect_cpu(benchmark):
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enable_cpu_device()
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feature_paddleaudio = benchmark(melspectrogram)
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feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_paddleaudio, decimal=3)
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def test_melspect_gpu(benchmark):
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enable_gpu_device()
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feature_paddleaudio = benchmark(melspectrogram)
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feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_paddleaudio, decimal=3)
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mel_extractor_torchaudio = torchaudio.transforms.MelSpectrogram(
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**mel_conf_torchaudio, f_min=0.0)
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def melspectrogram_torchaudio():
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return mel_extractor_torchaudio(waveform_tensor_torch).squeeze(0)
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def test_melspect_cpu_torchaudio(benchmark):
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global waveform_tensor_torch, mel_extractor_torchaudio
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mel_extractor_torchaudio = mel_extractor_torchaudio.to('cpu')
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waveform_tensor_torch = waveform_tensor_torch.to('cpu')
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feature_paddleaudio = benchmark(melspectrogram_torchaudio)
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feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_paddleaudio, decimal=3)
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def test_melspect_gpu_torchaudio(benchmark):
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global waveform_tensor_torch, mel_extractor_torchaudio
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mel_extractor_torchaudio = mel_extractor_torchaudio.to('cuda')
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waveform_tensor_torch = waveform_tensor_torch.to('cuda')
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feature_torchaudio = benchmark(melspectrogram_torchaudio)
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feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_torchaudio.cpu(), decimal=3)
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@ -0,0 +1,122 @@
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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 urllib.request
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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 torch
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import torchaudio
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import paddleaudio
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wav_url = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
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if not os.path.isfile(os.path.basename(wav_url)):
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urllib.request.urlretrieve(wav_url, os.path.basename(wav_url))
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waveform, sr = paddleaudio.load(os.path.abspath(os.path.basename(wav_url)))
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waveform_tensor = paddle.to_tensor(waveform).unsqueeze(0)
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waveform_tensor_torch = torch.from_numpy(waveform).unsqueeze(0)
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# Feature conf
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mel_conf = {
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'sr': sr,
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'n_fft': 512,
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'hop_length': 128,
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'n_mels': 40,
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}
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mfcc_conf = {
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'n_mfcc': 20,
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'top_db': 80.0,
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}
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mfcc_conf.update(mel_conf)
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mel_conf_torchaudio = {
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'sample_rate': sr,
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'n_fft': 512,
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'hop_length': 128,
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'n_mels': 40,
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'norm': 'slaney',
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'mel_scale': 'slaney',
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}
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mfcc_conf_torchaudio = {
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'sample_rate': sr,
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'n_mfcc': 20,
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}
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def enable_cpu_device():
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paddle.set_device('cpu')
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def enable_gpu_device():
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paddle.set_device('gpu')
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mfcc_extractor = paddleaudio.features.MFCC(
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**mfcc_conf, f_min=0.0, dtype=waveform_tensor.dtype)
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def mfcc():
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return mfcc_extractor(waveform_tensor).squeeze(0)
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def test_mfcc_cpu(benchmark):
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enable_cpu_device()
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feature_paddleaudio = benchmark(mfcc)
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feature_librosa = librosa.feature.mfcc(waveform, **mel_conf)
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_paddleaudio, decimal=3)
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def test_mfcc_gpu(benchmark):
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enable_gpu_device()
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feature_paddleaudio = benchmark(mfcc)
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feature_librosa = librosa.feature.mfcc(waveform, **mel_conf)
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_paddleaudio, decimal=3)
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del mel_conf_torchaudio['sample_rate']
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mfcc_extractor_torchaudio = torchaudio.transforms.MFCC(
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**mfcc_conf_torchaudio, melkwargs=mel_conf_torchaudio)
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def mfcc_torchaudio():
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return mfcc_extractor_torchaudio(waveform_tensor_torch).squeeze(0)
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def test_mfcc_cpu_torchaudio(benchmark):
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global waveform_tensor_torch, mfcc_extractor_torchaudio
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mel_extractor_torchaudio = mfcc_extractor_torchaudio.to('cpu')
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waveform_tensor_torch = waveform_tensor_torch.to('cpu')
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feature_paddleaudio = benchmark(mfcc_torchaudio)
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feature_librosa = librosa.feature.mfcc(waveform, **mel_conf)
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_paddleaudio, decimal=3)
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def test_mfcc_gpu_torchaudio(benchmark):
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global waveform_tensor_torch, mfcc_extractor_torchaudio
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mel_extractor_torchaudio = mfcc_extractor_torchaudio.to('cuda')
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waveform_tensor_torch = waveform_tensor_torch.to('cuda')
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feature_torchaudio = benchmark(mfcc_torchaudio)
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feature_librosa = librosa.feature.mfcc(waveform, **mel_conf)
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np.testing.assert_array_almost_equal(
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feature_librosa, feature_torchaudio.cpu(), decimal=3)
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