You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
124 lines
4.1 KiB
124 lines
4.1 KiB
# 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 os
|
|
import urllib.request
|
|
|
|
import librosa
|
|
import numpy as np
|
|
import paddle
|
|
import paddleaudio
|
|
import torch
|
|
import torchaudio
|
|
|
|
wav_url = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
|
|
if not os.path.isfile(os.path.basename(wav_url)):
|
|
urllib.request.urlretrieve(wav_url, os.path.basename(wav_url))
|
|
|
|
waveform, sr = paddleaudio.backends.soundfile_load(os.path.abspath(os.path.basename(wav_url)))
|
|
waveform_tensor = paddle.to_tensor(waveform).unsqueeze(0)
|
|
waveform_tensor_torch = torch.from_numpy(waveform).unsqueeze(0)
|
|
|
|
# Feature conf
|
|
mel_conf = {
|
|
'sr': sr,
|
|
'n_fft': 512,
|
|
'hop_length': 128,
|
|
'n_mels': 40,
|
|
}
|
|
|
|
mel_conf_torchaudio = {
|
|
'sample_rate': sr,
|
|
'n_fft': 512,
|
|
'hop_length': 128,
|
|
'n_mels': 40,
|
|
'norm': 'slaney',
|
|
'mel_scale': 'slaney',
|
|
}
|
|
|
|
|
|
def enable_cpu_device():
|
|
paddle.set_device('cpu')
|
|
|
|
|
|
def enable_gpu_device():
|
|
paddle.set_device('gpu')
|
|
|
|
|
|
log_mel_extractor = paddle.audio.features.LogMelSpectrogram(
|
|
**mel_conf, f_min=0.0, top_db=80.0, dtype=waveform_tensor.dtype)
|
|
|
|
|
|
def log_melspectrogram():
|
|
return log_mel_extractor(waveform_tensor).squeeze(0)
|
|
|
|
|
|
def test_log_melspect_cpu(benchmark):
|
|
enable_cpu_device()
|
|
feature_paddleaudio = benchmark(log_melspectrogram)
|
|
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
|
|
feature_librosa = librosa.power_to_db(feature_librosa, top_db=80.0)
|
|
np.testing.assert_array_almost_equal(
|
|
feature_librosa, feature_paddleaudio, decimal=3)
|
|
|
|
|
|
def test_log_melspect_gpu(benchmark):
|
|
enable_gpu_device()
|
|
feature_paddleaudio = benchmark(log_melspectrogram)
|
|
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
|
|
feature_librosa = librosa.power_to_db(feature_librosa, top_db=80.0)
|
|
np.testing.assert_array_almost_equal(
|
|
feature_librosa, feature_paddleaudio, decimal=2)
|
|
|
|
|
|
mel_extractor_torchaudio = torchaudio.transforms.MelSpectrogram(
|
|
**mel_conf_torchaudio, f_min=0.0)
|
|
amplitude_to_DB = torchaudio.transforms.AmplitudeToDB('power', top_db=80.0)
|
|
|
|
|
|
def melspectrogram_torchaudio():
|
|
return mel_extractor_torchaudio(waveform_tensor_torch).squeeze(0)
|
|
|
|
|
|
def log_melspectrogram_torchaudio():
|
|
mel_specgram = mel_extractor_torchaudio(waveform_tensor_torch)
|
|
return amplitude_to_DB(mel_specgram).squeeze(0)
|
|
|
|
|
|
def test_log_melspect_cpu_torchaudio(benchmark):
|
|
global waveform_tensor_torch, mel_extractor_torchaudio, amplitude_to_DB
|
|
|
|
mel_extractor_torchaudio = mel_extractor_torchaudio.to('cpu')
|
|
waveform_tensor_torch = waveform_tensor_torch.to('cpu')
|
|
amplitude_to_DB = amplitude_to_DB.to('cpu')
|
|
|
|
feature_paddleaudio = benchmark(log_melspectrogram_torchaudio)
|
|
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
|
|
feature_librosa = librosa.power_to_db(feature_librosa, top_db=80.0)
|
|
np.testing.assert_array_almost_equal(
|
|
feature_librosa, feature_paddleaudio, decimal=3)
|
|
|
|
|
|
def test_log_melspect_gpu_torchaudio(benchmark):
|
|
global waveform_tensor_torch, mel_extractor_torchaudio, amplitude_to_DB
|
|
|
|
mel_extractor_torchaudio = mel_extractor_torchaudio.to('cuda')
|
|
waveform_tensor_torch = waveform_tensor_torch.to('cuda')
|
|
amplitude_to_DB = amplitude_to_DB.to('cuda')
|
|
|
|
feature_torchaudio = benchmark(log_melspectrogram_torchaudio)
|
|
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
|
|
feature_librosa = librosa.power_to_db(feature_librosa, top_db=80.0)
|
|
np.testing.assert_array_almost_equal(
|
|
feature_librosa, feature_torchaudio.cpu(), decimal=2)
|