msb-public
/
PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
1
0
Code Issues Projects Releases Wiki Activity

Add benchmark.

Browse Source
pull/1548/head
KP 2 years ago
parent 7c3a11ccef
commit 052d329c9d
6 changed files with 142 additions and 193 deletions
Show Stats Download Patch File Download Diff File

55
paddleaudio/tests/benchmark/README.md
Unescape View File

@ -1,45 +1,40 @@
# 1. Prepare
First, install `line_profiler` via pip.
First, install `pytest-benchmark` via pip.
```sh
pip install line_profiler
pip install pytest-benchmark
```
# 2. Run
Run the specific script for profiling.
```sh
kernprof -l features/mel_spectrogram.py
python -m line_profiler -u 1e-3 mel_spectrogram.py.lprof
pytest features.py
```
Result:
```sh
Timer unit: 0.001 s
========================================================================== test session starts ==========================================================================
platform linux -- Python 3.7.7, pytest-7.0.1, pluggy-1.0.0
benchmark: 3.4.1 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)
rootdir: /ssd3/chenxiaojie06/PaddleSpeech/DeepSpeech/paddleaudio
plugins: typeguard-2.12.1, benchmark-3.4.1, anyio-3.5.0
collected 6 items
Total time: 22.1208 s
File: features/mel_spectrogram.py
Function: test_melspect_cpu at line 13
Line # Hits Time Per Hit % Time Line Contents
==============================================================
13 @profile
14 def test_melspect_cpu(input_shape, times):
15 1 0.1 0.1 0.0 paddle.set_device('cpu')
16 1 234.5 234.5 1.1 x = paddle.randn(input_shape)
17 1 85.3 85.3 0.4 feature_extractor = paddleaudio.features.MelSpectrogram(**feat_conf, dtype=x.dtype)
18 101 0.5 0.0 0.0 for i in range(times):
19 100 21800.5 218.0 98.6 y = feature_extractor(x)
features.py ...... [100%]
Total time: 4.80543 s
File: features/mel_spectrogram.py
Function: test_melspect_gpu at line 22
Line # Hits Time Per Hit % Time Line Contents
==============================================================
22 @profile
23 def test_melspect_gpu(input_shape, times):
24 1 0.5 0.5 0.0 paddle.set_device('gpu')
25 1 4144.8 4144.8 86.3 x = paddle.randn(input_shape)
26 1 41.9 41.9 0.9 feature_extractor = paddleaudio.features.MelSpectrogram(**feat_conf, dtype=x.dtype)
27 101 0.2 0.0 0.0 for i in range(times):
28 100 618.1 6.2 12.9 y = feature_extractor(x)
------------------------------------------------------------------------------------------------- benchmark: 6 tests ------------------------------------------------------------------------------------------------
Name (time in us) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_melspect_gpu 632.2041 (1.0) 898.7449 (1.0) 709.3824 (1.0) 109.7022 (6.91) 676.1923 (1.0) 115.2642 (22.19) 1;0 1,409.6768 (1.0) 5 1
test_log_melspect_gpu 912.9159 (1.44) 1,222.0535 (1.36) 931.2489 (1.31) 34.4270 (2.17) 924.9896 (1.37) 5.1949 (1.0) 4;13 1,073.8268 (0.76) 82 1
test_mfcc_gpu 1,244.8374 (1.97) 1,321.3232 (1.47) 1,262.1319 (1.78) 15.8698 (1.0) 1,258.3155 (1.86) 14.1086 (2.72) 17;9 792.3102 (0.56) 91 1
test_melspect_cpu 19,106.5744 (30.22) 46,194.2125 (51.40) 27,458.7850 (38.71) 9,786.1071 (616.65) 23,830.0692 (35.24) 14,344.4724 (>1000.0) 3;0 36.4182 (0.03) 14 1
test_log_melspect_cpu 19,513.7132 (30.87) 20,367.2443 (22.66) 19,765.4018 (27.86) 167.1289 (10.53) 19,750.2729 (29.21) 188.9346 (36.37) 16;1 50.5935 (0.04) 49 1
test_mfcc_cpu 19,881.3528 (31.45) 20,427.2158 (22.73) 20,104.6574 (28.34) 129.5621 (8.16) 20,075.8977 (29.69) 150.9022 (29.05) 12;2 49.7397 (0.04) 48 1
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Legend:
Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile.
OPS: Operations Per Second, computed as 1 / Mean
========================================================================== 6 passed in 20.51s ===========================================================================
```

117
paddleaudio/tests/benchmark/features.py
Unescape View File

@ -0,0 +1,117 @@
# 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 librosa
import numpy as np
import paddle
import paddleaudio
# Feature conf
mel_conf = {
'sr': 16000,
'n_fft': 512,
'hop_length': 128,
'n_mels': 40,
}
mfcc_conf = {
'n_mfcc': 20,
'top_db': 80.0,
}
mfcc_conf.update(mel_conf)
input_shape = (48000)
waveform = np.random.random(size=input_shape)
waveform_tensor = paddle.to_tensor(waveform).unsqueeze(0)
def enable_cpu_device():
paddle.set_device('cpu')
def enable_gpu_device():
paddle.set_device('gpu')
mel_extractor = paddleaudio.features.MelSpectrogram(
**mel_conf, f_min=0.0, dtype=waveform_tensor.dtype)
def melspectrogram():
return mel_extractor(waveform_tensor).squeeze(0)
def test_melspect_cpu(benchmark):
enable_cpu_device()
feature_paddleaudio = benchmark(melspectrogram)
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
np.testing.assert_array_almost_equal(
feature_librosa, feature_paddleaudio, decimal=4)
def test_melspect_gpu(benchmark):
enable_gpu_device()
feature_paddleaudio = benchmark(melspectrogram)
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
np.testing.assert_array_almost_equal(
feature_librosa, feature_paddleaudio, decimal=4)
log_mel_extractor = paddleaudio.features.LogMelSpectrogram(
**mel_conf, f_min=0.0, 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=None)
np.testing.assert_array_almost_equal(
feature_librosa, feature_paddleaudio, decimal=4)
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=None)
np.testing.assert_array_almost_equal(
feature_librosa, feature_paddleaudio, decimal=4)
mfcc_extractor = paddleaudio.features.MFCC(
**mfcc_conf, f_min=0.0, dtype=waveform_tensor.dtype)
def mfcc():
return mfcc_extractor(waveform_tensor).squeeze(0)
def test_mfcc_cpu(benchmark):
enable_cpu_device()
feature_paddleaudio = benchmark(mfcc)
feature_librosa = librosa.feature.mfcc(waveform, **mel_conf)
np.testing.assert_array_almost_equal(
feature_librosa, feature_paddleaudio, decimal=4)
def test_mfcc_gpu(benchmark):
enable_gpu_device()
feature_paddleaudio = benchmark(mfcc)
feature_librosa = librosa.feature.mfcc(waveform, **mel_conf)
np.testing.assert_array_almost_equal(
feature_librosa, feature_paddleaudio, decimal=4)

13
paddleaudio/tests/benchmark/features/__init__.py
Unescape View File

@ -1,13 +0,0 @@
# 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.

50
paddleaudio/tests/benchmark/features/log_mel_spectrogram.py
Unescape View File

@ -1,50 +0,0 @@
# 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 paddle
import paddleaudio
feat_conf = {
'sr': 16000,
'n_fft': 512,
'hop_length': 128,
'n_mels': 40,
'f_min': 0.0,
}
@profile
def test_log_melspect_cpu(input_shape, times):
paddle.set_device('cpu')
x = paddle.randn(input_shape)
feature_extractor = paddleaudio.features.LogMelSpectrogram(
**feat_conf, dtype=x.dtype)
for i in range(times):
y = feature_extractor(x)
@profile
def test_log_melspect_gpu(input_shape, times):
paddle.set_device('gpu')
x = paddle.randn(input_shape)
feature_extractor = paddleaudio.features.LogMelSpectrogram(
**feat_conf, dtype=x.dtype)
for i in range(times):
y = feature_extractor(x)
input_shape = (16, 48000) # (N, T)
times = 100
test_log_melspect_cpu(input_shape, times)
test_log_melspect_gpu(input_shape, times)

50
paddleaudio/tests/benchmark/features/mel_spectrogram.py
Unescape View File

@ -1,50 +0,0 @@
# 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 paddle
import paddleaudio
feat_conf = {
'sr': 16000,
'n_fft': 512,
'hop_length': 128,
'n_mels': 40,
'f_min': 0.0,
}
@profile
def test_melspect_cpu(input_shape, times):
paddle.set_device('cpu')
x = paddle.randn(input_shape)
feature_extractor = paddleaudio.features.MelSpectrogram(
**feat_conf, dtype=x.dtype)
for i in range(times):
y = feature_extractor(x)
@profile
def test_melspect_gpu(input_shape, times):
paddle.set_device('gpu')
x = paddle.randn(input_shape)
feature_extractor = paddleaudio.features.MelSpectrogram(
**feat_conf, dtype=x.dtype)
for i in range(times):
y = feature_extractor(x)
input_shape = (16, 48000) # (N, T)
times = 100
test_melspect_cpu(input_shape, times)
test_melspect_gpu(input_shape, times)

50
paddleaudio/tests/benchmark/features/mfcc.py
Unescape View File

@ -1,50 +0,0 @@
# 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 paddle
import paddleaudio
feat_conf = {
'sr': 16000,
'n_mfcc': 20,
'n_fft': 512,
'hop_length': 128,
'n_mels': 40,
'f_min': 0.0,
'top_db': 80.0,
}
@profile
def test_mfcc_cpu(input_shape, times):
paddle.set_device('cpu')
x = paddle.randn(input_shape)
feature_extractor = paddleaudio.features.MFCC(**feat_conf, dtype=x.dtype)
for i in range(times):
y = feature_extractor(x)
@profile
def test_mfcc_gpu(input_shape, times):
paddle.set_device('gpu')
x = paddle.randn(input_shape)
feature_extractor = paddleaudio.features.MFCC(**feat_conf, dtype=x.dtype)
for i in range(times):
y = feature_extractor(x)
input_shape = (16, 48000) # (N, T)
times = 100
test_mfcc_cpu(input_shape, times)
test_mfcc_gpu(input_shape, times)
Write Preview
Loading…
Cancel
Save