wehub-resource-sync
414 lines
13 KiB
Python
414 lines
13 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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from scipy import signal
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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 TestAudioFunctions(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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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.n_fft = 512
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self.hop_length = 128
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self.n_mels = 40
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self.n_mfcc = 20
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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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self.top_db = 80.0
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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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self.window_size = 1024
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waveform_tensor = get_wav_data(
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self.dtype,
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self.num_channels,
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num_frames=int(self.duration * self.sr),
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)
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self.waveform = waveform_tensor.numpy()
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@parameterize([1.0, 3.0, 9.0, 25.0], [True, False])
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def test_audio_function(self, val: float, htk_flag: bool):
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mel_paddle = paddle.audio.functional.hz_to_mel(val, htk_flag)
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mel_paddle_tensor = paddle.audio.functional.hz_to_mel(
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paddle.to_tensor([val]), htk_flag
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)
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mel_librosa = librosa.hz_to_mel(val, htk=htk_flag)
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np.testing.assert_almost_equal(mel_paddle, mel_librosa, decimal=5)
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np.testing.assert_almost_equal(
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mel_paddle_tensor.numpy(), mel_librosa, decimal=3
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)
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hz_paddle = paddle.audio.functional.mel_to_hz(val, htk_flag)
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hz_paddle_tensor = paddle.audio.functional.mel_to_hz(
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paddle.to_tensor([val]), htk_flag
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)
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hz_librosa = librosa.mel_to_hz(val, htk=htk_flag)
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np.testing.assert_almost_equal(hz_paddle, hz_librosa, decimal=4)
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np.testing.assert_almost_equal(
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hz_paddle_tensor.numpy(), hz_librosa, decimal=4
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)
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decibel_paddle = paddle.audio.functional.power_to_db(
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paddle.to_tensor([val])
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)
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decibel_librosa = librosa.power_to_db(val)
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np.testing.assert_almost_equal(
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decibel_paddle.numpy(), decibel_paddle, decimal=5
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)
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@parameterize([1.0, 3.0, 9.0, 25.0], [True, False])
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def test_audio_function_static(self, val: float, htk_flag: bool):
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paddle.enable_static()
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with paddle.static.program_guard(main, startup):
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mel_paddle_tensor = paddle.audio.functional.hz_to_mel(
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paddle.to_tensor([val]), htk_flag
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)
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hz_paddle_tensor = paddle.audio.functional.mel_to_hz(
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paddle.to_tensor([val]), htk_flag
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)
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decibel_paddle = paddle.audio.functional.power_to_db(
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paddle.to_tensor([val])
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)
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exe = paddle.static.Executor()
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(
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mel_paddle_tensor_ret,
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hz_paddle_tensor_ret,
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decibel_paddle_ret,
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) = exe.run(
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main,
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fetch_list=[
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mel_paddle_tensor,
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hz_paddle_tensor,
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decibel_paddle,
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],
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)
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mel_librosa = librosa.hz_to_mel(val, htk=htk_flag)
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np.testing.assert_almost_equal(
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mel_paddle_tensor_ret, mel_librosa, decimal=3
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)
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hz_librosa = librosa.mel_to_hz(val, htk=htk_flag)
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np.testing.assert_almost_equal(
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hz_paddle_tensor_ret, hz_librosa, decimal=4
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)
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decibel_librosa = librosa.power_to_db(val)
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np.testing.assert_almost_equal(
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decibel_paddle_ret, decibel_librosa, decimal=5
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)
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paddle.disable_static()
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@parameterize(
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[64, 128, 256], [0.0, 0.5, 1.0], [10000, 11025], [False, True]
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)
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def test_audio_function_mel(
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self, n_mels: int, f_min: float, f_max: float, htk_flag: bool
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):
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librosa_mel_freq = librosa.mel_frequencies(
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n_mels, fmin=f_min, fmax=f_max, htk=htk_flag
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)
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paddle_mel_freq = paddle.audio.functional.mel_frequencies(
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n_mels, f_min, f_max, htk_flag, 'float64'
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)
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np.testing.assert_almost_equal(
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paddle_mel_freq, librosa_mel_freq, decimal=3
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)
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@parameterize(
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[64, 128, 256], [0.0, 0.5, 1.0], [10000, 11025], [False, True]
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)
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# TODO(MarioLulab) May cause precision error. Fix it soon
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def test_audio_function_mel_static(
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self, n_mels: int, f_min: float, f_max: float, htk_flag: bool
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):
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paddle.enable_static()
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with paddle.static.program_guard(main, startup):
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paddle_mel_freq = paddle.audio.functional.mel_frequencies(
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n_mels, f_min, f_max, htk_flag, 'float64'
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)
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exe = paddle.static.Executor()
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(paddle_mel_freq_ret,) = exe.run(main, fetch_list=[paddle_mel_freq])
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librosa_mel_freq = librosa.mel_frequencies(
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n_mels, fmin=f_min, fmax=f_max, htk=htk_flag
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)
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np.testing.assert_almost_equal(
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paddle_mel_freq_ret, librosa_mel_freq, decimal=3
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)
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paddle.disable_static()
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@parameterize([8000, 16000], [64, 128, 256])
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def test_audio_function_fft(self, sr: int, n_fft: int):
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librosa_fft = librosa.fft_frequencies(sr=sr, n_fft=n_fft)
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paddle_fft = paddle.audio.functional.fft_frequencies(sr, n_fft)
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np.testing.assert_almost_equal(paddle_fft, librosa_fft, decimal=5)
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@parameterize([1.0, 3.0, 9.0])
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def test_audio_function_exception(self, spect: float):
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try:
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paddle.audio.functional.power_to_db(
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paddle.to_tensor([spect]), amin=0
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)
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except Exception:
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pass
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try:
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paddle.audio.functional.power_to_db(
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paddle.to_tensor([spect]), ref_value=0
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)
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except Exception:
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pass
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try:
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paddle.audio.functional.power_to_db(
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paddle.to_tensor([spect]), top_db=-1
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)
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except Exception:
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pass
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@parameterize(
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[
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"hamming",
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"hann",
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"triang",
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"bohman",
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"blackman",
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"cosine",
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"tukey",
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"taylor",
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],
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[1, 512],
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)
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def test_window(self, window_type: str, n_fft: int):
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window_scipy = signal.get_window(window_type, n_fft)
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window_paddle = paddle.audio.functional.get_window(window_type, n_fft)
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np.testing.assert_array_almost_equal(
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window_scipy, window_paddle.numpy(), decimal=5
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)
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@parameterize([1, 512])
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def test_gaussian_window_and_exception(self, n_fft: int):
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window_scipy_gaussain = signal.windows.gaussian(n_fft, std=7)
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window_paddle_gaussian = paddle.audio.functional.get_window(
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('gaussian', 7), n_fft, False
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)
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np.testing.assert_array_almost_equal(
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window_scipy_gaussain, window_paddle_gaussian.numpy(), decimal=5
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)
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window_scipy_general_gaussain = signal.windows.general_gaussian(
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n_fft, 1, 7
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)
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window_paddle_general_gaussian = paddle.audio.functional.get_window(
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('general_gaussian', 1, 7), n_fft, False
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)
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np.testing.assert_array_almost_equal(
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window_scipy_gaussain, window_paddle_gaussian.numpy(), decimal=5
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)
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window_scipy_exp = signal.windows.exponential(n_fft)
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window_paddle_exp = paddle.audio.functional.get_window(
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('exponential', None, 1), n_fft, False
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)
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np.testing.assert_array_almost_equal(
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window_scipy_exp, window_paddle_exp.numpy(), decimal=5
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)
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try:
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window_paddle = paddle.audio.functional.get_window("hann", -1)
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except ValueError:
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pass
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try:
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window_paddle = paddle.audio.functional.get_window(
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"fake_window", self.n_fft
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)
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except ValueError:
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pass
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try:
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window_paddle = paddle.audio.functional.get_window(1043, self.n_fft)
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except ValueError:
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pass
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@parameterize([5, 13, 23], [257, 513, 1025])
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def test_create_dct(self, n_mfcc: int, n_mels: int):
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def dct(n_filters, n_input):
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basis = np.empty((n_filters, n_input))
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basis[0, :] = 1.0 / np.sqrt(n_input)
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samples = np.arange(1, 2 * n_input, 2) * np.pi / (2.0 * n_input)
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for i in range(1, n_filters):
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basis[i, :] = np.cos(i * samples) * np.sqrt(2.0 / n_input)
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return basis.T
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librosa_dct = dct(n_mfcc, n_mels)
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paddle_dct = paddle.audio.functional.create_dct(n_mfcc, n_mels)
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np.testing.assert_array_almost_equal(librosa_dct, paddle_dct, decimal=5)
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@parameterize(
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[128, 256, 512],
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[
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"hamming",
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"hann",
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"triang",
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"bohman",
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],
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[True, False],
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)
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def test_stft_and_spect(
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self, n_fft: int, window_str: str, center_flag: bool
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):
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hop_length = int(n_fft / 4)
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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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feature_librosa = librosa.core.stft(
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y=self.waveform,
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n_fft=n_fft,
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hop_length=hop_length,
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win_length=None,
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window=window_str,
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center=center_flag,
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dtype=None,
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pad_mode=self.pad_mode,
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)
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x = paddle.to_tensor(self.waveform).unsqueeze(0)
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window = paddle.audio.functional.get_window(
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window_str, n_fft, dtype=x.dtype
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)
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feature_paddle = paddle.signal.stft(
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x=x,
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n_fft=n_fft,
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hop_length=hop_length,
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win_length=None,
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window=window,
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center=center_flag,
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pad_mode=self.pad_mode,
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normalized=False,
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onesided=True,
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).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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)
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feature_bg = np.power(np.abs(feature_librosa), 2.0)
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feature_extractor = paddle.audio.features.Spectrogram(
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n_fft=n_fft,
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hop_length=hop_length,
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win_length=None,
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window=window_str,
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power=2.0,
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center=center_flag,
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pad_mode=self.pad_mode,
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)
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feature_layer = feature_extractor(x).squeeze(0)
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np.testing.assert_array_almost_equal(
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feature_layer, feature_bg, decimal=3
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)
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@parameterize(
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[128, 256, 512],
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[64, 82],
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[
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"hamming",
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"hann",
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"triang",
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"bohman",
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],
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)
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def test_istft(self, n_fft: int, hop_length: int, window_str: str):
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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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# 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=n_fft,
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hop_length=hop_length,
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win_length=None,
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window=window_str,
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center=True,
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pad_mode=self.pad_mode,
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)
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feature_librosa = librosa.core.istft(
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stft_matrix=stft_matrix,
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hop_length=hop_length,
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win_length=None,
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window=window_str,
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center=True,
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dtype=None,
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length=None,
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)
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x = paddle.to_tensor(stft_matrix).unsqueeze(0)
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window = paddle.audio.functional.get_window(
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window_str, n_fft, dtype=paddle.to_tensor(self.waveform).dtype
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)
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feature_paddle = paddle.signal.istft(
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x=x,
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n_fft=n_fft,
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hop_length=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,
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).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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)
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if __name__ == '__main__':
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unittest.main()
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