wehub-resource-sync
121 lines
4.3 KiB
Python
121 lines
4.3 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 numpy as np
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from parameterized import parameterized
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import paddle
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def parameterize(*params):
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return parameterized.expand(list(itertools.product(*params)))
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class TestAudioDatasets(unittest.TestCase):
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@parameterize(["dev", "train"], [40, 64])
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def test_tess_dataset(self, mode: str, params: int):
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"""
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TESS dataset
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Reference:
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Toronto emotional speech set (TESS) https://tspace.library.utoronto.ca/handle/1807/24487
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https://doi.org/10.5683/SP2/E8H2MF
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"""
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archive = {
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'url': 'https://bj.bcebos.com/paddleaudio/datasets/TESS_Toronto_emotional_speech_set_lite.zip',
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'md5': '9ffb5e3adf28d4d6b787fa94bd59b975',
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} # small part of TESS dataset for test.
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tess_dataset = paddle.audio.datasets.TESS(
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mode=mode, feat_type='mfcc', n_mfcc=params, archive=archive
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)
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idx = np.random.randint(0, 30)
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elem = tess_dataset[idx]
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self.assertTrue(elem[0].shape[0] == params)
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self.assertTrue(0 <= elem[1] <= 6)
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tess_dataset = paddle.audio.datasets.TESS(
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mode=mode, feat_type='spectrogram', n_fft=params
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)
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elem = tess_dataset[idx]
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self.assertTrue(elem[0].shape[0] == (params // 2 + 1))
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self.assertTrue(0 <= elem[1] <= 6)
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tess_dataset = paddle.audio.datasets.TESS(
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mode="dev", feat_type='logmelspectrogram', n_mels=params
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)
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elem = tess_dataset[idx]
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self.assertTrue(elem[0].shape[0] == params)
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self.assertTrue(0 <= elem[1] <= 6)
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tess_dataset = paddle.audio.datasets.TESS(
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mode="dev", feat_type='melspectrogram', n_mels=params
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)
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elem = tess_dataset[idx]
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self.assertTrue(elem[0].shape[0] == params)
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self.assertTrue(0 <= elem[1] <= 6)
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@parameterize(["dev", "train"], [40, 64])
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def test_esc50_dataset(self, mode: str, params: int):
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"""
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ESC50 dataset
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Reference:
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ESC: Dataset for Environmental Sound Classification
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http://dx.doi.org/10.1145/2733373.2806390
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"""
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archive = {
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'url': 'https://bj.bcebos.com/paddleaudio/datasets/ESC-50-master-lite.zip',
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'md5': '1e9ba53265143df5b2804a743f2d1956',
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} # small part of ESC50 dataset for test.
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esc50_dataset = paddle.audio.datasets.ESC50(
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mode=mode, feat_type='raw', archive=archive
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)
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idx = np.random.randint(0, 6)
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elem = esc50_dataset[idx]
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self.assertTrue(elem[0].shape[0] == 220500)
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self.assertTrue(0 <= elem[1] <= 2)
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esc50_dataset = paddle.audio.datasets.ESC50(
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mode=mode, feat_type='mfcc', n_mfcc=params, archive=archive
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)
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idx = np.random.randint(0, 6)
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elem = esc50_dataset[idx]
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self.assertTrue(elem[0].shape[0] == params)
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self.assertTrue(0 <= elem[1] <= 2)
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esc50_dataset = paddle.audio.datasets.ESC50(
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mode=mode, feat_type='spectrogram', n_fft=params
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)
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elem = esc50_dataset[idx]
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self.assertTrue(elem[0].shape[0] == (params // 2 + 1))
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self.assertTrue(0 <= elem[1] <= 2)
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esc50_dataset = paddle.audio.datasets.ESC50(
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mode=mode, feat_type='logmelspectrogram', n_mels=params
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)
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elem = esc50_dataset[idx]
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self.assertTrue(elem[0].shape[0] == params)
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self.assertTrue(0 <= elem[1] <= 2)
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esc50_dataset = paddle.audio.datasets.ESC50(
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mode=mode, feat_type='melspectrogram', n_mels=params
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)
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elem = esc50_dataset[idx]
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self.assertTrue(elem[0].shape[0] == params)
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self.assertTrue(0 <= elem[1] <= 2)
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if __name__ == '__main__':
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unittest.main()
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