forked from mindspore-Ecosystem/mindspore
210 lines
8.5 KiB
Python
210 lines
8.5 KiB
Python
"""
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Test Librispeech dataset operators
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"""
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import pytest
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import numpy as np
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import matplotlib.pyplot as plt
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import mindspore.dataset as ds
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import mindspore.dataset.vision.c_transforms as vision
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from mindspore import log as logger
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DATA_DIR = "/home/user06/zjm/data/libri_speech/LibriSpeech/"
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def test_librispeech_basic():
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"""
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Validate LibriSpeechDataset
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"""
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logger.info("Test LibriSpeechDataset Op")
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# case 1: test loading fault dataset
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data1 = ds.LibriSpeechDataset(DATA_DIR)
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num_iter1 = 0
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for _ in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_iter1 += 1
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assert num_iter1 == 2939
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# case 2: test num_samples
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data2 = ds.LibriSpeechDataset(DATA_DIR, num_samples=500)
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num_iter2 = 0
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for _ in data2.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_iter2 += 1
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assert num_iter2 == 500
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# case 3: test repeat
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data3 = ds.LibriSpeechDataset(DATA_DIR, num_samples=200)
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data3 = data3.repeat(5)
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num_iter3 = 0
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for _ in data3.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_iter3 += 1
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assert num_iter3 == 1000
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# case 4: test batch with drop_remainder=False
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data4 = ds.LibriSpeechDataset(DATA_DIR, num_samples=100)
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assert data4.get_dataset_size() == 100
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assert data4.get_batch_size() == 1
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data4 = data4.batch(batch_size=7) # drop_remainder is default to be False
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assert data4.get_dataset_size() == 15
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assert data4.get_batch_size() == 7
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# num_iter4 = 0
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# for _ in data4.create_dict_iterator(num_epochs=1,output_numpy=True):
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# num_iter4 += 1
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# assert num_iter4 == 15
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# case 5: test batch with drop_remainder=True
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data5 = ds.LibriSpeechDataset(DATA_DIR, num_samples=100)
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assert data5.get_dataset_size() == 100
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assert data5.get_batch_size() == 1
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data5 = data5.batch(batch_size=7, drop_remainder=True) # the rest of incomplete batch will be dropped
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assert data5.get_dataset_size() == 14
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assert data5.get_batch_size() == 7
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# num_iter5 = 0
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# for _ in data5.create_dict_iterator(num_epochs=1,output_numpy=True):
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# num_iter5 += 1
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# assert num_iter5 == 14
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def test_librispeech_sequential_sampler():
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"""
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Test LibriSpeechDataset with SequentialSampler
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"""
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logger.info("Test LibriSpeechDataset Op with SequentialSampler")
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num_samples = 50
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sampler = ds.SequentialSampler(num_samples=num_samples)
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data1 = ds.LibriSpeechDataset(DATA_DIR, sampler=sampler)
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data2 = ds.LibriSpeechDataset(DATA_DIR, shuffle=False, num_samples=num_samples)
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label_list1, label_list2 = [], []
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num_iter = 0
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
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data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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label_list1.append(item1["utterance"])
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label_list2.append(item2["utterance"])
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num_iter += 1
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np.testing.assert_array_equal(label_list1, label_list2)
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assert num_iter == num_samples
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def test_librispeech_exception():
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"""
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Test error cases for LibriSpeechDataset
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"""
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logger.info("Test error cases for LibriSpeechDataset")
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error_msg_1 = "sampler and shuffle cannot be specified at the same time"
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with pytest.raises(RuntimeError, match=error_msg_1):
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ds.LibriSpeechDataset(DATA_DIR, shuffle=False, sampler=ds.PKSampler(3))
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error_msg_2 = "sampler and sharding cannot be specified at the same time"
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with pytest.raises(RuntimeError, match=error_msg_2):
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ds.LibriSpeechDataset(DATA_DIR, sampler=ds.PKSampler(3), num_shards=2, shard_id=0)
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error_msg_3 = "num_shards is specified and currently requires shard_id as well"
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with pytest.raises(RuntimeError, match=error_msg_3):
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ds.LibriSpeechDataset(DATA_DIR, num_shards=10)
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error_msg_4 = "shard_id is specified but num_shards is not"
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with pytest.raises(RuntimeError, match=error_msg_4):
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ds.LibriSpeechDataset(DATA_DIR, shard_id=0)
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error_msg_5 = "Input shard_id is not within the required interval"
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with pytest.raises(ValueError, match=error_msg_5):
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ds.LibriSpeechDataset(DATA_DIR, num_shards=5, shard_id=-1)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.LibriSpeechDataset(DATA_DIR, num_shards=5, shard_id=5)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.LibriSpeechDataset(DATA_DIR, num_shards=2, shard_id=5)
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error_msg_6 = "num_parallel_workers exceeds"
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with pytest.raises(ValueError, match=error_msg_6):
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ds.LibriSpeechDataset(DATA_DIR, shuffle=False, num_parallel_workers=0)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.LibriSpeechDataset(DATA_DIR, shuffle=False, num_parallel_workers=256)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.LibriSpeechDataset(DATA_DIR, shuffle=False, num_parallel_workers=-2)
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error_msg_7 = "Argument shard_id"
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with pytest.raises(TypeError, match=error_msg_7):
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ds.LibriSpeechDataset(DATA_DIR, num_shards=2, shard_id="0")
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def exception_func(item):
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raise Exception("Error occur!")
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error_msg_8 = "The corresponding data files"
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with pytest.raises(RuntimeError, match=error_msg_8):
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data = ds.LibriSpeechDataset(DATA_DIR)
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data = data.map(operations=exception_func, input_columns=["waveform"], num_parallel_workers=1)
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for _ in data.__iter__():
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pass
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with pytest.raises(RuntimeError, match=error_msg_8):
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data = ds.LibriSpeechDataset(DATA_DIR)
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data = data.map(operations=vision.Decode(), input_columns=["waveform"], num_parallel_workers=1)
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data = data.map(operations=exception_func, input_columns=["waveform"], num_parallel_workers=1)
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for _ in data.__iter__():
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pass
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with pytest.raises(RuntimeError, match=error_msg_8):
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data = ds.LibriSpeechDataset(DATA_DIR)
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data = data.map(operations=exception_func, input_columns=["waveform"], num_parallel_workers=1)
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for _ in data.__iter__():
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pass
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def test_librispeech_visualize(plot=False):
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"""
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Visualize LibriSpeechDataset results
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"""
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logger.info("Test LibriSpeechDataset visualization")
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data1 = ds.LibriSpeechDataset(DATA_DIR, num_samples=10, shuffle=False)
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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audio = item["waveform"]
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sample_rate = item["sample_rate"]
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speaker_id = item["speaker_id"];
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chapter_id = item["chapter_id"];
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utterance_id = item["utterance_id"];
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assert isinstance(audio, np.ndarray)
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assert audio.dtype == np.float64
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assert sample_rate.dtype == np.uint32
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assert speaker_id.dtype == np.uint32
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assert chapter_id.dtype == np.uint32
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assert utterance_id.dtype == np.uint32
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num_iter += 1
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assert num_iter == 10
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def test_librispeech_usage():
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"""
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Validate LibriSpeechDataset audio readings
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"""
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logger.info("Test LibriSpeechDataset usage flag")
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def test_config(usage, librispeech_path=None):
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librispeech_path = DATA_DIR if librispeech_path is None else librispeech_path
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try:
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data = ds.LibriSpeechDataset(librispeech_path, usage=usage, shuffle=False)
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num_rows = 0
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for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_rows += 1
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except (ValueError, TypeError, RuntimeError) as e:
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return str(e)
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return num_rows
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assert test_config("dev-clean") == 2703
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assert test_config("dev-other") == 2864
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assert "Input usage is not within the valid set of ['dev-clean', 'dev-other', 'test-clean', 'test-other', 'train-clean-100', 'train-clean-360', 'train-other-500']." in test_config("invalid")
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assert "Argument usage with value ['list'] is not of type [<class 'str'>]" in test_config(["list"])
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all_files_path = None
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if all_files_path is not None:
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assert test_config("dev-clean", all_files_path) == 2703
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assert test_config("dev-other", all_files_path) == 2864
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assert ds.LibriSpeechDataset(all_files_path, usage="dev-clean").get_dataset_size() == 2703
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assert ds.LibrispeechDataset(all_files_path, usage="dev-other").get_dataset_size() == 2864
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if __name__ == '__main__':
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test_librispeech_basic()#pass
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test_librispeech_sequential_sampler()#pass
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test_librispeech_exception()#pass
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test_librispeech_visualize(plot=True)#pass
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test_librispeech_usage()#pass
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