50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
import numpy as np
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import pytest
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import torch
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from enhancer.data.fileprocessor import Fileprocessor
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from enhancer.utils.io import Audio
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def test_io_channel():
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input_audio = np.random.rand(2, 32000)
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audio = Audio(mono=True, return_tensor=False)
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output_audio = audio(input_audio)
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assert output_audio.shape[0] == 1
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def test_io_resampling():
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input_audio = np.random.rand(1, 32000)
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resampled_audio = Audio.resample_audio(input_audio, 16000, 8000)
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input_audio = torch.rand(1, 32000)
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resampled_audio_pt = Audio.resample_audio(input_audio, 16000, 8000)
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assert resampled_audio.shape[1] == resampled_audio_pt.size(1) == 16000
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def test_fileprocessor_vctk():
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fp = Fileprocessor.from_name(
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"vctk",
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"tests/data/vctk/clean_testset_wav",
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"tests/data/vctk/noisy_testset_wav",
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)
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matching_dict = fp.prepare_matching_dict()
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assert len(matching_dict) == 2
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@pytest.mark.parametrize("dataset_name", ["vctk", "dns-2020"])
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def test_fileprocessor_names(dataset_name):
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fp = Fileprocessor.from_name(dataset_name, "clean_dir", "noisy_dir")
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assert hasattr(fp.matching_function, "__call__")
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def test_fileprocessor_invaliname():
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with pytest.raises(ValueError):
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_ = Fileprocessor.from_name(
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"undefined", "clean_dir", "noisy_dir", 16000
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).prepare_matching_dict()
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