47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
from logging import root
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import pytest
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import torch
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import numpy as np
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from enhancer.utils.io import Audio
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from enhancer.utils.config import Files
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from enhancer.utils.fileprocessor import Fileprocessor
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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.pt_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("vctk","tests/data/vctk/clean_testset_wav",
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"tests/data/vctk/noisy_testset_wav",48000)
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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",16000)
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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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fp = Fileprocessor.from_name("undefined","clean_dir","noisy_dir",16000).prepare_matching_dict()
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