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1fe211ffe5
* fix pipeline builder when model is not supported * fix ci & skip --------- Co-authored-by: suluyan.sly@alibaba-inc.com <suluyan.sly@alibaba-inc.com>
92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import shutil
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import tempfile
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import unittest
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from modelscope.metainfo import Trainers
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from modelscope.msdatasets import MsDataset
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from modelscope.preprocessors.audio import AudioBrainPreprocessor
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from modelscope.trainers import build_trainer
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from modelscope.utils.test_utils import test_level
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MIX_SPEECH_FILE = 'data/test/audios/mix_speech.wav'
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S1_SPEECH_FILE = 'data/test/audios/s1_speech.wav'
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S2_SPEECH_FILE = 'data/test/audios/s2_speech.wav'
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class TestSeparationTrainer(unittest.TestCase):
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def setUp(self):
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self.tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(self.tmp_dir):
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os.makedirs(self.tmp_dir)
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self.model_id = 'damo/speech_mossformer_separation_temporal_8k'
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csv_path = os.path.join(self.tmp_dir, 'test.csv')
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mix_path = os.path.join(os.getcwd(), MIX_SPEECH_FILE)
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s1_path = os.path.join(os.getcwd(), S1_SPEECH_FILE)
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s2_path = os.path.join(os.getcwd(), S2_SPEECH_FILE)
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with open(csv_path, 'w') as w:
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w.write(f'id,mix_wav:FILE,s1_wav:FILE,s2_wav:FILE\n'
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f'0,{mix_path},{s1_path},{s2_path}\n')
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self.dataset = MsDataset.load(
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'csv', data_files={
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'test': [csv_path]
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}).to_torch_dataset(
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preprocessors=[
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AudioBrainPreprocessor(
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takes='mix_wav:FILE', provides='mix_sig'),
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AudioBrainPreprocessor(
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takes='s1_wav:FILE', provides='s1_sig'),
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AudioBrainPreprocessor(
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takes='s2_wav:FILE', provides='s2_sig')
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],
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to_tensor=False)
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def tearDown(self):
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shutil.rmtree(self.tmp_dir, ignore_errors=True)
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super().tearDown()
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@unittest.skip
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def test_trainer(self):
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kwargs = dict(
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model=self.model_id,
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train_dataset=self.dataset,
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eval_dataset=self.dataset,
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max_epochs=2,
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work_dir=self.tmp_dir)
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trainer = build_trainer(
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Trainers.speech_separation, default_args=kwargs)
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# model placement
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trainer.model.load_check_point(device=trainer.device)
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trainer.train()
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logging_path = os.path.join(self.tmp_dir, 'train_log.txt')
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self.assertTrue(
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os.path.exists(logging_path),
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f'Cannot find logging file {logging_path}')
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save_dir = os.path.join(self.tmp_dir, 'save')
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checkpoint_dirs = os.listdir(save_dir)
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self.assertEqual(
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len(checkpoint_dirs), 2, f'Cannot find checkpoint in {save_dir}!')
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@unittest.skip
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def test_eval(self):
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kwargs = dict(
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model=self.model_id,
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train_dataset=None,
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eval_dataset=self.dataset,
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max_epochs=2,
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work_dir=self.tmp_dir)
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trainer = build_trainer(
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Trainers.speech_separation, default_args=kwargs)
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result = trainer.evaluate(None)
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self.assertTrue('si-snr' in result)
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if __name__ == '__main__':
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unittest.main()
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