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@ -823,46 +823,9 @@ def main():
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# we need cut ids to display recognition results.
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args.return_cuts = True
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librispeech = LibriSpeechAsrDataModule(args)
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#test_clean_cuts = librispeech.test_clean_cuts(option='male')
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#test_other_cuts = librispeech.test_other_cuts(option='male')
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if 0:
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test_clean_cuts = librispeech.test_clean_user(option=option)
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test_other_cuts = librispeech.test_other_user(option=option)
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test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
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test_other_dl = librispeech.test_dataloaders(test_other_cuts)
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test_sets = [f"test-clean", f"test-other"]
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test_dl = [test_clean_dl, test_other_dl]
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if 0:
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option = 'big'
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test_clean_cuts = librispeech.test_clean_user(option=option)
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test_other_cuts = librispeech.test_other_user(option=option)
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test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
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test_other_dl = librispeech.test_dataloaders(test_other_cuts)
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test_sets = [f"test-clean_sampling"]
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test_dl = [test_clean_dl]
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#test_sets = [f"test-other_sampling"]
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#test_dl = [test_other_dl]
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#test_sets = [f"test-clean_sampling", f"test-other_sampling"]
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#test_dl = [test_clean_dl, test_other_dl]
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if 0:
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option = '6938'
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test_clean_cuts = librispeech.vox_cuts(option=option)
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test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
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test_sets = [f"test-clean_sampling"]
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test_dl = [test_clean_dl]
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if 1:
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test_clean_cuts = librispeech.userlibri_cuts(option=params.spk_id)
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test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
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test_sets = [f"{params.spk_id}"]
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test_dl = [test_clean_dl]
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tedlium = TedLiumAsrDataModule(args)
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test_cuts = tedlium.test_cuts()
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test_dl = tedlium.train_dataloaders(train_cuts)
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for test_set, test_dl in zip(test_sets, test_dl):
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results_dict = decode_dataset(
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