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@ -28,7 +28,7 @@ import k2
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import sentencepiece as spm
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import torch
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import torch.nn as nn
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from asr_datamodule import LibriSpeechAsrDataModule
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from asr_datamodule import TedAsrDataModule
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from conformer import Conformer
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from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
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@ -731,7 +731,7 @@ def save_results(
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@torch.no_grad()
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def main():
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parser = get_parser()
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LibriSpeechAsrDataModule.add_arguments(parser)
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TedAsrDataModule.add_arguments(parser)
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args = parser.parse_args()
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args.exp_dir = Path(args.exp_dir)
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args.lang_dir = Path(args.lang_dir)
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@ -960,13 +960,22 @@ 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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tedlium = TedAsrDataModule(args)
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test_clean_cuts = librispeech.test_clean_cuts()
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test_other_cuts = librispeech.test_other_cuts()
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#test_clean_cuts = librispeech.test_clean_cuts()
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#test_other_cuts = librispeech.test_other_cuts()
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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_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 = ["test-clean", "test-other"]
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#test_dl = [test_clean_dl, test_other_dl]
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dev_cuts = tedlium.dev_cuts()
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test_cuts = tedlium.test_cuts()
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dev_dl = tedlium.valid_dataloaders(test_clean_cuts)
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test_dl = tedlium.test_dataloaders(test_other_cuts)
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test_sets = ["test-clean", "test-other"]
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test_dl = [test_clean_dl, test_other_dl]
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