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90 lines
2.6 KiB
Python
Executable File
90 lines
2.6 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# See ../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Runt his file using one of the following two ways:
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# (1) python3 ./test/test_ali.py
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# (2) pytest ./test/test_ali.py
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# The purpose of this file is to show that if we build a mask
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# from alignments and add it to a randomly generated nnet_output,
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# we can decode the correct transcript.
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from pathlib import Path
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from lhotse import CutSet, load_manifest
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from lhotse.dataset import K2SpeechRecognitionDataset, SimpleCutSampler
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from lhotse.dataset.collation import collate_custom_field
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from torch.utils.data import DataLoader
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ICEFALL_DIR = Path(__file__).resolve().parent.parent
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egs_dir = ICEFALL_DIR / "egs/librispeech/ASR"
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lang_dir = egs_dir / "data/lang_bpe_500"
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cuts_json = egs_dir / "data/ali/cuts_dev-clean.json.gz"
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def data_exists():
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return cuts_json.exists() and lang_dir.exists()
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def get_dataloader():
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cuts = load_manifest(cuts_json)
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print(cuts[0])
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cuts = cuts.with_features_path_prefix(egs_dir)
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sampler = SimpleCutSampler(
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cuts,
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max_duration=10,
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shuffle=False,
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)
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dataset = K2SpeechRecognitionDataset(return_cuts=True)
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dl = DataLoader(
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dataset,
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sampler=sampler,
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batch_size=None,
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num_workers=1,
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persistent_workers=False,
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)
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return dl
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def test():
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if not data_exists():
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return
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dl = get_dataloader()
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for batch in dl:
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supervisions = batch["supervisions"]
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cuts = supervisions["cut"]
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labels_alignment, labels_alignment_length = collate_custom_field(
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CutSet.from_cuts(cuts), "labels_alignment"
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)
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(
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aux_labels_alignment,
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aux_labels_alignment_length,
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) = collate_custom_field(CutSet.from_cuts(cuts), "aux_labels_alignment")
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print(labels_alignment)
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print(aux_labels_alignment)
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print(labels_alignment_length)
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print(aux_labels_alignment_length)
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break
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if __name__ == "__main__":
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test()
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