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* Fix an error in TDNN-LSTM training. * WIP: Refactoring * Refactor transformer.py * Remove unused code. * Minor fixes. * Fix decoder padding mask. * Add MMI training with word pieces. * Remove unused files. * Minor fixes. * Refactoring. * Minor fixes. * Use pre-computed alignments in LF-MMI training. * Minor fixes. * Update decoding script. * Add doc about how to check and use extracted alignments. * Fix style issues. * Fix typos. * Fix style issues. * Disable macOS tests for now.
42 lines
1.4 KiB
Python
Executable File
42 lines
1.4 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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from pathlib import Path
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from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
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from icefall.lexicon import UniqLexicon
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ICEFALL_DIR = Path(__file__).resolve().parent.parent
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def test():
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lang_dir = ICEFALL_DIR / "egs/librispeech/ASR/data/lang_bpe"
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if not lang_dir.is_dir():
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return
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compiler = BpeCtcTrainingGraphCompiler(lang_dir)
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ids = compiler.texts_to_ids(["HELLO", "WORLD ZZZ"])
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compiler.compile(ids)
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lexicon = UniqLexicon(lang_dir, uniq_filename="lexicon.txt")
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ids0 = lexicon.words_to_piece_ids(["HELLO"])
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assert ids[0] == ids0.values().tolist()
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ids1 = lexicon.words_to_piece_ids(["WORLD", "ZZZ"])
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assert ids[1] == ids1.values().tolist()
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