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https://github.com/k2-fsa/icefall.git
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d12e6f098c
* parse timestamps and texts for BPE-based models * parse timestamps (frame indexes) and texts for other cases * add test functions * add parse_fsa_timestamps_and_texts function, test in conformer_ctc3/decode.py * calculate symbol delay for (start, end) timestamps
155 lines
5.0 KiB
Python
Executable File
155 lines
5.0 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (authors: Zengwei Yao)
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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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import k2
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import sentencepiece as spm
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import torch
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from icefall.lexicon import Lexicon
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from icefall.utils import parse_bpe_timestamps_and_texts, parse_timestamps_and_texts
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ICEFALL_DIR = Path(__file__).resolve().parent.parent
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def test_parse_bpe_timestamps_and_texts():
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lang_dir = ICEFALL_DIR / "egs/librispeech/ASR/data/lang_bpe_500"
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if not lang_dir.is_dir():
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print(f"{lang_dir} does not exist.")
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return
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sp = spm.SentencePieceProcessor()
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sp.load(str(lang_dir / "bpe.model"))
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text_1 = "HELLO WORLD"
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token_ids_1 = sp.encode(text_1, out_type=int)
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# out_type=str: ['_HE', 'LL', 'O', '_WORLD']
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# out_type=int: [22, 58, 24, 425]
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# [22, 22, 58, 24, 0, 0, 425, 425, 425, 0, 0]
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labels_1 = (
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token_ids_1[0:1] * 2
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+ token_ids_1[1:3]
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+ [0] * 2
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+ token_ids_1[3:4] * 3
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+ [0] * 2
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)
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# [22, 0, 58, 24, 0, 0, 425, 0, 0, 0, 0, -1]
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aux_labels_1 = (
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token_ids_1[0:1]
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+ [0]
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+ token_ids_1[1:3]
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+ [0] * 2
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+ token_ids_1[3:4]
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+ [0] * 4
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+ [-1]
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)
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fsa_1 = k2.linear_fsa(labels_1)
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fsa_1.aux_labels = torch.tensor(aux_labels_1).to(torch.int32)
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text_2 = "SAY GOODBYE"
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token_ids_2 = sp.encode(text_2, out_type=int)
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# out_type=str: ['_SAY', '_GOOD', 'B', 'Y', 'E']
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# out_type=int: [289, 286, 41, 16, 11]
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# [289, 0, 0, 286, 286, 41, 16, 11, 0, 0]
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labels_2 = (
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token_ids_2[0:1] + [0] * 2 + token_ids_2[1:2] * 2 + token_ids_2[2:5] + [0] * 2
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)
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# [289, 0, 0, 286, 0, 41, 16, 11, 0, 0, -1]
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aux_labels_2 = (
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token_ids_2[0:1]
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+ [0] * 2
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+ token_ids_2[1:2]
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+ [0]
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+ token_ids_2[2:5]
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+ [0] * 2
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+ [-1]
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)
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fsa_2 = k2.linear_fsa(labels_2)
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fsa_2.aux_labels = torch.tensor(aux_labels_2).to(torch.int32)
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fsa_vec = k2.create_fsa_vec([fsa_1, fsa_2])
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utt_index_pairs, utt_words = parse_bpe_timestamps_and_texts(fsa_vec, sp)
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assert utt_index_pairs[0] == [(0, 3), (6, 8)], utt_index_pairs[0]
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assert utt_words[0] == ["HELLO", "WORLD"], utt_words[0]
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assert utt_index_pairs[1] == [(0, 0), (3, 7)], utt_index_pairs[1]
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assert utt_words[1] == ["SAY", "GOODBYE"], utt_words[1]
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def test_parse_timestamps_and_texts():
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lang_dir = ICEFALL_DIR / "egs/librispeech/ASR/data/lang_bpe_500"
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if not lang_dir.is_dir():
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print(f"{lang_dir} does not exist.")
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return
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lexicon = Lexicon(lang_dir)
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sp = spm.SentencePieceProcessor()
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sp.load(str(lang_dir / "bpe.model"))
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word_table = lexicon.word_table
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text_1 = "HELLO WORLD"
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token_ids_1 = sp.encode(text_1, out_type=int)
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# out_type=str: ['_HE', 'LL', 'O', '_WORLD']
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# out_type=int: [22, 58, 24, 425]
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word_ids_1 = [word_table[s] for s in text_1.split()] # [79677, 196937]
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# [22, 22, 58, 24, 0, 0, 425, 425, 425, 0, 0]
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labels_1 = (
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token_ids_1[0:1] * 2
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+ token_ids_1[1:3]
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+ [0] * 2
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+ token_ids_1[3:4] * 3
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+ [0] * 2
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)
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# [[79677], [], [], [], [], [], [196937], [], [], [], [], []]
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aux_labels_1 = [word_ids_1[0:1]] + [[]] * 5 + [word_ids_1[1:2]] + [[]] * 5
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fsa_1 = k2.linear_fsa(labels_1)
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fsa_1.aux_labels = k2.RaggedTensor(aux_labels_1)
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text_2 = "SAY GOODBYE"
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token_ids_2 = sp.encode(text_2, out_type=int)
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# out_type=str: ['_SAY', '_GOOD', 'B', 'Y', 'E']
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# out_type=int: [289, 286, 41, 16, 11]
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word_ids_2 = [word_table[s] for s in text_2.split()] # [154967, 72079]
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# [289, 0, 0, 286, 286, 41, 16, 11, 0, 0]
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labels_2 = (
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token_ids_2[0:1] + [0] * 2 + token_ids_2[1:2] * 2 + token_ids_2[2:5] + [0] * 2
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)
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# [[154967], [], [], [72079], [], [], [], [], [], [], []]
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aux_labels_2 = [word_ids_2[0:1]] + [[]] * 2 + [word_ids_2[1:2]] + [[]] * 7
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fsa_2 = k2.linear_fsa(labels_2)
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fsa_2.aux_labels = k2.RaggedTensor(aux_labels_2)
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fsa_vec = k2.create_fsa_vec([fsa_1, fsa_2])
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utt_index_pairs, utt_words = parse_timestamps_and_texts(fsa_vec, word_table)
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assert utt_index_pairs[0] == [(0, 3), (6, 8)], utt_index_pairs[0]
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assert utt_words[0] == ["HELLO", "WORLD"], utt_words[0]
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assert utt_index_pairs[1] == [(0, 0), (3, 7)], utt_index_pairs[1]
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assert utt_words[1] == ["SAY", "GOODBYE"], utt_words[1]
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if __name__ == "__main__":
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test_parse_bpe_timestamps_and_texts()
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test_parse_timestamps_and_texts()
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