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#!/usr/bin/env python3
# Copyright 2023 Xiaomi Corp. (authors: Zengwei Yao)
#
# See ../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
import k2
import sentencepiece as spm
import torch
from icefall.lexicon import Lexicon
from icefall.utils import parse_bpe_timestamps_and_texts, parse_timestamps_and_texts
ICEFALL_DIR = Path(__file__).resolve().parent.parent
def test_parse_bpe_timestamps_and_texts():
lang_dir = ICEFALL_DIR / "egs/librispeech/ASR/data/lang_bpe_500"
if not lang_dir.is_dir():
print(f"{lang_dir} does not exist.")
return
sp = spm.SentencePieceProcessor()
sp.load(str(lang_dir / "bpe.model"))
text_1 = "HELLO WORLD"
token_ids_1 = sp.encode(text_1, out_type=int)
# out_type=str: ['_HE', 'LL', 'O', '_WORLD']
# out_type=int: [22, 58, 24, 425]
# [22, 22, 58, 24, 0, 0, 425, 425, 425, 0, 0]
labels_1 = (
token_ids_1[0:1] * 2
+ token_ids_1[1:3]
+ [0] * 2
+ token_ids_1[3:4] * 3
+ [0] * 2
)
# [22, 0, 58, 24, 0, 0, 425, 0, 0, 0, 0, -1]
aux_labels_1 = (
token_ids_1[0:1]
+ [0]
+ token_ids_1[1:3]
+ [0] * 2
+ token_ids_1[3:4]
+ [0] * 4
+ [-1]
)
fsa_1 = k2.linear_fsa(labels_1)
fsa_1.aux_labels = torch.tensor(aux_labels_1).to(torch.int32)
text_2 = "SAY GOODBYE"
token_ids_2 = sp.encode(text_2, out_type=int)
# out_type=str: ['_SAY', '_GOOD', 'B', 'Y', 'E']
# out_type=int: [289, 286, 41, 16, 11]
# [289, 0, 0, 286, 286, 41, 16, 11, 0, 0]
labels_2 = (
token_ids_2[0:1] + [0] * 2 + token_ids_2[1:2] * 2 + token_ids_2[2:5] + [0] * 2
)
# [289, 0, 0, 286, 0, 41, 16, 11, 0, 0, -1]
aux_labels_2 = (
token_ids_2[0:1]
+ [0] * 2
+ token_ids_2[1:2]
+ [0]
+ token_ids_2[2:5]
+ [0] * 2
+ [-1]
)
fsa_2 = k2.linear_fsa(labels_2)
fsa_2.aux_labels = torch.tensor(aux_labels_2).to(torch.int32)
fsa_vec = k2.create_fsa_vec([fsa_1, fsa_2])
utt_index_pairs, utt_words = parse_bpe_timestamps_and_texts(fsa_vec, sp)
assert utt_index_pairs[0] == [(0, 3), (6, 8)], utt_index_pairs[0]
assert utt_words[0] == ["HELLO", "WORLD"], utt_words[0]
assert utt_index_pairs[1] == [(0, 0), (3, 7)], utt_index_pairs[1]
assert utt_words[1] == ["SAY", "GOODBYE"], utt_words[1]
def test_parse_timestamps_and_texts():
lang_dir = ICEFALL_DIR / "egs/librispeech/ASR/data/lang_bpe_500"
if not lang_dir.is_dir():
print(f"{lang_dir} does not exist.")
return
lexicon = Lexicon(lang_dir)
sp = spm.SentencePieceProcessor()
sp.load(str(lang_dir / "bpe.model"))
word_table = lexicon.word_table
text_1 = "HELLO WORLD"
token_ids_1 = sp.encode(text_1, out_type=int)
# out_type=str: ['_HE', 'LL', 'O', '_WORLD']
# out_type=int: [22, 58, 24, 425]
word_ids_1 = [word_table[s] for s in text_1.split()] # [79677, 196937]
# [22, 22, 58, 24, 0, 0, 425, 425, 425, 0, 0]
labels_1 = (
token_ids_1[0:1] * 2
+ token_ids_1[1:3]
+ [0] * 2
+ token_ids_1[3:4] * 3
+ [0] * 2
)
# [[79677], [], [], [], [], [], [196937], [], [], [], [], []]
aux_labels_1 = [word_ids_1[0:1]] + [[]] * 5 + [word_ids_1[1:2]] + [[]] * 5
fsa_1 = k2.linear_fsa(labels_1)
fsa_1.aux_labels = k2.RaggedTensor(aux_labels_1)
text_2 = "SAY GOODBYE"
token_ids_2 = sp.encode(text_2, out_type=int)
# out_type=str: ['_SAY', '_GOOD', 'B', 'Y', 'E']
# out_type=int: [289, 286, 41, 16, 11]
word_ids_2 = [word_table[s] for s in text_2.split()] # [154967, 72079]
# [289, 0, 0, 286, 286, 41, 16, 11, 0, 0]
labels_2 = (
token_ids_2[0:1] + [0] * 2 + token_ids_2[1:2] * 2 + token_ids_2[2:5] + [0] * 2
)
# [[154967], [], [], [72079], [], [], [], [], [], [], []]
aux_labels_2 = [word_ids_2[0:1]] + [[]] * 2 + [word_ids_2[1:2]] + [[]] * 7
fsa_2 = k2.linear_fsa(labels_2)
fsa_2.aux_labels = k2.RaggedTensor(aux_labels_2)
fsa_vec = k2.create_fsa_vec([fsa_1, fsa_2])
utt_index_pairs, utt_words = parse_timestamps_and_texts(fsa_vec, word_table)
assert utt_index_pairs[0] == [(0, 3), (6, 8)], utt_index_pairs[0]
assert utt_words[0] == ["HELLO", "WORLD"], utt_words[0]
assert utt_index_pairs[1] == [(0, 0), (3, 7)], utt_index_pairs[1]
assert utt_words[1] == ["SAY", "GOODBYE"], utt_words[1]
if __name__ == "__main__":
test_parse_bpe_timestamps_and_texts()
test_parse_timestamps_and_texts()
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