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PaddleSpeech/paddlespeech/dataset/s2t/build_vocab.py

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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""Build vocabulary from manifest files.
Each item in vocabulary file is a character.
"""
import argparse
import functools
import os
import tempfile
from collections import Counter
import jsonlines
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.frontend.utility import BLANK
from paddlespeech.s2t.frontend.utility import SOS
from paddlespeech.s2t.frontend.utility import SPACE
from paddlespeech.s2t.frontend.utility import UNK
from paddlespeech.utils.argparse import add_arguments
from paddlespeech.utils.argparse import print_arguments
def count_manifest(counter, text_feature, manifest_path):
manifest_jsons = []
with jsonlines.open(manifest_path, 'r') as reader:
for json_data in reader:
manifest_jsons.append(json_data)
for line_json in manifest_jsons:
if isinstance(line_json['text'], str):
tokens = text_feature.tokenize(
line_json['text'], replace_space=False)
counter.update(tokens)
else:
assert isinstance(line_json['text'], list)
for text in line_json['text']:
tokens = text_feature.tokenize(text, replace_space=False)
counter.update(tokens)
def dump_text_manifest(fileobj, manifest_path, key='text'):
manifest_jsons = []
with jsonlines.open(manifest_path, 'r') as reader:
for json_data in reader:
manifest_jsons.append(json_data)
for line_json in manifest_jsons:
if isinstance(line_json[key], str):
fileobj.write(line_json[key] + "\n")
else:
assert isinstance(line_json[key], list)
for line in line_json[key]:
fileobj.write(line + "\n")
def build_vocab(manifest_paths="",
vocab_path="examples/librispeech/data/vocab.txt",
unit_type="char",
count_threshold=0,
text_keys='text',
spm_mode="unigram",
spm_vocab_size=0,
spm_model_prefix="",
spm_character_coverage=0.9995):
manifest_paths = [manifest_paths] if isinstance(manifest_paths,
str) else manifest_paths
fout = open(vocab_path, 'w', encoding='utf-8')
fout.write(BLANK + "\n") # 0 will be used for "blank" in CTC
fout.write(UNK + '\n') # <unk> must be 1
if unit_type == 'spm':
# tools/spm_train --input=$wave_data/lang_char/input.txt
# --vocab_size=${nbpe} --model_type=${bpemode}
# --model_prefix=${bpemodel} --input_sentence_size=100000000
import sentencepiece as spm
fp = tempfile.NamedTemporaryFile(mode='w', delete=False)
for manifest_path in manifest_paths:
_text_keys = [text_keys] if type(
text_keys) is not list else text_keys
for text_key in _text_keys:
dump_text_manifest(fp, manifest_path, key=text_key)
fp.close()
# train
spm.SentencePieceTrainer.Train(
input=fp.name,
vocab_size=spm_vocab_size,
model_type=spm_mode,
model_prefix=spm_model_prefix,
input_sentence_size=100000000,
character_coverage=spm_character_coverage)
os.unlink(fp.name)
# encode
text_feature = TextFeaturizer(unit_type, "", spm_model_prefix)
counter = Counter()
for manifest_path in manifest_paths:
count_manifest(counter, text_feature, manifest_path)
count_sorted = sorted(counter.items(), key=lambda x: x[1], reverse=True)
tokens = []
for token, count in count_sorted:
if count < count_threshold:
break
# replace space by `<space>`
token = SPACE if token == ' ' else token
tokens.append(token)
tokens = sorted(tokens)
for token in tokens:
fout.write(token + '\n')
fout.write(SOS + "\n") # <sos/eos>
fout.close()
def define_argparse():
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('unit_type', str, "char", "Unit type, e.g. char, word, spm")
add_arg('count_threshold', int, 0,
"Truncation threshold for char/word counts.Default 0, no truncate.")
add_arg('vocab_path', str,
'examples/librispeech/data/vocab.txt',
"Filepath to write the vocabulary.")
add_arg('manifest_paths', str,
None,
"Filepaths of manifests for building vocabulary. "
"You can provide multiple manifest files.",
nargs='+',
required=True)
add_arg('text_keys', str,
'text',
"keys of the text in manifest for building vocabulary. "
"You can provide multiple k.",
nargs='+')
# bpe
add_arg('spm_vocab_size', int, 0, "Vocab size for spm.")
add_arg('spm_mode', str, 'unigram', "spm model type, e.g. unigram, spm, char, word. only need when `unit_type` is spm")
add_arg('spm_model_prefix', str, "", "spm_model_%(spm_mode)_%(count_threshold), spm model prefix, only need when `unit_type` is spm")
add_arg('spm_character_coverage', float, 0.9995, "character coverage to determine the minimum symbols")
# yapf: disable
args = parser.parse_args()
return args
def main():
args = define_argparse()
print_arguments(args, globals())
build_vocab(**vars(args))
if __name__ == '__main__':
main()