mirror of
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109 lines
2.9 KiB
Python
109 lines
2.9 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# Copyright 2021 Xiaomi Corp. (authors: Mingshuang Luo)
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# 2022 Xiaomi Corp. (authors: Weiji Zhuang)
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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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"""
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This script takes as input "text", which refers to the transcript file for
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WenetSpeech:
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- text
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and generates the output file text_word_segmentation which is implemented
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with word segmenting:
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- text_words_segmentation
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"""
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import argparse
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from multiprocessing import Pool
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import jieba
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import paddle
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from tqdm import tqdm
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# In PaddlePaddle 2.x, dynamic graph mode is turned on by default,
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# and 'data()' is only supported in static graph mode. So if you
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# want to use this api, should call 'paddle.enable_static()' before
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# this api to enter static graph mode.
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# paddle.enable_static()
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# paddle.disable_signal_handler()
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jieba.enable_paddle()
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def get_parser():
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parser = argparse.ArgumentParser(
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description="Chinese Word Segmentation for text",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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parser.add_argument(
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"--num-process",
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"-n",
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default=20,
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type=int,
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help="the number of processes",
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)
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parser.add_argument(
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"--input-file",
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"-i",
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default="data/lang_char/text",
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type=str,
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help="the input text file for WenetSpeech",
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)
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parser.add_argument(
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"--output-file",
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"-o",
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default="data/lang_char/text_words_segmentation",
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type=str,
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help="the text implemented with words segmenting for WenetSpeech",
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)
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return parser
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def cut(lines):
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if lines is not None:
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cut_lines = jieba.cut(lines, use_paddle=True)
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return [i for i in cut_lines]
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else:
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return None
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def main():
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parser = get_parser()
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args = parser.parse_args()
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num_process = args.num_process
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input_file = args.input_file
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output_file = args.output_file
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# parallel mode does not support use_paddle
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# jieba.enable_parallel(num_process)
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with open(input_file, "r", encoding="utf-8") as fr:
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lines = fr.readlines()
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with Pool(processes=num_process) as p:
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new_lines = list(tqdm(p.imap(cut, lines), total=len(lines)))
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with open(output_file, "w", encoding="utf-8") as fw:
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for line in new_lines:
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fw.write(" ".join(line) + "\n")
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if __name__ == "__main__":
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main()
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