generate_vocab_file_aishell.py

#! python
# -*- coding: utf-8 -*-
# Author: kun
# @Time: 2019-10-30 14:04

# ! python
# -*- coding: utf-8 -*-
# Author: kun
# @Time: 2019-10-29 20:54


import argparse
import logging
from collections import Counter


def main(args):
    if args.mode == "subword":
        logging.warn("Subword model is based on `sentencepiece`.")

        import sentencepiece as splib

        cmd = ("--input={} --model_prefix={} --model_type=bpe "
               "--vocab_size={} --character_coverage={} "
               "--pad_id=0 --eos_id=1 --unk_id=2 --bos_id=-1 "
               "--eos_piece=<eos> --remove_extra_whitespaces=true".format(
            args.input_file, args.output_file,
            args.vocab_size, args.character_coverage))

        splib.SentencePieceTrainer.Train(cmd)
    else:
        with open(args.input_file, "r") as f:
            lines = [line.strip("\r\n ") for line in f]
        counter = Counter()
        if args.mode == "word":
            for line in lines:
                counter.update(line.split())
            # In word mode, vocab_list is sorted by frequency
            # Only selected top `vocab_size` vocabularies
            vocab_list = sorted(
                counter.keys(), key=lambda k: counter[k], reverse=True)[:args.vocab_size]
        elif args.mode == "character":
            for line in lines:
                line = line.strip('\n')
                aishell_word_list = line.split(' ')
                new_list = []
                for word in aishell_word_list[1:]:  # 去除idx
                    if word != ' ' and word != '':
                        if len(word) == 1:
                            new_list.append(word)
                        else:
                            for char in word:
                                new_list.append(char)
                new_line = ' '.join(new_list).strip(' ')
                # print(line.split(' '))
                # print(new_line.split(" "))
                # counter.update(line)
                counter.update(new_line)
            # In character mode, vocab_list is sorted in alphabetical order
            vocab_list = sorted(counter)

        logging.info("Collected totally {} vocabularies.".format(len(counter)))
        logging.info("Selected {} vocabularies.".format(len(vocab_list)))

        with open(args.output_file, "w") as f:
            f.write("\n".join(vocab_list))


if __name__ == "__main__":
    logging.getLogger().setLevel(logging.INFO)

    parser = argparse.ArgumentParser(
        "Utility script to generate `vocab_file` needed by text encoder.")
    parser.add_argument("--input_file", required=True)
    parser.add_argument(
        "--mode", choices=["character", "word", "subword"], required=True)
    parser.add_argument("--output_file", required=True)
    parser.add_argument("--vocab_size", type=int, default=5000)
    parser.add_argument("--character_coverage", type=float, default=1)

    args = parser.parse_args()

    if args.mode != "subword":
        logging.warn(
            "`character_coverage` is not used in `word` and `character` mode.")
    if args.mode == "character":
        logging.warn("`vocab_size` is not used in `character` mode.")

    main(args)