add_kenlm.py

import argparse
from transformers import AutoProcessor
from transformers import Wav2Vec2ProcessorWithLM
from pyctcdecode import build_ctcdecoder


def main(args):
    processor = AutoProcessor.from_pretrained(args.model_name_or_path)
    vocab_dict = processor.tokenizer.get_vocab()
    sorted_vocab_dict = {
        k.lower(): v for k, v in sorted(vocab_dict.items(), key=lambda item: item[1])
    }
    decoder = build_ctcdecoder(
        labels=list(sorted_vocab_dict.keys()),
        kenlm_model_path=args.kenlm_model_path,
    )
    processor_with_lm = Wav2Vec2ProcessorWithLM(
        feature_extractor=processor.feature_extractor,
        tokenizer=processor.tokenizer,
        decoder=decoder,
    )
    processor_with_lm.save_pretrained(args.model_name_or_path)
    print(
        f"Run: ~/bin/build_binary language_model/*.arpa language_model/5gram.bin -T $(pwd) && rm language_model/*.arpa")


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--model_name_or_path', default="./", help='Model name or path. Defaults to ./')
    parser.add_argument('--kenlm_model_path', required=True, help='Path to KenLM arpa file.')
    args = parser.parse_args()
    return args


if __name__ == "__main__":
    main(parse_args())