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https://github.com/k2-fsa/icefall.git
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238b45bea8
* initial commit for libriheavy * Data prepare pipeline * Fix train.py * Fix decode.py * Add results * minor fixes * black * black * Incorporate PR https://github.com/k2-fsa/icefall/pull/1269 --------- Co-authored-by: zr_jin <peter.jin.cn@gmail.com>
114 lines
3.0 KiB
Python
Executable File
114 lines
3.0 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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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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# You can install sentencepiece via:
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#
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# pip install sentencepiece
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#
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# Due to an issue reported in
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# https://github.com/google/sentencepiece/pull/642#issuecomment-857972030
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#
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# Please install a version >=0.1.96
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import argparse
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import shutil
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from pathlib import Path
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import sentencepiece as spm
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--lang-dir",
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type=str,
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help="""Input and output directory.
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The generated bpe.model is saved to this directory.
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""",
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)
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parser.add_argument(
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"--byte-fallback",
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action="store_true",
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help="""Whether to enable byte_fallback when training bpe.""",
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)
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parser.add_argument(
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"--character-coverage",
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type=float,
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default=1.0,
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help="Character coverage in vocabulary.",
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)
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parser.add_argument(
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"--transcript",
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type=str,
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help="Training transcript.",
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)
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parser.add_argument(
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"--vocab-size",
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type=int,
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help="Vocabulary size for BPE training",
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)
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return parser.parse_args()
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def main():
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args = get_args()
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vocab_size = args.vocab_size
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lang_dir = Path(args.lang_dir)
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model_type = "unigram"
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model_prefix = f"{lang_dir}/{model_type}_{vocab_size}"
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train_text = args.transcript
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input_sentence_size = 100000000
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user_defined_symbols = ["<blk>", "<sos/eos>"]
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unk_id = len(user_defined_symbols)
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# Note: unk_id is fixed to 2.
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# If you change it, you should also change other
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# places that are using it.
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model_file = Path(model_prefix + ".model")
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if not model_file.is_file():
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spm.SentencePieceTrainer.train(
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input=train_text,
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vocab_size=vocab_size,
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model_type=model_type,
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model_prefix=model_prefix,
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input_sentence_size=input_sentence_size,
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character_coverage=args.character_coverage,
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user_defined_symbols=user_defined_symbols,
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byte_fallback=args.byte_fallback,
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unk_id=unk_id,
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bos_id=-1,
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eos_id=-1,
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)
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else:
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print(f"{model_file} exists - skipping")
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return
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shutil.copyfile(model_file, f"{lang_dir}/bpe.model")
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if __name__ == "__main__":
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main()
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