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* use piper_phonemize as text tokenizer in ljspeech recipe * modify usage of tokenizer in vits/train.py * update docs
63 lines
1.9 KiB
Python
Executable File
63 lines
1.9 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (authors: Zengwei Yao)
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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 file generates the file that maps tokens to IDs.
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"""
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import argparse
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import logging
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from pathlib import Path
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from typing import Dict
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from piper_phonemize import get_espeak_map
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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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"--tokens",
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type=Path,
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default=Path("data/tokens.txt"),
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help="Path to the dict that maps the text tokens to IDs",
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)
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return parser.parse_args()
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def get_token2id(filename: Path) -> Dict[str, int]:
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"""Get a dict that maps token to IDs, and save it to the given filename."""
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all_tokens = get_espeak_map() # token: [token_id]
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all_tokens = {token: token_id[0] for token, token_id in all_tokens.items()}
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# sort by token_id
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all_tokens = sorted(all_tokens.items(), key=lambda x: x[1])
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with open(filename, "w", encoding="utf-8") as f:
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for token, token_id in all_tokens:
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f.write(f"{token} {token_id}\n")
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if __name__ == "__main__":
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formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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logging.basicConfig(format=formatter, level=logging.INFO)
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args = get_args()
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out_file = Path(args.tokens)
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get_token2id(out_file)
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