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https://github.com/k2-fsa/icefall.git
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0622dea30d
* first commit * replace phonimizer with g2p * use Conformer as text encoder * modify training script, clean codes * rename directory * convert text to tokens in data preparation stage * fix tts_datamodule.py * support onnx export and testing the exported onnx model * add doc * add README.md * fix style
60 lines
1.9 KiB
Python
Executable File
60 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 reads the texts in given manifest and save the new cuts with phoneme tokens.
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"""
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import logging
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from pathlib import Path
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import g2p_en
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import tacotron_cleaner.cleaners
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from lhotse import CutSet, load_manifest
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def prepare_tokens_ljspeech():
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output_dir = Path("data/spectrogram")
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prefix = "ljspeech"
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suffix = "jsonl.gz"
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partition = "all"
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cut_set = load_manifest(output_dir / f"{prefix}_cuts_{partition}.{suffix}")
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g2p = g2p_en.G2p()
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new_cuts = []
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for cut in cut_set:
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# Each cut only contains one supervision
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assert len(cut.supervisions) == 1, len(cut.supervisions)
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text = cut.supervisions[0].normalized_text
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# Text normalization
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text = tacotron_cleaner.cleaners.custom_english_cleaners(text)
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# Convert to phonemes
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cut.tokens = g2p(text)
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new_cuts.append(cut)
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new_cut_set = CutSet.from_cuts(new_cuts)
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new_cut_set.to_file(output_dir / f"{prefix}_cuts_with_tokens_{partition}.{suffix}")
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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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prepare_tokens_ljspeech()
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