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* init * isort formatted * minor updates * Create shared * Update prepare_tokens_vctk.py * Update prepare_tokens_vctk.py * Update prepare_tokens_vctk.py * Update prepare.sh * updated * Update train.py * Update train.py * Update tts_datamodule.py * Update train.py * Update train.py * Update train.py * Update train.py * Update train.py * Update train.py * fixed formatting issue * Update infer.py * removed redundant files * Create monotonic_align * removed redundant files * created symlinks * Update prepare.sh * minor adjustments * Create requirements_tts.txt * Update requirements_tts.txt added version constraints * Update infer.py * Update infer.py * Update infer.py * updated docs * Update export-onnx.py * Update export-onnx.py * Update test_onnx.py * updated requirements.txt * Update test_onnx.py * Update test_onnx.py * docs updated * docs fixed * minor updates
109 lines
3.4 KiB
Python
109 lines
3.4 KiB
Python
# 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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from typing import Dict, List
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import g2p_en
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import tacotron_cleaner.cleaners
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from utils import intersperse
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class Tokenizer(object):
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def __init__(self, tokens: str):
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"""
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Args:
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tokens: the file that maps tokens to ids
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"""
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# Parse token file
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self.token2id: Dict[str, int] = {}
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with open(tokens, "r", encoding="utf-8") as f:
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for line in f.readlines():
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info = line.rstrip().split()
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if len(info) == 1:
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# case of space
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token = " "
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id = int(info[0])
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else:
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token, id = info[0], int(info[1])
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self.token2id[token] = id
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self.blank_id = self.token2id["<blk>"]
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self.oov_id = self.token2id["<unk>"]
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self.vocab_size = len(self.token2id)
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self.g2p = g2p_en.G2p()
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def texts_to_token_ids(self, texts: List[str], intersperse_blank: bool = True):
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"""
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Args:
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texts:
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A list of transcripts.
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intersperse_blank:
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Whether to intersperse blanks in the token sequence.
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Returns:
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Return a list of token id list [utterance][token_id]
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"""
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token_ids_list = []
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for text in texts:
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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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tokens = self.g2p(text)
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token_ids = []
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for t in tokens:
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if t in self.token2id:
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token_ids.append(self.token2id[t])
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else:
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token_ids.append(self.oov_id)
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if intersperse_blank:
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token_ids = intersperse(token_ids, self.blank_id)
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token_ids_list.append(token_ids)
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return token_ids_list
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def tokens_to_token_ids(
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self, tokens_list: List[str], intersperse_blank: bool = True
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):
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"""
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Args:
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tokens_list:
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A list of token list, each corresponding to one utterance.
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intersperse_blank:
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Whether to intersperse blanks in the token sequence.
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Returns:
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Return a list of token id list [utterance][token_id]
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"""
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token_ids_list = []
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for tokens in tokens_list:
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token_ids = []
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for t in tokens:
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if t in self.token2id:
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token_ids.append(self.token2id[t])
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else:
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token_ids.append(self.oov_id)
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if intersperse_blank:
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token_ids = intersperse(token_ids, self.blank_id)
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token_ids_list.append(token_ids)
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return token_ids_list
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