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# coding=utf-8
# Copyright 2023 The Kakao Enterprise Authors, the MMS-TTS Authors and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization class for VITS."""
import json
import os
import re
from typing import Any, Dict, List, Optional, Tuple, Union
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils import is_phonemizer_available, logging
from transformers.utils import get_file_from_repo
if is_phonemizer_available():
import phonemizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.json",
}
def is_symbol(ch):
return ch in "!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~"
class BertVits2Tokenizer(PreTrainedTokenizer):
"""
Construct a VITS tokenizer. Also supports MMS-TTS.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
language (`str`, *optional*):
Language identifier.
add_blank (`bool`, *optional*, defaults to `True`):
Whether to insert token id 0 in between the other tokens.
normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the input text by removing all casing and punctuation.
phonemize (`bool`, *optional*, defaults to `True`):
Whether to convert the input text into phonemes.
is_uroman (`bool`, *optional*, defaults to `False`):
Whether the `uroman` Romanizer needs to be applied to the input text prior to tokenizing.
"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = [
"input_ids",
# "input_tones",
"attention_mask",
]
def __init__(
self,
vocab_file,
pad_token="<pad>",
unk_token="<unk>",
space_token=None,
languages=None,
add_blank=True,
**kwargs,
) -> None:
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
self.languages = languages
self.add_blank = add_blank
super().__init__(
pad_token=pad_token,
unk_token=unk_token,
space_token=space_token,
languages=languages,
add_blank=add_blank,
**kwargs,
)
@property
def vocab_size(self):
return len(self.encoder)
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def zh_g2p(self, text: str) -> Tuple[str, List[int], List[int]]:
"""Converts a string of Chinese text into a list of phonemes and tones."""
from pypinyin import lazy_pinyin, Style
g2p_file = get_file_from_repo(self.name_or_path, "zh_g2p.json", subfolder="data")
with open(g2p_file, encoding="utf-8") as f:
g2p = json.load(f)
phones = []
tones = []
word2ph = []
initials = lazy_pinyin(text, neutral_tone_with_five=True, style=Style.INITIALS, tone_sandhi=True)
finals = lazy_pinyin(text, neutral_tone_with_five=True, style=Style.FINALS_TONE3, tone_sandhi=True)
for initial, final in zip(initials, finals):
tone = 0
if final[-1].isdigit():
pinyin = initial + final[:-1]
tone = int(final[-1])
if initial:
pinyin = re.sub(r"uei$", "ui", pinyin)
pinyin = re.sub(r"iou$", "iu", pinyin)
pinyin = re.sub(r"uen$", "un", pinyin)
else:
pinyin = re.sub(r"^ing$", "ying", pinyin)
pinyin = re.sub(r"^i$", "yi", pinyin)
pinyin = re.sub(r"^in$", "yin", pinyin)
pinyin = re.sub(r"^u$", "wu", pinyin)
pinyin = re.sub(r"^v", "yu", pinyin)
pinyin = re.sub(r"^e", "e", pinyin)
pinyin = re.sub(r"^i", "y", pinyin)
pinyin = re.sub(r"^u", "w", pinyin)
else:
pinyin = initial + final
if initial == final:
tone = 0
phone = [initial]
else:
phone = g2p.get(pinyin, [self.unk_token])
if phone[0] == self.unk_token:
tone = 0
phone = [self.unk_token]
tones += [tone] * len(phone)
phones += phone
if initial != 'SP':
word2ph.append(len(phone))
else:
word2ph[-1] += 1
phones = "<|SEP|>".join(phones)
return phones, tones, word2ph
def convert_g2p(self, text: str, language: str, add_special_tokens: bool) -> Tuple[str, List[int], List[int]]:
"""Converts a string of text into a list of phonemes and tones."""
if not is_phonemizer_available():
raise ImportError("Phonemizer is not available. Please install it using `pip install phonemizer`.")
if language.startswith("zh"):
phones, tones, word2ph = self.zh_g2p(text)
else:
raise ValueError(f"Language '{language}' not supported by VITS.")
lang_ids = [self.languages.index(language)] * len(tones)
if self.add_blank:
tones = self._add_blank(tones, 0)
lang_ids = self._add_blank(lang_ids, 0)
for i in range(len(word2ph)):
word2ph[i] = word2ph[i] * 2
word2ph[0] += 1
if add_special_tokens:
word2ph = [0] + word2ph + [0]
return phones, tones, lang_ids, word2ph
def _add_blank(self, sequence: List[Union[str, int]], blank: Union[str, int]) -> List[Union[str, int]]:
interspersed = [blank] * (len(sequence) * 2 + 1)
interspersed[1::2] = sequence
return interspersed
def _tokenize(self, text: str) -> List[str]:
"""Tokenize a string by inserting the `<pad>` token at the boundary between adjacent characters."""
tokens = []
if '<|SEP|>' in text:
tokens = text.split('<|SEP|>')
else: # fallback
i = 0
while i < len(text):
found = False
for j in range(min(len(text), i + 2), i, -1):
subtext = text[i:j]
if subtext in self.encoder:
tokens.append(subtext)
i = j
found = True
break
if not found:
tokens.append(self.unk_token)
i += 1
if self.add_blank:
tokens = self._add_blank(tokens, self.pad_token)
return tokens
def convert_tokens_to_string(self, tokens: List[str]) -> str:
if self.add_blank and len(tokens) > 1:
tokens = tokens[1::2]
return "".join(tokens)
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Union[Tuple[str], None]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
return (vocab_file,)
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