nemo/collections/common/tokenizers/text_to_speech/tokenizer_utils.py

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  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.


import re
import unicodedata
from builtins import str as unicode
from typing import List, Tuple

__all__ = [
    "french_text_preprocessing",
    "chinese_text_preprocessing",
    "english_text_preprocessing",
    "any_locale_text_preprocessing",
    "spanish_text_preprocessing",
    "vietnamese_text_preprocessing",
    "italian_text_preprocessing",
    "any_locale_word_tokenize",
    "english_word_tokenize",
    "LATIN_CHARS_ALL",
    "normalize_unicode_text",
    "japanese_text_preprocessing",
]

# Derived from LJSpeech
_synoglyphs = {
    "'": ['’'],
    '"': ['”', '“'],
}
SYNOGLYPH2ASCII = {g: asc for asc, glyphs in _synoglyphs.items() for g in glyphs}

# Example of parsing by groups via _WORDS_RE_EN.
# Regular expression pattern groups:
#   1st group -- valid english words,
#   2nd group -- any substring starts from | to | (mustn't be nested), useful when you want to leave sequence unchanged,
#   3rd group -- punctuation marks or whitespaces.
# Text (first line) and mask of groups for every char (second line).
# config file must contain |EY1 EY1|, B, C, D, E, F, and G.

# define char set based on https://en.wikipedia.org/wiki/List_of_Unicode_characters
LATIN_ALPHABET_BASIC = "A-Za-z"
ACCENTED_CHARS = "À-ÖØ-öø-ÿ"
LATIN_CHARS_ALL = f"{LATIN_ALPHABET_BASIC}{ACCENTED_CHARS}"
_WORDS_RE_EN = re.compile(
    fr"([{LATIN_ALPHABET_BASIC}]+(?:[{LATIN_ALPHABET_BASIC}\-']*[{LATIN_ALPHABET_BASIC}]+)*)|(\|[^|]*\|)|([^{LATIN_ALPHABET_BASIC}|]+)"
)
_WORDS_RE_ANY_LOCALE = re.compile(
    fr"([{LATIN_CHARS_ALL}]+(?:[{LATIN_CHARS_ALL}\-']*[{LATIN_CHARS_ALL}]+)*)|(\|[^|]*\|)|([^{LATIN_CHARS_ALL}|]+)"
)


def english_text_preprocessing(text, lower=True):
    text = unicode(text)
    text = ''.join(char for char in unicodedata.normalize('NFD', text) if unicodedata.category(char) != 'Mn')
    text = ''.join(char if char not in SYNOGLYPH2ASCII else SYNOGLYPH2ASCII[char] for char in text)

    if lower:
        text = text.lower()

    return text


def any_locale_text_preprocessing(text: str) -> str:
    """
    Normalize unicode text with "NFC", and convert right single quotation mark (U+2019, decimal 8217) as an apostrophe.

    Args:
        text (str): the original input sentence.

    Returns: normalized text (str).
    """
    res = []
    for c in normalize_unicode_text(text):
        if c in ['’']:  # right single quotation mark (U+2019, decimal 8217) as an apostrophe
            res.append("'")
        else:
            res.append(c)

    return ''.join(res)


def normalize_unicode_text(text: str) -> str:
    """
    TODO @xueyang: Apply NFC form may be too aggressive since it would ignore some accented characters that do not exist
      in predefined German alphabet (nemo.collections.common.tokenizers.text_to_speech.ipa_lexicon.IPA_CHARACTER_SETS),
      such as 'é'. This is not expected. A better solution is to add an extra normalization with NFD to discard the
      diacritics and consider 'é' and 'e' produce similar pronunciations.

    Note that the tokenizer needs to run `unicodedata.normalize("NFC", x)` before calling `encode` function,
    especially for the characters that have diacritics, such as 'ö' in the German alphabet. 'ö' can be encoded as
    b'\xc3\xb6' (one char) as well as b'o\xcc\x88' (two chars). Without the normalization of composing two chars
    together and without a complete predefined set of diacritics, when the tokenizer reads the input sentence
    char-by-char, it would skip the combining diaeresis b'\xcc\x88', resulting in indistinguishable pronunciations
    for 'ö' and 'o'.

    Args:
        text (str): the original input sentence.

    Returns:
        NFC normalized sentence (str).
    """
    # normalize word with NFC form
    if not unicodedata.is_normalized("NFC", text):
        text = unicodedata.normalize("NFC", text)

    return text


def _word_tokenize(words: List[Tuple[str, str, str]], is_lower: bool = False) -> List[Tuple[List[str], bool]]:
    """
    Process a list of words and attach indicators showing if each word is unchangeable or not. Each word representation
    can be one of valid word, any substring starting from | to | (unchangeable word), or punctuation marks including
    whitespaces. This function will split unchanged strings by whitespaces and return them as `List[str]`. For example,

    .. code-block:: python
        [
            ('Hello', '', ''),  # valid word
            ('', '', ' '),  # punctuation mark
            ('World', '', ''),  # valid word
            ('', '', ' '),  # punctuation mark
            ('', '|NVIDIA unchanged|', ''),  # unchangeable word
            ('', '', '!')  # punctuation mark
        ]

    will be converted into,

    .. code-block:: python
        [
            (["Hello"], False),
            ([" "], False),
            (["World"], False),
            ([" "], False),
            (["NVIDIA", "unchanged"], True),
            (["!"], False)
        ]

    Args:
        words (List[str]): a list of tuples like `(maybe_word, maybe_without_changes, maybe_punct)` where each element
            corresponds to a non-overlapping match of either `_WORDS_RE_EN` or `_WORDS_RE_ANY_LOCALE`.
        is_lower (bool): a flag to trigger lowercase all words. By default, it is False.

    Returns: List[Tuple[List[str], bool]], a list of tuples like `(a list of words, is_unchanged)`.

    """
    result = []
    for word in words:
        maybe_word, maybe_without_changes, maybe_punct = word

        without_changes = False
        if maybe_word != '':
            if is_lower:
                token = [maybe_word.lower()]
            else:
                token = [maybe_word]
        elif maybe_punct != '':
            token = [maybe_punct]
        elif maybe_without_changes != '':
            without_changes = True
            token = maybe_without_changes[1:-1].split(" ")
        else:
            raise ValueError(
                f"This is not expected. Found empty string: <{word}>. "
                f"Please validate your regular expression pattern '_WORDS_RE_EN' or '_WORDS_RE_ANY_LOCALE'."
            )

        result.append((token, without_changes))

    return result


def english_word_tokenize(text: str) -> List[Tuple[List[str], bool]]:
    words = _WORDS_RE_EN.findall(text)
    return _word_tokenize(words, is_lower=True)


def any_locale_word_tokenize(text: str) -> List[Tuple[List[str], bool]]:
    words = _WORDS_RE_ANY_LOCALE.findall(text)
    return _word_tokenize(words)


def spanish_text_preprocessing(text: str) -> str:
    return text.lower()


def italian_text_preprocessing(text: str) -> str:
    return text.lower()


def chinese_text_preprocessing(text: str) -> str:
    return text


def french_text_preprocessing(text: str) -> str:
    return text.lower()


def vietnamese_text_preprocessing(text: str) -> str:
    return text.lower()


def japanese_text_preprocessing(text: str) -> str:
    return text.lower()