tortoise/utils/tokenizer.py

import os
import re

import inflect
import torch
from tokenizers import Tokenizer

# Regular expression matching whitespace:
from unidecode import unidecode

_whitespace_re = re.compile(r"\s+")


# List of (regular expression, replacement) pairs for abbreviations:
_abbreviations = [
    (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
    for x in [
        ("mrs", "misess"),
        ("mr", "mister"),
        ("dr", "doctor"),
        ("st", "saint"),
        ("co", "company"),
        ("jr", "junior"),
        ("maj", "major"),
        ("gen", "general"),
        ("drs", "doctors"),
        ("rev", "reverend"),
        ("lt", "lieutenant"),
        ("hon", "honorable"),
        ("sgt", "sergeant"),
        ("capt", "captain"),
        ("esq", "esquire"),
        ("ltd", "limited"),
        ("col", "colonel"),
        ("ft", "fort"),
    ]
]


def expand_abbreviations(text):
    for regex, replacement in _abbreviations:
        text = re.sub(regex, replacement, text)
    return text


_inflect = inflect.engine()
_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
_number_re = re.compile(r"[0-9]+")


def _remove_commas(m):
    return m.group(1).replace(",", "")


def _expand_decimal_point(m):
    return m.group(1).replace(".", " point ")


def _expand_dollars(m):
    match = m.group(1)
    parts = match.split(".")
    if len(parts) > 2:
        return match + " dollars"  # Unexpected format
    dollars = int(parts[0]) if parts[0] else 0
    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
    if dollars and cents:
        dollar_unit = "dollar" if dollars == 1 else "dollars"
        cent_unit = "cent" if cents == 1 else "cents"
        return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
    elif dollars:
        dollar_unit = "dollar" if dollars == 1 else "dollars"
        return "%s %s" % (dollars, dollar_unit)
    elif cents:
        cent_unit = "cent" if cents == 1 else "cents"
        return "%s %s" % (cents, cent_unit)
    else:
        return "zero dollars"


def _expand_ordinal(m):
    return _inflect.number_to_words(m.group(0))


def _expand_number(m):
    num = int(m.group(0))
    if num > 1000 and num < 3000:
        if num == 2000:
            return "two thousand"
        elif num > 2000 and num < 2010:
            return "two thousand " + _inflect.number_to_words(num % 100)
        elif num % 100 == 0:
            return _inflect.number_to_words(num // 100) + " hundred"
        else:
            return _inflect.number_to_words(
                num, andword="", zero="oh", group=2
            ).replace(", ", " ")
    else:
        return _inflect.number_to_words(num, andword="")


def normalize_numbers(text):
    text = re.sub(_comma_number_re, _remove_commas, text)
    text = re.sub(_pounds_re, r"\1 pounds", text)
    text = re.sub(_dollars_re, _expand_dollars, text)
    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
    text = re.sub(_ordinal_re, _expand_ordinal, text)
    text = re.sub(_number_re, _expand_number, text)
    return text


def expand_numbers(text):
    return normalize_numbers(text)


def lowercase(text):
    return text.lower()


def collapse_whitespace(text):
    return re.sub(_whitespace_re, " ", text)


def convert_to_ascii(text):
    return unidecode(text)


def basic_cleaners(text):
    """Basic pipeline that lowercases and collapses whitespace without transliteration."""
    text = lowercase(text)
    text = collapse_whitespace(text)
    return text


def transliteration_cleaners(text):
    """Pipeline for non-English text that transliterates to ASCII."""
    text = convert_to_ascii(text)
    text = lowercase(text)
    text = collapse_whitespace(text)
    return text


def english_cleaners(text):
    """Pipeline for English text, including number and abbreviation expansion."""
    text = convert_to_ascii(text)
    text = lowercase(text)
    text = expand_numbers(text)
    text = expand_abbreviations(text)
    text = collapse_whitespace(text)
    text = text.replace('"', "")
    return text


def lev_distance(s1, s2):
    if len(s1) > len(s2):
        s1, s2 = s2, s1

    distances = range(len(s1) + 1)
    for i2, c2 in enumerate(s2):
        distances_ = [i2 + 1]
        for i1, c1 in enumerate(s1):
            if c1 == c2:
                distances_.append(distances[i1])
            else:
                distances_.append(
                    1 + min((distances[i1], distances[i1 + 1], distances_[-1]))
                )
        distances = distances_
    return distances[-1]


DEFAULT_VOCAB_FILE = os.path.join(
    os.path.dirname(os.path.realpath(__file__)), "../data/tokenizer.json"
)


class VoiceBpeTokenizer:
    def __init__(self, vocab_file=DEFAULT_VOCAB_FILE):
        if vocab_file is not None:
            self.tokenizer = Tokenizer.from_file(vocab_file)

    def preprocess_text(self, txt):
        txt = english_cleaners(txt)
        return txt

    def encode(self, txt):
        txt = self.preprocess_text(txt)
        txt = txt.replace(" ", "[SPACE]")
        return self.tokenizer.encode(txt).ids

    def decode(self, seq):
        if isinstance(seq, torch.Tensor):
            seq = seq.cpu().numpy()
        txt = self.tokenizer.decode(seq, skip_special_tokens=False).replace(" ", "")
        txt = txt.replace("[SPACE]", " ")
        txt = txt.replace("[STOP]", "")
        txt = txt.replace("[UNK]", "")
        return txt