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""" from https://github.com/keithito/tacotron
Cleaners are transformations that run over the input text at both training and eval time.
Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
hyperparameter. Some cleaners are English-specific. You'll typically want to use:
1. "english_cleaners" for English text
2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
the Unidecode library (https://pypi.python.org/pypi/Unidecode)
3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
the symbols in symbols.py to match your data).
"""
import logging
import re
import phonemizer
import piper_phonemize
from unidecode import unidecode
# To avoid excessive logging we set the log level of the phonemizer package to Critical
critical_logger = logging.getLogger("phonemizer")
critical_logger.setLevel(logging.CRITICAL)
# Intializing the phonemizer globally significantly reduces the speed
# now the phonemizer is not initialising at every call
# Might be less flexible, but it is much-much faster
global_phonemizer = phonemizer.backend.EspeakBackend(
language="en-us",
preserve_punctuation=True,
with_stress=True,
language_switch="remove-flags",
logger=critical_logger,
)
# Regular expression matching whitespace:
_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
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_cleaners2(text):
"""Pipeline for English text, including abbreviation expansion. + punctuation + stress"""
text = convert_to_ascii(text)
text = lowercase(text)
text = expand_abbreviations(text)
phonemes = global_phonemizer.phonemize([text], strip=True, njobs=1)[0]
phonemes = collapse_whitespace(phonemes)
return phonemes
def english_cleaners_piper(text):
"""Pipeline for English text, including abbreviation expansion. + punctuation + stress"""
text = convert_to_ascii(text)
text = lowercase(text)
text = expand_abbreviations(text)
phonemes = "".join(piper_phonemize.phonemize_espeak(text=text, voice="en-US")[0])
phonemes = collapse_whitespace(phonemes)
return phonemes
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