mirror of
https://github.com/storytold/storyteller-ml.git
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Brandon Thomas
162 lines
4.6 KiB
Python
162 lines
4.6 KiB
Python
""" from https://github.com/keithito/tacotron """
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'''
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Cleaners are transformations that run over the input text at both training and eval time.
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Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
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hyperparameter. Some cleaners are English-specific. You'll typically want to use:
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1. "english_cleaners" for English text
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2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
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the Unidecode library (https://pypi.python.org/pypi/Unidecode)
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3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
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the symbols in symbols.py to match your data).
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'''
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import re
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from unidecode import unidecode
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from .numbers import normalize_numbers
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from phonemizer import phonemize
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import phonemizer
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global_phonemizer = phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True, with_stress=True)
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spanish_phonemizer = phonemizer.backend.EspeakBackend(language='es-419', preserve_punctuation=True, with_stress=True)
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# Regular expression matching whitespace:
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_whitespace_re = re.compile(r'\s+')
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from g2p_en import G2p as grapheme_to_phn
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_punctuation = "!'(),.:;?"
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_special = "- "
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_arpa_exempt = _punctuation + _special
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arpa_g2p = grapheme_to_phn()
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def to_arpa(in_str):
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phn_arr = arpa_g2p(in_str)
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arpa_str = "{"
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in_chain = True
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# Iterative array-traverse approach to build ARPA string. Phonemes must be in curly braces, but not punctuation
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for token in phn_arr:
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if token in _arpa_exempt and in_chain:
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arpa_str += "}"
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in_chain = False
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if token not in _arpa_exempt and not in_chain:
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arpa_str += "{"
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in_chain = True
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arpa_str += " " + token
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if in_chain:
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arpa_str += "}"
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#removing multiple whitespace
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arpa_str = " ".join(arpa_str.split())
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arpa_str = arpa_str.replace("{ ","{")
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# string results in double-dir spaced punctuation "{M AY1} , {M AY1}", we want it only single right spaced: "{M AY1}, {M AY1}"
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for p in _punctuation:
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arpa_str = arpa_str.replace(f" {p}",f"{p}")
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return arpa_str
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# List of (regular expression, replacement) pairs for abbreviations:
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_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
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('mrs', 'misess'),
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('mr', 'mister'),
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('dr', 'doctor'),
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('st', 'saint'),
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('co', 'company'),
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('jr', 'junior'),
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('maj', 'major'),
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('gen', 'general'),
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('drs', 'doctors'),
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('rev', 'reverend'),
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('lt', 'lieutenant'),
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('hon', 'honorable'),
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('sgt', 'sergeant'),
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('capt', 'captain'),
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('esq', 'esquire'),
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('ltd', 'limited'),
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('col', 'colonel'),
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('ft', 'fort'),
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]]
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def expand_abbreviations(text):
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for regex, replacement in _abbreviations:
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text = re.sub(regex, replacement, text)
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return text
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def expand_numbers(text):
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return normalize_numbers(text)
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def lowercase(text):
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return text.lower()
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def collapse_whitespace(text):
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return re.sub(_whitespace_re, ' ', text)
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def convert_to_ascii(text):
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return unidecode(text)
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def basic_cleaners(text):
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'''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
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text = lowercase(text)
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text = collapse_whitespace(text)
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return text
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def transliteration_cleaners(text):
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'''Pipeline for non-English text that transliterates to ASCII.'''
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text = convert_to_ascii(text)
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text = lowercase(text)
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text = collapse_whitespace(text)
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return text
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def vbasic_cleaners(text):
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'''Only collapses whitespaces, for already processed text'''
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text = collapse_whitespace(text)
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return text
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def english_cleaners(text):
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'''Pipeline for English text, including number and abbreviation expansion.'''
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text = convert_to_ascii(text)
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text = lowercase(text)
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text = expand_numbers(text)
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text = expand_abbreviations(text)
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text = collapse_whitespace(text)
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return text
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def english_cleaners2(text):
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'''Pipeline for IPA English text, including abbreviation expansion. + punctuation + stress'''
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text = convert_to_ascii(text)
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text = lowercase(text)
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text = expand_abbreviations(text)
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phonemes = global_phonemizer.phonemize([text], strip=True, njobs=1)
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phonemes = phonemes[0]
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phonemes = collapse_whitespace(phonemes)
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return phonemes
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def spanish_cleaners(text):
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'''Pipeline for IPA Spanish text, punctuation + stress'''
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text = lowercase(text)
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phonemes = spanish_phonemizer.phonemize([text], strip=True, njobs=1)
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phonemes = phonemes[0].replace("(es-419)","").replace("(en)","")
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phonemes = collapse_whitespace(phonemes)
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return phonemes |