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""" 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 phonemizer import phonemize |
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from phonemizer.backend import EspeakBackend |
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backend_cat = EspeakBackend("ca", preserve_punctuation=True, with_stress=True) |
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backend_bal = EspeakBackend("ca-ba", preserve_punctuation=True, with_stress=True) |
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backend_val = EspeakBackend("ca-va", preserve_punctuation=True, with_stress=True) |
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backend_occ = EspeakBackend("ca-nw", preserve_punctuation=True, with_stress=True) |
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_whitespace_re = re.compile(r"\s+") |
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_abbreviations = [ |
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(re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) |
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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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] |
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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 english_cleaners(text): |
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"""Pipeline for English text, including abbreviation expansion.""" |
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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 = phonemize(text, language="en-us", backend="espeak", strip=True) |
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phonemes = collapse_whitespace(phonemes) |
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return phonemes |
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def english_cleaners2(text): |
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"""Pipeline for 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 = phonemize( |
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text, |
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language="en-us", |
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backend="espeak", |
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strip=True, |
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preserve_punctuation=True, |
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with_stress=True, |
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) |
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phonemes = collapse_whitespace(phonemes) |
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return phonemes |
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def catalan_cleaners(text): |
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"""Pipeline for catalan text, including punctuation + stress""" |
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text = lowercase(text) |
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phonemes = backend_cat.phonemize([text], strip=True)[0] |
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phonemes = collapse_whitespace(phonemes) |
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return phonemes |
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def catalan_balear_cleaners(text): |
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"""Pipeline for Catalan text, including abbreviation expansion. + punctuation + stress""" |
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text = lowercase(text) |
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phonemes = backend_bal.phonemize([text], strip=True, njobs=1)[0] |
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phonemes = collapse_whitespace(phonemes) |
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return phonemes |
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def catalan_occidental_cleaners(text): |
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"""Pipeline for Catalan text, including abbreviation expansion. + punctuation + stress""" |
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text = lowercase(text) |
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phonemes = backend_occ.phonemize([text], strip=True, njobs=1)[0] |
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phonemes = collapse_whitespace(phonemes) |
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return phonemes |
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def catalan_valencia_cleaners(text): |
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"""Pipeline for Catalan text, including abbreviation expansion. + punctuation + stress""" |
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text = lowercase(text) |
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phonemes = backend_val.phonemize([text], strip=True, njobs=1)[0] |
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phonemes = collapse_whitespace(phonemes) |
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return phonemes |
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