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remove the text folder
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Fangjun Kuang
parent
ba4df19224
commit
f6328edf5b
@ -1,64 +0,0 @@
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""" from https://github.com/keithito/tacotron """
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from matcha.text import cleaners
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from matcha.text.symbols import symbols
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# Mappings from symbol to numeric ID and vice versa:
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_symbol_to_id = {s: i for i, s in enumerate(symbols)}
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_id_to_symbol = {
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i: s for i, s in enumerate(symbols)
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} # pylint: disable=unnecessary-comprehension
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def text_to_sequence(text, cleaner_names):
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"""Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
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Args:
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text: string to convert to a sequence
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cleaner_names: names of the cleaner functions to run the text through
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Returns:
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List of integers corresponding to the symbols in the text
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"""
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sequence = []
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clean_text = _clean_text(text, cleaner_names)
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for symbol in clean_text:
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try:
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if symbol in "_()[]# ̃":
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continue
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symbol_id = _symbol_to_id[symbol]
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except Exception as ex:
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print(text)
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print(clean_text)
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raise RuntimeError(
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f"text: {text}, clean_text: {clean_text}, ex: {ex}, symbol: {symbol}"
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)
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sequence += [symbol_id]
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return sequence, clean_text
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def cleaned_text_to_sequence(cleaned_text):
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"""Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
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Args:
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text: string to convert to a sequence
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Returns:
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List of integers corresponding to the symbols in the text
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"""
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sequence = [_symbol_to_id[symbol] for symbol in cleaned_text]
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return sequence
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def sequence_to_text(sequence):
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"""Converts a sequence of IDs back to a string"""
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result = ""
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for symbol_id in sequence:
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s = _id_to_symbol[symbol_id]
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result += s
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return result
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def _clean_text(text, cleaner_names):
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for name in cleaner_names:
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cleaner = getattr(cleaners, name)
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if not cleaner:
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raise Exception("Unknown cleaner: %s" % name)
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text = cleaner(text)
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return text
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@ -1,130 +0,0 @@
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""" from https://github.com/keithito/tacotron
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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 logging
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import re
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import phonemizer
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from unidecode import unidecode
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# To avoid excessive logging we set the log level of the phonemizer package to Critical
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critical_logger = logging.getLogger("phonemizer")
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critical_logger.setLevel(logging.CRITICAL)
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# Intializing the phonemizer globally significantly reduces the speed
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# now the phonemizer is not initialising at every call
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# Might be less flexible, but it is much-much faster
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global_phonemizer = phonemizer.backend.EspeakBackend(
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language="en-us",
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preserve_punctuation=True,
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with_stress=True,
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language_switch="remove-flags",
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logger=critical_logger,
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)
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# Regular expression matching whitespace:
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_whitespace_re = re.compile(r"\s+")
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# List of (regular expression, replacement) pairs for abbreviations:
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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 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 remove_parentheses(text):
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text = text.replace("(", "")
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text = text.replace(")", "")
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text = text.replace("[", "")
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text = text.replace("]", "")
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return 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_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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text = remove_parentheses(text)
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phonemes = global_phonemizer.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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# I am removing this due to incompatibility with several version of python
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# However, if you want to use it, you can uncomment it
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# and install piper-phonemize with the following command:
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# pip install piper-phonemize
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# import piper_phonemize
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# def english_cleaners_piper(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 = "".join(piper_phonemize.phonemize_espeak(text=text, voice="en-US")[0])
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# phonemes = collapse_whitespace(phonemes)
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# return phonemes
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@ -1,73 +0,0 @@
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""" from https://github.com/keithito/tacotron """
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import re
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import inflect
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_inflect = inflect.engine()
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_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
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_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
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_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
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_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
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_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
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_number_re = re.compile(r"[0-9]+")
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def _remove_commas(m):
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return m.group(1).replace(",", "")
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def _expand_decimal_point(m):
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return m.group(1).replace(".", " point ")
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def _expand_dollars(m):
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match = m.group(1)
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parts = match.split(".")
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if len(parts) > 2:
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return match + " dollars"
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dollars = int(parts[0]) if parts[0] else 0
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cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
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if dollars and cents:
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dollar_unit = "dollar" if dollars == 1 else "dollars"
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cent_unit = "cent" if cents == 1 else "cents"
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return f"{dollars} {dollar_unit}, {cents} {cent_unit}"
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elif dollars:
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dollar_unit = "dollar" if dollars == 1 else "dollars"
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return f"{dollars} {dollar_unit}"
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elif cents:
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cent_unit = "cent" if cents == 1 else "cents"
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return f"{cents} {cent_unit}"
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else:
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return "zero dollars"
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def _expand_ordinal(m):
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return _inflect.number_to_words(m.group(0))
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def _expand_number(m):
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num = int(m.group(0))
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if num > 1000 and num < 3000:
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if num == 2000:
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return "two thousand"
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elif num > 2000 and num < 2010:
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return "two thousand " + _inflect.number_to_words(num % 100)
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elif num % 100 == 0:
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return _inflect.number_to_words(num // 100) + " hundred"
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else:
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return _inflect.number_to_words(
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num, andword="", zero="oh", group=2
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).replace(", ", " ")
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else:
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return _inflect.number_to_words(num, andword="")
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def normalize_numbers(text):
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text = re.sub(_comma_number_re, _remove_commas, text)
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text = re.sub(_pounds_re, r"\1 pounds", text)
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text = re.sub(_dollars_re, _expand_dollars, text)
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text = re.sub(_decimal_number_re, _expand_decimal_point, text)
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text = re.sub(_ordinal_re, _expand_ordinal, text)
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text = re.sub(_number_re, _expand_number, text)
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return text
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@ -1,15 +0,0 @@
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""" from https://github.com/keithito/tacotron
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Defines the set of symbols used in text input to the model.
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"""
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_pad = "_"
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_punctuation = ';:,.!?¡¿—…"«»“” '
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_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
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_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
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# Export all symbols:
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symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
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# Special symbol ids
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SPACE_ID = symbols.index(" ")
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