mirror of
https://github.com/malarinv/tacotron2
synced 2026-03-08 09:42:34 +00:00
integrate tacotron2/waveglow based tts server
This commit is contained in:
@@ -1,22 +1,23 @@
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# -*- coding: utf-8 -*-
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""" from https://github.com/keithito/tacotron """
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import re
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from text import cleaners
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from text.symbols import symbols
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from . import cleaners
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from .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 = {i: s for i, s in enumerate(symbols)}
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# Regular expression matching text enclosed in curly braces:
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_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
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_curly_re = re.compile(r"(.*?)\{(.+?)\}(.*)")
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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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"""Converts a string of text to a sequence of IDs corresponding to the
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symbols in the text.
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The text can optionally have ARPAbet sequences enclosed in curly braces embedded
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in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
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The text can optionally have ARPAbet sequences enclosed in curly braces
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embedded in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
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Args:
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text: string to convert to a sequence
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@@ -24,51 +25,53 @@ def text_to_sequence(text, cleaner_names):
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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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"""
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sequence = []
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# Check for curly braces and treat their contents as ARPAbet:
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while len(text):
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m = _curly_re.match(text)
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if not m:
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sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
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break
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sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
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sequence += _arpabet_to_sequence(m.group(2))
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text = m.group(3)
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# Check for curly braces and treat their contents as ARPAbet:
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while len(text):
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m = _curly_re.match(text)
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if not m:
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sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
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break
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sequence += _symbols_to_sequence(
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_clean_text(m.group(1), cleaner_names)
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)
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sequence += _arpabet_to_sequence(m.group(2))
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text = m.group(3)
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return sequence
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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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if symbol_id in _id_to_symbol:
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s = _id_to_symbol[symbol_id]
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# Enclose ARPAbet back in curly braces:
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if len(s) > 1 and s[0] == '@':
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s = '{%s}' % s[1:]
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result += s
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return result.replace('}{', ' ')
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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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if symbol_id in _id_to_symbol:
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s = _id_to_symbol[symbol_id]
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# Enclose ARPAbet back in curly braces:
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if len(s) > 1 and s[0] == "@":
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s = "{%s}" % s[1:]
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result += s
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return result.replace("}{", " ")
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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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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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def _symbols_to_sequence(symbols):
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return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
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return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
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def _arpabet_to_sequence(text):
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return _symbols_to_sequence(['@' + s for s in text.split()])
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return _symbols_to_sequence(["@" + s for s in text.split()])
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def _should_keep_symbol(s):
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return s in _symbol_to_id and s is not '_' and s is not '~'
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return s in _symbol_to_id and s != "_" and s != "~"
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125
text/cleaners.py
125
text/cleaners.py
@@ -1,90 +1,99 @@
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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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# -*- coding: utf-8 -*-
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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 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
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eval time.
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Cleaners can be selected by passing a comma-delimited list of cleaner names as
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the "cleaners"
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hyperparameter. Some cleaners are English-specific. You'll typically want to
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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
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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
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should also update
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the symbols in symbols.py to match your data).
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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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_whitespace_re = re.compile(r"\s+")
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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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_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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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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return normalize_numbers(text)
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def lowercase(text):
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return text.lower()
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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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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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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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"""Basic pipeline that lowercases and collapses whitespace without
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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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"""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 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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"""Pipeline for English text, including number and abbreviation
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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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170
text/cmudict.py
170
text/cmudict.py
@@ -1,65 +1,143 @@
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# -*- coding: utf-8 -*-
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""" from https://github.com/keithito/tacotron """
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import re
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valid_symbols = [
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'AA', 'AA0', 'AA1', 'AA2', 'AE', 'AE0', 'AE1', 'AE2', 'AH', 'AH0', 'AH1', 'AH2',
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'AO', 'AO0', 'AO1', 'AO2', 'AW', 'AW0', 'AW1', 'AW2', 'AY', 'AY0', 'AY1', 'AY2',
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'B', 'CH', 'D', 'DH', 'EH', 'EH0', 'EH1', 'EH2', 'ER', 'ER0', 'ER1', 'ER2', 'EY',
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'EY0', 'EY1', 'EY2', 'F', 'G', 'HH', 'IH', 'IH0', 'IH1', 'IH2', 'IY', 'IY0', 'IY1',
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'IY2', 'JH', 'K', 'L', 'M', 'N', 'NG', 'OW', 'OW0', 'OW1', 'OW2', 'OY', 'OY0',
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'OY1', 'OY2', 'P', 'R', 'S', 'SH', 'T', 'TH', 'UH', 'UH0', 'UH1', 'UH2', 'UW',
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'UW0', 'UW1', 'UW2', 'V', 'W', 'Y', 'Z', 'ZH'
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"AA",
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"AA0",
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"AA1",
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"AA2",
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"AE",
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"AE0",
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"AE1",
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"AE2",
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"AH",
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"AH0",
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"AH1",
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"AH2",
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"AO",
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"AO0",
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"AO1",
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"AO2",
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"AW",
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"AW0",
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"AW1",
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"AW2",
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"AY",
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"AY0",
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"AY1",
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"AY2",
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"B",
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"CH",
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"D",
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"DH",
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"EH",
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"EH0",
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"EH1",
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"EH2",
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"ER",
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"ER0",
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"ER1",
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"ER2",
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"EY",
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"EY0",
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"EY1",
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"EY2",
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"F",
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"G",
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"HH",
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"IH",
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"IH0",
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"IH1",
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"IH2",
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"IY",
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"IY0",
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"IY1",
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"IY2",
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"JH",
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"K",
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"L",
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"M",
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"N",
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"NG",
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"OW",
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"OW0",
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"OW1",
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"OW2",
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"OY",
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"OY0",
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"OY1",
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"OY2",
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"P",
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"R",
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"S",
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"SH",
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"T",
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"TH",
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"UH",
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"UH0",
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"UH1",
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"UH2",
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"UW",
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"UW0",
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"UW1",
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"UW2",
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"V",
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"W",
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"Y",
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"Z",
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"ZH",
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]
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_valid_symbol_set = set(valid_symbols)
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class CMUDict:
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'''Thin wrapper around CMUDict data. http://www.speech.cs.cmu.edu/cgi-bin/cmudict'''
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def __init__(self, file_or_path, keep_ambiguous=True):
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if isinstance(file_or_path, str):
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with open(file_or_path, encoding='latin-1') as f:
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entries = _parse_cmudict(f)
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else:
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entries = _parse_cmudict(file_or_path)
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if not keep_ambiguous:
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entries = {word: pron for word, pron in entries.items() if len(pron) == 1}
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self._entries = entries
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"""Thin wrapper around CMUDict data.
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http://www.speech.cs.cmu.edu/cgi-bin/cmudict"""
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def __init__(self, file_or_path, keep_ambiguous=True):
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if isinstance(file_or_path, str):
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with open(file_or_path, encoding="latin-1") as f:
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entries = _parse_cmudict(f)
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else:
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entries = _parse_cmudict(file_or_path)
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if not keep_ambiguous:
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entries = {
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word: pron for word, pron in entries.items() if len(pron) == 1
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}
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self._entries = entries
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def __len__(self):
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return len(self._entries)
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def lookup(self, word):
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"""Returns list of ARPAbet pronunciations of the given word."""
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return self._entries.get(word.upper())
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def __len__(self):
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return len(self._entries)
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|
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|
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def lookup(self, word):
|
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'''Returns list of ARPAbet pronunciations of the given word.'''
|
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return self._entries.get(word.upper())
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|
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|
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|
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_alt_re = re.compile(r'\([0-9]+\)')
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_alt_re = re.compile(r"\([0-9]+\)")
|
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|
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|
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def _parse_cmudict(file):
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cmudict = {}
|
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for line in file:
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if len(line) and (line[0] >= 'A' and line[0] <= 'Z' or line[0] == "'"):
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parts = line.split(' ')
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word = re.sub(_alt_re, '', parts[0])
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pronunciation = _get_pronunciation(parts[1])
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if pronunciation:
|
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if word in cmudict:
|
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cmudict[word].append(pronunciation)
|
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else:
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cmudict[word] = [pronunciation]
|
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return cmudict
|
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cmudict = {}
|
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for line in file:
|
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if len(line) and (line[0] >= "A" and line[0] <= "Z" or line[0] == "'"):
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parts = line.split(" ")
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word = re.sub(_alt_re, "", parts[0])
|
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pronunciation = _get_pronunciation(parts[1])
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if pronunciation:
|
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if word in cmudict:
|
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cmudict[word].append(pronunciation)
|
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else:
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cmudict[word] = [pronunciation]
|
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return cmudict
|
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|
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def _get_pronunciation(s):
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parts = s.strip().split(' ')
|
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for part in parts:
|
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if part not in _valid_symbol_set:
|
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return None
|
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return ' '.join(parts)
|
||||
parts = s.strip().split(" ")
|
||||
for part in parts:
|
||||
if part not in _valid_symbol_set:
|
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return None
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return " ".join(parts)
|
||||
|
||||
@@ -1,71 +1,73 @@
|
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# -*- coding: utf-8 -*-
|
||||
""" from https://github.com/keithito/tacotron """
|
||||
|
||||
import inflect
|
||||
import re
|
||||
|
||||
|
||||
_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]+')
|
||||
_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(',', '')
|
||||
return m.group(1).replace(",", "")
|
||||
|
||||
|
||||
def _expand_decimal_point(m):
|
||||
return m.group(1).replace('.', ' point ')
|
||||
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'
|
||||
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))
|
||||
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'
|
||||
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='', zero='oh', group=2).replace(', ', ' ')
|
||||
else:
|
||||
return _inflect.number_to_words(num, andword='')
|
||||
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
|
||||
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
|
||||
|
||||
@@ -1,18 +1,24 @@
|
||||
""" from https://github.com/keithito/tacotron """
|
||||
# -*- coding: utf-8 -*-
|
||||
from . import cmudict
|
||||
|
||||
'''
|
||||
""" from https://github.com/keithito/tacotron """
|
||||
"""
|
||||
Defines the set of symbols used in text input to the model.
|
||||
|
||||
The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. '''
|
||||
from text import cmudict
|
||||
The default is a set of ASCII characters that works well for English or text
|
||||
that has been run through Unidecode. For other data, you can modify
|
||||
_characters. See TRAINING_DATA.md for details. """
|
||||
|
||||
_pad = '_'
|
||||
_punctuation = '!\'(),.:;? '
|
||||
_special = '-'
|
||||
_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
|
||||
_pad = "_"
|
||||
_punctuation = "!'(),.:;? "
|
||||
_special = "-"
|
||||
_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
|
||||
|
||||
# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
|
||||
_arpabet = ['@' + s for s in cmudict.valid_symbols]
|
||||
# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as
|
||||
# uppercase letters):
|
||||
_arpabet = ["@" + s for s in cmudict.valid_symbols]
|
||||
|
||||
# Export all symbols:
|
||||
symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet
|
||||
symbols = (
|
||||
[_pad] + list(_special) + list(_punctuation) + list(_letters) + _arpabet
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user