mirror of
https://github.com/storytold/torchscript-models.git
synced 2026-10-25 08:19:27 +00:00
rename glow-tts-2 to glow-tts (removing git submodule)
This commit is contained in:
@@ -0,0 +1,90 @@
|
||||
""" 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 re
|
||||
from unidecode import unidecode
|
||||
from .numbers import normalize_numbers
|
||||
|
||||
|
||||
# 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 expand_numbers(text):
|
||||
return normalize_numbers(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_cleaners(text):
|
||||
'''Pipeline for English text, including number and abbreviation expansion.'''
|
||||
text = convert_to_ascii(text)
|
||||
text = lowercase(text)
|
||||
text = expand_numbers(text)
|
||||
text = expand_abbreviations(text)
|
||||
text = collapse_whitespace(text)
|
||||
return text
|
||||
Reference in New Issue
Block a user