|
""" 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 phonemizer import phonemize |
|
from phonemizer.backend import EspeakBackend |
|
import matplotlib.pyplot as plt |
|
import traceback |
|
import sys |
|
import os |
|
from tqdm import tqdm |
|
import numpy as np |
|
|
|
|
|
|
|
_whitespace_re = re.compile(r'\s+') |
|
|
|
|
|
_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 abbreviation expansion.''' |
|
text = convert_to_ascii(text) |
|
text = lowercase(text) |
|
text = expand_abbreviations(text) |
|
phonemes = phonemize(text, language='en-us', backend='espeak', strip=True) |
|
phonemes = collapse_whitespace(phonemes) |
|
return phonemes |
|
|
|
|
|
def english_cleaners2(text): |
|
'''Pipeline for English text, including abbreviation expansion. + punctuation + stress''' |
|
|
|
|
|
if __name__ == '__main__': |
|
text_file = sys.argv[1] |
|
phoneme_file = sys.argv[2] |
|
|
|
backend = EspeakBackend('en-us', preserve_punctuation=True, with_stress=True) |
|
|
|
buffer = "" |
|
|
|
out_file = open(phoneme_file, 'w') |
|
for line in tqdm(open(text_file, errors='ignore').read().splitlines()): |
|
try: |
|
filepath, text, language, confidence = line.split('|') |
|
confidence = float(confidence) |
|
filename = os.path.basename(filepath).split('.')[0] |
|
duration = float(filename.split('_')[-1]) / 1000 |
|
|
|
if language == "en": |
|
phone = convert_to_ascii(text) |
|
phone = lowercase(phone) |
|
phone = expand_abbreviations(phone) |
|
|
|
phone = backend.phonemize([phone], strip=True)[0] |
|
phone = collapse_whitespace(phone) |
|
ratio = len(phone) / duration |
|
else: |
|
phone = "[blank]" |
|
ratio = 0 |
|
buffer += f"{filepath}|{text}|{phone}|{language}|{confidence:.3f}|{ratio:.3f}\n" |
|
if len(buffer) > 100000: |
|
out_file.write(buffer) |
|
buffer = "" |
|
except Exception as e: |
|
print(filename, line, e) |
|
continue |
|
out_file.write(buffer) |
