wer update

This commit is contained in:
James Betker 2021-12-31 16:21:39 -07:00
parent f0c4cd6317
commit 17fb934575
2 changed files with 51 additions and 31 deletions

View File

@ -13,13 +13,35 @@ from models.tacotron2.taco_utils import load_filepaths_and_text
from models.tacotron2.text.cleaners import english_cleaners
def remove_extraneous_punctuation(word):
replacement_punctuation = {
'{': '(', '}': ')',
'[': '(', ']': ')',
'`': '\'', '': '-',
'': '-', '`': '\'',
'ʼ': '\''
}
replace = re.compile("|".join([re.escape(k) for k in sorted(replacement_punctuation, key=len, reverse=True)]), flags=re.DOTALL)
word = replace.sub(lambda x: replacement_punctuation[x.group(0)], word)
# TODO: some of these are spoken ('@', '%', '+', etc). Integrate them into the cleaners.
extraneous = re.compile(r'^[@#%_=\$\^&\*\+\\]$')
word = extraneous.sub('', word)
return word
class VoiceBpeTokenizer:
def __init__(self, vocab_file):
self.tokenizer = Tokenizer.from_file(vocab_file)
if vocab_file is not None:
self.tokenizer = Tokenizer.from_file(vocab_file)
def encode(self, txt):
def preprocess_text(self, txt):
txt = english_cleaners(txt)
txt = remove_extraneous_punctuation(txt)
return txt
def encode(self, txt):
txt = self.preprocess_text(txt)
txt = txt.replace(' ', '[SPACE]')
return self.tokenizer.encode(txt).ids
@ -28,6 +50,9 @@ class VoiceBpeTokenizer:
seq = seq.cpu().numpy()
txt = self.tokenizer.decode(seq, skip_special_tokens=False).replace(' ', '')
txt = txt.replace('[SPACE]', ' ')
txt = txt.replace('[STOP]', '')
txt = txt.replace('[UNK]', '')
return txt
@ -50,23 +75,6 @@ def build_text_file_from_priors(priors, output):
out.flush()
def remove_extraneous_punctuation(word):
replacement_punctuation = {
'{': '(', '}': ')',
'[': '(', ']': ')',
'`': '\'', '': '-',
'': '-', '`': '\'',
'ʼ': '\''
}
replace = re.compile("|".join([re.escape(k) for k in sorted(replacement_punctuation, key=len, reverse=True)]), flags=re.DOTALL)
word = replace.sub(lambda x: replacement_punctuation[x.group(0)], word)
# TODO: some of these are spoken ('@', '%', '+', etc). Integrate them into the cleaners.
extraneous = re.compile(r'^[@#%_=\$\^&\*\+\\]$')
word = extraneous.sub('', word)
return word
def train():
with open('all_texts.txt', 'r', encoding='utf-8') as at:
ttsd = at.readlines()

View File

@ -5,6 +5,7 @@ import Levenshtein as Lev
import torch
from tqdm import tqdm
from data.audio.voice_tokenizer import VoiceBpeTokenizer
from models.tacotron2.text import cleaners
@ -56,9 +57,27 @@ class WordErrorRate:
return Lev.distance(''.join(w1), ''.join(w2))
def load_truths(file):
niltok = VoiceBpeTokenizer(None)
out = {}
with open(file, 'r', encoding='utf-8') as f:
for line in f.readline():
spl = line.split('|')
if len(spl) != 2:
continue
path, truth = spl
path = path.replace('wav/', '')
truth = niltok.preprocess_text(truth) # This may or may not be considered a "cheat", but the model is only trained on preprocessed text.
out[path] = truth
return out
if __name__ == '__main__':
inference_tsv = 'D:\\dlas\\codes\\results.tsv'
libri_base = 'Z:\\libritts\\test-clean'
inference_tsv = 'results.tsv'
libri_base = '/h/bigasr_dataset/librispeech/test_clean/test_clean.txt'
# Pre-process truth values
truths = load_truths(libri_base)
wer = WordErrorRate()
wer_scores = []
@ -67,13 +86,6 @@ if __name__ == '__main__':
for line in tqdm(tsv):
sentence_pred, wav = line.split('\t')
sentence_pred = normalize_text(sentence_pred)
wav_comp = wav.split('_')
reader = wav_comp[0]
book = wav_comp[1]
txt_file = os.path.join(libri_base, reader, book, wav.replace('.wav', '.normalized.txt'))
with open(txt_file, 'r') as txt_file_hndl:
txt_uncleaned = txt_file_hndl.read()
sentence_real = normalize_text(clean_text(txt_uncleaned))
wer_scores.append(wer.wer_calc(sentence_real, sentence_pred))
print(f"WER: {torch.tensor(wer_scores, dtype=torch.float).mean()}")
sentence_real = normalize_text(truths[wav])
wer_scores.append(wer.wer_calc(sentence_real, sentence_pred))
print(f"WER: {torch.tensor(wer_scores, dtype=torch.float).mean()}")