79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
# Original source: https://github.com/SeanNaren/deepspeech.pytorch/blob/master/deepspeech_pytorch/validation.py
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import os
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import Levenshtein as Lev
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import torch
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from tqdm import tqdm
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from models.tacotron2.text import cleaners
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def clean_text(text):
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for name in ['english_cleaners']:
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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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# Converts text to all-uppercase and separates punctuation from words.
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def normalize_text(text):
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text = text.upper()
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for punc in ['.', ',', ':', ';']:
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text = text.replace(punc, f' {punc}')
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return text.strip()
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class WordErrorRate:
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def calculate_metric(self, transcript, reference):
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wer_inst = self.wer_calc(transcript, reference)
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self.wer += wer_inst
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self.n_tokens += len(reference.split())
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def compute(self):
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wer = float(self.wer) / self.n_tokens
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return wer.item() * 100
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def wer_calc(self, s1, s2):
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"""
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Computes the Word Error Rate, defined as the edit distance between the
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two provided sentences after tokenizing to words.
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Arguments:
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s1 (string): space-separated sentence
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s2 (string): space-separated sentence
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"""
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# build mapping of words to integers
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b = set(s1.split() + s2.split())
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word2char = dict(zip(b, range(len(b))))
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# map the words to a char array (Levenshtein packages only accepts
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# strings)
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w1 = [chr(word2char[w]) for w in s1.split()]
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w2 = [chr(word2char[w]) for w in s2.split()]
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return Lev.distance(''.join(w1), ''.join(w2))
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if __name__ == '__main__':
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inference_tsv = 'D:\\dlas\\codes\\results.tsv'
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libri_base = 'Z:\\libritts\\test-clean'
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wer = WordErrorRate()
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wer_scores = []
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with open(inference_tsv, 'r') as tsv_file:
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tsv = tsv_file.read().splitlines()
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for line in tqdm(tsv):
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sentence_pred, wav = line.split('\t')
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sentence_pred = normalize_text(sentence_pred)
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wav_comp = wav.split('_')
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reader = wav_comp[0]
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book = wav_comp[1]
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txt_file = os.path.join(libri_base, reader, book, wav.replace('.wav', '.normalized.txt'))
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with open(txt_file, 'r') as txt_file_hndl:
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txt_uncleaned = txt_file_hndl.read()
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sentence_real = normalize_text(clean_text(txt_uncleaned))
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wer_scores.append(wer.wer_calc(sentence_real, sentence_pred))
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print(f"WER: {torch.tensor(wer_scores, dtype=torch.float).mean()}") |