45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
import Levenshtein
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from jiwer import wer, compute_measures
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import torch
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from tqdm import tqdm
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from data.audio.voice_tokenizer import VoiceBpeTokenizer
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def load_truths(file):
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niltok = VoiceBpeTokenizer(None)
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out = {}
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with open(file, 'r', encoding='utf-8') as f:
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for line in f.readlines():
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spl = line.split('|')
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if len(spl) != 2:
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print(spl)
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continue
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path, truth = spl
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#path = path.replace('wav/', '')
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# This preprocesses the truth data in the same way that training data is processed: removing punctuation, all lowercase, removing unnecessary
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# whitespace, and applying "english cleaners", which convert words like "mrs" to "missus" and such.
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truth = niltok.preprocess_text(truth)
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out[path] = truth
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return out
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if __name__ == '__main__':
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inference_tsv = 'results.tsv'
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libri_base = 'y:\\bigasr_dataset/librispeech/test_clean/test_clean.txt'
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# Pre-process truth values
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truths = load_truths(libri_base)
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niltok = VoiceBpeTokenizer(None)
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ground_truths = []
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hypotheses = []
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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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hypotheses.append(niltok.preprocess_text(sentence_pred))
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ground_truths.append(truths[wav])
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wer = wer(ground_truths, hypotheses)*100
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print(f"WER: {wer}")
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