import Levenshtein from jiwer import wer, compute_measures import torch from tqdm import tqdm from data.audio.voice_tokenizer import VoiceBpeTokenizer def load_truths(file): niltok = VoiceBpeTokenizer(None) out = {} with open(file, 'r', encoding='utf-8') as f: for line in f.readlines(): spl = line.split('|') if len(spl) != 2: print(spl) continue path, truth = spl #path = path.replace('wav/', '') # This preprocesses the truth data in the same way that training data is processed: removing punctuation, all lowercase, removing unnecessary # whitespace, and applying "english cleaners", which convert words like "mrs" to "missus" and such. truth = niltok.preprocess_text(truth) out[path] = truth return out if __name__ == '__main__': inference_tsv = 'results.tsv' libri_base = 'y:\\bigasr_dataset/librispeech/test_clean/test_clean.txt' # Pre-process truth values truths = load_truths(libri_base) ground_truths = [] hypotheses = [] with open(inference_tsv, 'r') as tsv_file: tsv = tsv_file.read().splitlines() for line in tqdm(tsv): sentence_pred, wav = line.split('\t') hypotheses.append(sentence_pred) ground_truths.append(truths[wav]) wer = wer(ground_truths, hypotheses)*100 print(f"WER: {wer}")