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import os.path |
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import sys |
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base_dir = '..' |
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sys.path.append(base_dir) |
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from Trainer import Trainer |
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from TranslatorTrainer import TranslatorTrainer |
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from dataset import GridDataset, CharMap |
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WORD_TOKENIZE = False |
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PHONEME_FILTER_PREV = False |
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BEAM_SIZE = 0 |
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lipnet_weights = 'weights/phoneme-231201-2218/I119000-L00001-W00000-C00000.pt' |
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if WORD_TOKENIZE: |
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translator_weights = 'weights/translate-231204-1652/I160-L00047-W00000.pt' |
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else: |
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translator_weights = 'weights/translate-231204-2227/I860-L00000-W00000.pt' |
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lipnet_predictor = Trainer( |
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write_logs=False, base_dir=base_dir, |
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num_workers=0, char_map=CharMap.phonemes |
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) |
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lipnet_predictor.load_weights(lipnet_weights) |
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lipnet_predictor.load_datasets() |
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dataset = lipnet_predictor.test_dataset |
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phoneme_translator = TranslatorTrainer( |
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write_logs=False, base_dir=base_dir, word_tokenize=WORD_TOKENIZE |
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) |
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phoneme_translator.load_weights(os.path.join( |
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base_dir, translator_weights |
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)) |
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""" |
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new_phonemes = GridDataset.text_to_phonemes("Do you like fries") |
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print("PRE_REV_TRANSLATE", [new_phonemes]) |
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pred_text = phoneme_translator.translate(new_phonemes) |
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print("AFT_REV_TRANSLATE", pred_text) |
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phoneme_sentence = 'B-IH1-N B-L-UW1 AE1-T EH1-F TH-R-IY1 S-UW1-N' |
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pred_text = phoneme_translator.translate(phoneme_sentence) |
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print(f'PRED_TEXT: [{pred_text}]') |
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""" |
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total_samples = 1000 |
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total_wer = 0 |
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num_correct = 0 |
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num_phonemes_correct = 0 |
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video_path = '/media/milselarch/47FC4BC577667AAD/LRS2/lrs2_v1/mvlrs_v1/main/5535423430009926848/00002.mp4' |
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vid = dataset.process_vid(video_path) |
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pred_phonemes_sentence = lipnet_predictor.predict_video(vid)[0] |
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print('PRED PHONEMES', pred_phonemes_sentence) |
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pred_text = phoneme_translator.translate( |
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pred_phonemes_sentence, beam_size=BEAM_SIZE |
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) |
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avg_wer = total_wer / total_samples |
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print('PRED TEXT =', pred_text) |
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print(f'{num_correct}/{total_samples} samples correct') |
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print(f'{num_phonemes_correct}/{total_samples} phoneme samples correct') |
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print(f'average WER: {avg_wer}') |
