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import os
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from pathlib import Path
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from gector import GecBERTModel
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from faster_whisper import WhisperModel, BatchedInferencePipeline
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from transformers.models.whisper.english_normalizer import BasicTextNormalizer
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from text_processing.inverse_normalize import InverseNormalizer
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import time
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inverse_normalizer = InverseNormalizer('vi')
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current_dir = Path(__file__).parent.as_posix()
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whisper_model = WhisperModel("pho_distill_q8", device="auto", compute_type="auto")
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batched_model = BatchedInferencePipeline(model=whisper_model, use_vad_model=True, chunk_length=15)
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gector_model = GecBERTModel(
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vocab_path=os.path.join(current_dir, "gector/vocabulary"),
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model_paths=[os.path.join(current_dir, "gector/Model_GECTOR")],
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split_chunk=True
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)
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normalizer = BasicTextNormalizer()
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start = time.time()
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segments, info = batched_model.transcribe("HA1.wav", language="vi", batch_size=32)
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transcriptions = [segment.text for segment in segments]
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normalized_transcriptions = [inverse_normalizer.inverse_normalize(normalizer(text)) for text in transcriptions]
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corrected_texts = gector_model(normalized_transcriptions)
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print(''.join(text for text in corrected_texts))
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print(time.time() - start)
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