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import whisper |
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from ipex_llm import optimize_model |
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def has_intersection(t1, t2): |
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if t1[1] < t2[0] or t2[1] < t1[0]: |
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return False |
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else: |
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return True |
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class AudioTranslator(): |
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def __init__(self, args): |
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self.model = whisper.load_model(args.whisper_version, download_root='checkpoints') |
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self.model = optimize_model(self.model) |
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def __call__(self, video_path): |
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""" |
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input: video_path (str) |
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output: audio_results (list) |
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""" |
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print("Extract the audio results.") |
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audio_results = self.model.transcribe(video_path, task = 'translate')["segments"] |
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print("Finished.") |
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return audio_results |
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def match(self, audio_results): |
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transcript = '' |
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for res in audio_results: |
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transcript += res['text'] + ' ' |
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return transcript |
