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