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from time import time |
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from pprint import pprint |
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import torch |
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from transformers import pipeline |
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from datasets import load_dataset |
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generate_kwargs = {"language": "japanese", "task": "transcribe"} |
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model_id = "kotoba-tech/kotoba-whisper-v1.0" |
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pipe = pipeline( |
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"automatic-speech-recognition", |
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model=model_id, |
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torch_dtype=torch.float32 |
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) |
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test_audio = [ |
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"kotoba-whisper-eval/audio/manzai1.wav", |
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"kotoba-whisper-eval/audio/manzai2.wav", |
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"kotoba-whisper-eval/audio/manzai3.wav", |
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"kotoba-whisper-eval/audio/long_interview_1.wav", |
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] |
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elapsed = {} |
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for x in test_audio: |
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start = time() |
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transcription = pipe(x, generate_kwargs=generate_kwargs) |
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elapsed[x] = time() - start |
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pprint(transcription) |
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pprint(elapsed) |
