benchmark.py

from time import time
from pprint import pprint
import torch
from transformers import pipeline
from datasets import load_dataset

# config
generate_kwargs = {"language": "japanese", "task": "transcribe"}
model_id = "kotoba-tech/kotoba-whisper-v1.0"

# load model
pipe = pipeline(
    "automatic-speech-recognition",
    model=model_id,
    torch_dtype=torch.bfloat16
)

test_audio = [
    "kotoba-whisper-eval/audio/long_interview_1.wav",
    "kotoba-whisper-eval/audio/manzai1.wav",
    "kotoba-whisper-eval/audio/manzai2.wav",
    "kotoba-whisper-eval/audio/manzai3.wav"
]
elapsed = {}
for x in test_audio:
    start = time()
    transcription = pipe(x, generate_kwargs=generate_kwargs)
    elapsed[x] = time() - start
pprint(elapsed)