benchmark_hf_pipeline.py

from pprint import pprint
from transformers import pipeline
from datasets import load_dataset

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

# load model
pipe = pipeline(
    "automatic-speech-recognition",
    model=model_id,
    chunk_length_s=15,
    batch_size=64
)

# load sample audio (concatenate instances to create a long audio)
dataset = load_dataset("kotoba-tech/kotoba-whisper-eval", split="train")
x = dataset['audio'][0]
elapsed = {}
for x in dataset['audio']:
    start = time()
    transcription = pipe(x.copy(), generate_kwargs=generate_kwargs)
    elapsed[x['path']] = time() - start
pprint(elapsed)