Comments (4)
cc @sanchit-gandhi @kamilakesbi
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Hey @maxkvbn, super sorry for the late reply here. I've been trying to reproduce the issue locally, but with every case I've tried the current code on main
works as expected. For example, here's a reproducer that uses long-form generation with a single audio sample and word-level timestamps:
from transformers import pipeline, AutoProcessor, WhisperForConditionalGeneration
from transformers.utils import is_accelerate_available
from datasets import load_dataset
processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", low_cpu_mem_usage=is_accelerate_available())
pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny.en")
dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
sample = dataset[0]["audio"]
text = pipe(sample, batch_size=1, return_timestamps="word")
Could you please share a reproducer for the issue that I can run end-to-end? E.g. by sharing the audio file you're using, or updating the code example I've shared to trigger the error.
I've had a quick glance over your PR and the changes seem well-addressed, so if I can confirm it's a fix we need I'm confident we can merge it quickly.
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Hey @sanchit-gandhi.
Did you try with the uploaded audio from #31683 (comment)?
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