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whisper-large

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You should be able to! The trick is to properly set the ‘forced_decoder_ids’ with the processor. Otherwise we are looking into adding the la language detection automatically

@Spotex93 , where you able to find a solution to the transcription problem? I ran into the same question. Kind regards

You can set the forced_decoder_ids as follows:

from transformers import WhisperForConditionalGeneration, WhisperProcessor

checkpoint = "openai/whisper-large"

model = WhisperForConditionalGeneration.from_pretrained(checkpoint)
processor = WhisperProcessor.from_pretrained(checkpoint)

print("Default:")
print(model.config.forced_decoder_ids)
print(processor.batch_decode([i[1] for i in model.config.forced_decoder_ids]))

# now change to Hindi (hi)
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="hi", task="transcribe")

print("\nHindi:")
print(model.config.forced_decoder_ids)
print(processor.batch_decode([i[1] for i in model.config.forced_decoder_ids]))

Print Output:

Default:
[[1, 50259], [2, 50359], [3, 50363]]
['<|en|>', '<|transcribe|>', '<|notimestamps|>']

Hindi:
[(1, 50276), (2, 50359), (3, 50363)]
['<|hi|>', '<|transcribe|>', '<|notimestamps|>']

@muertinho for now I switched to https://replicate.com/openai/whisper/api#input-audio since to me it seems that it is not possible with the currently provided inference api. Maybe you'd have to host your own.

@Spotex93 thank you for providing me with the link. I see the same issue you mentioned above and will look into replicate.com. @sanchit-gandhi thank you for your reply and efforts as well. However, I am looking for an option to run it over a cloud API.

@muertinho tell me if you found a better solution!