Fair Benchmarking of Faster-Whisper - Parameter equivalents to Hugginface

[asusdisciple]

I want to benchmark faster-whisper and some pipeline whisper implementations of whisper in huggingface.
For the sake of fairness I would like to parametrize the models as equally as possible.

In HF you have different generation possibilities which are:

greedy decoding if num_beams=1 and do_sample=False
contrastive search if penalty_alpha>0 and top_k>1
multinomial sampling if num_beams=1 and do_sample=True
beam-search decoding if num_beams>1 and do_sample=False
beam-search multinomial sampling if num_beams>1 and do_sample=True
diverse beam-search decoding if num_beams>1 and num_beam_groups>1

How would I for example reproduce greedy decoding in faster-whisper? Is there a do_sample parameter?
Should I set best_of = 1 and beam_size = 1? Also in case I set do_sample = True in HF would that be
equal to setting best_of = 5? Maybe you can share some insights with me, best case I want to reproduce all of the above strategies.

Best regards

[MahmoudAshraf97]

@BBC-Esq and I are currently working on this, check #974

[BBC-Esq]

I'll send an invite to the repo if he wants to help out or just kibitz. Like @MahmoudAshraf97 I've been inundated with other stuff but do plan to get back to the benchmarking in the very near future.