[Misc] Benchmarks for audio models

[NickLucche]

Implements feature requested in #16354.

Test with:

# server
vllm serve openai/whisper-large-v3-turbo

# client
python3 benchmarks/benchmark_serving.py \       
    --backend openai-audio \
    --dataset-name hf \
    --dataset-path edinburghcstr/ami --hf-subset ihm \
    --model openai/whisper-large-v3-turbo \
    --num-prompts 1000 \
    --endpoint /v1/audio/transcriptions \
    --save-result \
    2>&1 | tee benchmark_whisper.txt

It's still a draft because I want to sweep through the datasets first, but reviews are welcome!
cc @DarkLight1337

FIX #16354

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[NickLucche]

PS: these datasets need to be granted access manually on hf. Posting here in case we decide to run performance checks on them:
https://huggingface.co/datasets/speechcolab/gigaspeech
https://huggingface.co/datasets/kensho/spgispeech

[DarkLight1337]

Can we abstract this to a class-level flag on the dataset class?

[DarkLight1337]

Maybe also need this for the backends

[DarkLight1337]

Actually that may take more effort, let's just merge this PR first

[NickLucche]

I can make a list like OPENAI_COMPATIBLE_BACKENDS but I wouldn't tie it to the dataset

[NickLucche]

actually maybe not even that because current check is on the specific backend key/name rather than on the function it uses

[NickLucche]

Actually that may take more effort, let's just merge this PR first

right I missed this comment I was answering to an old version lol

[DarkLight1337]

Can you merge from main to try to fix the CI errors?