[WIP] Add implementation of `_extract_fbank_features_batch` 
Author: ravenouse
Pull request: yes
Labels: name: Audio

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Following the discussion in #27159, this PR will add implementations of `_extract_fbank_features_batch` for the [audio models](https://github.com/search?q=repo%3Ahuggingface%2Ftransformers%20_extract_fbank_features&type=code) that use the `spectrogram` function for feature extractions. 

This PR is still a WIP and it is not ready for review yet.


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Comments:
cc @ylacombe 

Hi @ylacombe,

I have completed the integration for Seamless M4T and would appreciate it if you could review the current code and provide some feedback. 

I have also created a notebook for speed and accuracy checks, which you can find [here](https://colab.research.google.com/drive/1Q0yAMRjUaUZAD1KUTAgTiq-fObA4As_Z?usp=sharing).

I plan to work on other models once I receive your feedback.

Thank you so much for your time and help!

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.

Hi @ylacombe ,

I'm sorry for the delay in following up on this PR. I've been tied up with work commitments.

During my initial investigation, I realized that to conduct comprehensive and fair speed tests, it's necessary to implement the `_extract_fbank_features_batch` class function across all models.

I plan to work on this gradually and will actively provide updates as I make progress. If possible, could you please reopen the PR?

Thank you so much! 

Sorry for the delay! 

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_31579). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Thank you @ArthurZucker !

Hey @ravenouse, how are things going on ? Don't hesitate to tag me when you think the PR is ready for a review!


btw feel free to ping @eustlb if this is ready! 🤗 

Hi @ArthurZucker and @eustlb,

Thank you so much for your patience—I really appreciate it. Since last September, I’ve been adjusting to some changes in my work and life, but I am able to work on this PR actively now.

During testing of the CLAP model’s batch functions, I observed slight discrepancies compared to the original function. For example, over 100 iterations (batch size=32), the maximum difference was 0.001041. While the batch implementation offers a 1.27x speedup, I’d like to confirm: What’s the acceptable tolerance for numerical differences in this context?

Thanks a lot!