Fix Audio Classification Pipeline top_k Documentation Mismatch and Bug #35736
Author: sambhavnoobcoder
Pull request: yes

## Problem Statement
There were two issues with the audio classification pipeline's `top_k` parameter:
1. Documentation mismatch: The docs stated that when `top_k=None`, all labels should be returned, but this wasn't implemented
2. Runtime error: When `top_k=None` and a model had fewer labels than the default (5), the pipeline would crash

Fixes : #35736 

## Root Cause Analysis
After investigating the implementation, we found:
1. The `__init__` method was unconditionally setting a default `top_k=5`, even when explicitly set to `None`
2. The `_sanitize_parameters` method wasn't properly handling the `None` case, leading to potential crashes when models had fewer labels than the requested/default `top_k`

## Solution
The fix involves:
1. Properly handling `top_k=None` in initialization to respect the user's intent to get all labels
2. Updating `_sanitize_parameters` to use the model's total number of labels when `top_k=None`
4. Maintaining backward compatibility by keeping the default `top_k=5` when not explicitly specified
5. Adding comprehensive tests to verify the behavior

## Implementation Details
Changes were made to:
1. `AudioClassificationPipeline.__init__`: Modified to properly handle `None` value
2. `AudioClassificationPipeline._sanitize_parameters`: Updated to use all labels when `top_k=None`
3. Added new test file `test_audio_classification_top_k.py` with three test cases:
   - Testing `top_k=None` returns all labels
   - Testing behavior with models having fewer labels
   - Testing `top_k` greater than available labels

## Testing
Added comprehensive tests that verify:
1. When `top_k=None`, all labels are returned
2. Models with fewer labels than the default work correctly
3. Large `top_k` values are properly capped to available labels

## Test Results
<img width="598" alt="Screenshot 2025-01-19 at 2 21 30 PM" src="https://github.com/user-attachments/assets/3e698ec1-bf07-4859-a059-e93e51d4ce59" />

All test cases pass successfully, confirming the fix works as intended while maintaining backward compatibility.

**Note About Warning:**
During test execution, we see a deprecation warning: UserWarning: 

`Passing gradient_checkpointing to a config initialization is deprecated and will be removed in v5 Transformers. Using model.gradient_checkpointing_enable() instead... `

This warning is unrelated to our `top_k` changes. It comes from the model configuration initialization and is about the deprecation of setting gradient checkpointing via config. This is a broader change planned for Transformers v5 to move gradient checkpointing configuration from model initialization to explicit method calls. It doesn't affect our audio classification pipeline's functionality or the `top_k` parameter behavior.

the warning would go away by exploring a better model with multiple labels supporting gradient checkpointing even now , but i decided to leave the same model in place for originality purposes as it doesn't affect this PR in any way or form .

**cc:** @Rocketknight1 


Comments:
Yes, this looks clean to me, and I really appreciate the broad test coverage!

cc @wilke0818, does this fix the issues you saw?

Yep just tried on the original code I had, as well as did some stress testing of it and it seems to work in all scenarios I can think of. Thanks for the quick responses and fixes! Also did a quick check and it doesn't seem like this issue exists in the other classification pipelines (I don't see any top_k default value setting).

Cool . Then I think we can merge this @Rocketknight1 ?

Hey @Rocketknight1 , sorry for inconvenience on a merged PR , but i was looking through my past contributions and i found that even though i was getting PRs merged like this one , or others like [35859](https://github.com/huggingface/transformers/pull/35859) , [35858](https://github.com/huggingface/transformers/pull/35858) , [35735](https://github.com/huggingface/transformers/pull/35735) or [36345](https://github.com/huggingface/transformers/pull/36345) , I could not see my name / image on the contributors board/list for this repo/ project  . Could you look into it and tell me why is that happening ? Is there some other process i am missing out to be listed on the board ? i already posted about this on discord first , but was told to discuss this directly on the github issue itself , hence the query. 

Hi @sambhavnoobcoder, do you mean here? https://github.com/huggingface/transformers/graphs/contributors

If so, that's part of Github, not managed by us. It's sorted by total commits (PRs merged) to the repo. You'll turn up there eventually!

yes @Rocketknight1 , that was what i was looking at . actually i added the number of commits from all of my PRs that were merged so far , and thought that i could've been there on the basis of that number , but if the metrics/requirements for the board are something different , then i am not sure . 

Hi @sambhavnoobcoder, actually, one single PR being merged counts as one "commit" to Transformers. Even though the PR branch has multiple commits, it's squished into a single update to the main branch that is recorded as a single commit.

That makes it very hard for the rest of us to catch up with founders like @julien-c and @thomwolf who were able to just make lots of raw commits directly to the repo in the early days :sweat_smile: 

ohh , that makes perfect sense !! haha , i was wondering if i was doing something wrong , but this makes perfect sense . thanks for the quick reply .