Repository: transformers
Issue: Audio-Classification Pipeline top_k Documentation mismatch and bug (possibly generalizes to any classification pipelines)
Labels: bug

### System Info

- `transformers` version: 4.48.0
- Platform: macOS-14.6-arm64-arm-64bit
- Python version: 3.12.4
- Huggingface_hub version: 0.27.1
- Safetensors version: 0.5.2
- Accelerate version: 1.2.1
- Accelerate config: 	not found
- PyTorch version (GPU?): 2.5.1 (False)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: Neither/NA

### Who can help?

@Rocketknight1 @HERIUN 

### Information

- [ ] The official example scripts
- [x] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)

### Reproduction

1. For the documentation mismatch, running the below is enough:
```python
from transformers import pipeline
import torch
import numpy as np

# model_name = 'audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim'
model_name = 'pollner/distilhubert-finetuned-ravdess'
top_k = None
device = 'cuda' if torch.cuda.is_available() else 'cpu'
classification_pipeline = pipeline(
                "audio-classification",
                model=model_name,
                # top_k=top_k, 
                #function_to_apply='none',
                device=device,
            )

# dummy signal
sampling_rate = 16000
signal = np.zeros((sampling_rate), dtype=np.float32)
print(classification_pipeline(signal))
```
2. Setting top_k to None in the initialization of the pipeline causes breaking behavior if the number of labels is less than 5
```python
from transformers import pipeline
import torch
import numpy as np

model_name = 'audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim'
#model_name = 'pollner/distilhubert-finetuned-ravdess'
top_k = None
device = 'cuda' if torch.cuda.is_available() else 'cpu'
classification_pipeline = pipeline(
                "audio-classification",
                model=model_name,
                top_k=top_k, 
                #function_to_apply='none',
                device=device,
            )

# dummy signal
sampling_rate = 16000
signal = np.zeros((sampling_rate), dtype=np.float32)
print(classification_pipeline(signal))
```
Stak trace:
```python
Some weights of Wav2Vec2ForSequenceClassification were not initialized from the model checkpoint at audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim and are newly initialized: ['classifier.bias', 'classifier.weight', 'projector.bias', 'projector.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Device set to use cpu

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[16], line 20
     18 sampling_rate = 16000
     19 signal = np.zeros((sampling_rate), dtype=np.float32)
---> 20 print(classification_pipeline(signal))
     21 # example_audio = {
     22 #     'raw': signal,
     23 #     'sampling_rate': sampling_rate
     24 # }

File ~/Library/Caches/pypoetry/virtualenvs/senselab-MihWUM64-py3.12/lib/python3.12/site-packages/transformers/pipelines/audio_classification.py:141, in AudioClassificationPipeline.__call__(self, inputs, **kwargs)
    103 def __call__(
    104     self,
    105     inputs: Union[np.ndarray, bytes, str],
    106     **kwargs,
    107 ):
    108     """
    109     Classify the sequence(s) given as inputs. See the [`AutomaticSpeechRecognitionPipeline`] documentation for more
    110     information.
   (...)
    139         - **score** (`float`) -- The corresponding probability.
    140     """
--> 141     return super().__call__(inputs, **kwargs)

File ~/Library/Caches/pypoetry/virtualenvs/senselab-MihWUM64-py3.12/lib/python3.12/site-packages/transformers/pipelines/base.py:1362, in Pipeline.__call__(self, inputs, num_workers, batch_size, *args, **kwargs)
   1354     return next(
   1355         iter(
   1356             self.get_iterator(
   (...)
   1359         )
   1360     )
   1361 else:
-> 1362     return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)

File ~/Library/Caches/pypoetry/virtualenvs/senselab-MihWUM64-py3.12/lib/python3.12/site-packages/transformers/pipelines/base.py:1370, in Pipeline.run_single(self, inputs, preprocess_params, forward_params, postprocess_params)
   1368 model_inputs = self.preprocess(inputs, **preprocess_params)
   1369 model_outputs = self.forward(model_inputs, **forward_params)
-> 1370 outputs = self.postprocess(model_outputs, **postprocess_params)
   1371 return outputs

File ~/Library/Caches/pypoetry/virtualenvs/senselab-MihWUM64-py3.12/lib/python3.12/site-packages/transformers/pipelines/audio_classification.py:227, in AudioClassificationPipeline.postprocess(self, model_outputs, top_k, function_to_apply)
    225 else:
    226     probs = model_outputs.logits[0]
--> 227 scores, ids = probs.topk(top_k)
    229 scores = scores.tolist()
    230 ids = ids.tolist()

RuntimeError: selected index k out of range
```

### Expected behavior

1. For the documentation mismatch, the [documentation](https://huggingface.co/docs/transformers/v4.47.1/en/main_classes/pipelines#transformers.AudioClassificationPipeline.__call__.top_k) states that if no `top_k` is provided (or None value), then we should return all labels. The magic number of 5 in the initialization of the pipeline causes this to not be the case for models that are returning more than 5 labels (code example above).
2. Setting `top_k` to None in the initializer should not create possible bugs for models with less than 5 (the magic number) labels. Instead the code should set the `top_k` to the maximum allowable labels. Additionally, the above is still true, that if we set to `None` in the pipeline initialization and do not overwrite it on any calls, we should output all labels (expected behavior number one). This issue is from 4.47.0 due to [this change](https://github.com/huggingface/transformers/commit/854dc7941bf250ab27610f114d048ec9ca08ad1d) not taking into account of a user sending in `None` for the argument.