[Gemma3N] Audio processing issue
Author: NickLucche
Pull request: no
Labels: name: bug

### System Info

```
- `transformers` version: 4.53.2
- Platform: Linux-5.15.0-113-generic-x86_64-with-glibc2.35
- Python version: 3.12.11
- Huggingface_hub version: 0.33.4
- Safetensors version: 0.5.3
- Accelerate version: 1.9.0
- Accelerate config: 	not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.7.1+cu126 (CUDA)
- 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?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: NVIDIA H100 80GB HBM3
```

### Who can help?

_No response_

### Information

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

### Tasks

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

### Reproduction

Hey, I am working on integrating Gemma3N on vLLM here https://github.com/vllm-project/vllm/pull/20495.
We're testing out a few random shapes for the audio inputs in our tests and we found inconsistencies between the returned `input_features` and `input_features_mask` shape, which we're currently use to "unpad".

Here's a script for reproducing the issue
```
from transformers import AutoProcessor
import numpy as np
processor = AutoProcessor.from_pretrained("google/gemma-3n-E2B-it")

prompt = "<audio_soft_token>"
for i in range(64, 2048):
    # eg audio = np.random.randn(588,)
    audio = np.random.randn(i,)
    mm_data = {
        "audio": [audio],
        "sampling_rate": 16000,
    }
    inputs = processor(
        text=prompt,
        **mm_data,
        return_tensors="pt"
    )
    out = inputs["input_features"]
    mask = inputs["input_features_mask"]
    if out.shape[:2] != mask.shape[:2]:
        print("Audio length", i)
        print(out.shape)
        print(mask.shape)
```

And here's a few outputs from the scan run:
```
Audio length 513
torch.Size([1, 128])
torch.Size([1, 4])
Audio length 514
torch.Size([1, 128])
torch.Size([1, 4])
...

Audio length 639
torch.Size([1, 128])
torch.Size([1, 4])
Audio length 640
torch.Size([1, 128])
torch.Size([1, 4])
``` 


cc @DarkLight1337 @hmellor 

### Expected behavior

If you run the same script for a few other sizes:
```
Audio length 64
torch.Size([1, 0, 128])
torch.Size([1, 0])
Audio length 128
torch.Size([1, 0, 128])
torch.Size([1, 0])
Audio length 256
torch.Size([1, 0, 128])
torch.Size([1, 0])
Audio length 512
torch.Size([1, 0, 128])
torch.Size([1, 0])
Audio length 1024
torch.Size([1, 4, 128])
torch.Size([1, 4])
Audio length 2048
torch.Size([1, 10, 128])
torch.Size([1, 10])
```

Comments:
Given the range of failing values, I suspect `input_features` with shape `(1, 1, 128)` is accidentally flattened to `(1, 128)` somehow. This violates the invariant of being able to recover the original features by

```py
features_unpadded = [f[mask] for f, mask in zip(inputs["input_features"], inputs["input_features_mask"])]
```

cc @ArthurZucker @Isotr0py PTAL

Seems that the zero-size dimension comes from the unfolding operation:
https://github.com/huggingface/transformers/blob/dedcbd6e3d52b9e49031df7ade1eafe75fca2f60/src/transformers/models/gemma3n/feature_extraction_gemma3n.py#L98-L102

cc @eustlb @zucchini-nlp - not sure who to ping for audio/language models!

Sorry for the late answer, thanks @Isotr0py for the fix!