Clap processor: remove wasteful np.stack operations
Author: m-bain
Pull request: yes

# What does this PR do?

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Upon profiling, it showed some strange result that the ClapProcessor was taking **0.5s** to apply `_get_input_mel(...)` on short audio (less than 10s), whereas medium length audio (10s-20s) was taking only **0.02s**

As it turns out there was a wasteful `np.stack` operation on the 1-D waveform numpy array, meaning that the 1-D array is unpacked then stacked back together again, with no effect. This PR removes this wasteful op and short audio is now also processed in **0.02s**

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Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_27454). All of your documentation changes will be reflected on that endpoint.

@amyeroberts hows this ?

```python
import time
import numpy as np

waveform = np.random.rand(100_000)
n_repeat = 10

t1_p = time.time()
prev_impl = np.stack(np.tile(waveform, n_repeat))
t2_p = time.time()

t1_n = time.time()
new_impl = np.tile(waveform, n_repeat)
t2_n = time.time()

assert (prev_impl == new_impl).all()
print(f"Time to process [prev. impl.]: {t2_p-t1_p:.3f}s")
print(f"Time to process [new. impl.]: {t2_n-t1_n:.3f}s")
```
```plaintext
Time to process [prev. impl.]: 0.883s
Time to process [new. impl.]: 0.001s
```

@m-bain Thanks!