controllable `WaveformToFbankConverter` multithreading

**Describe the bug:**
`WaveformToFbankConverter` is running in multithread parallel.
This method (as possibly some others) uses [`parallel_for`](https://github.com/facebookresearch/fairseq2/blob/main/fairseq2n/src/fairseq2n/data/audio/detail/kaldi_fbank.cc#L94) statement for the execution.
Currently, there's no obvious way to control the number of threads it uses.
That could lead to some performance drawback (like threads/cpu oversubscription).
Moreover, it turns out that even when used inside `DataPipeline.map(...)`,
it does not respect the number of required parallel calls.

**Describe how to reproduce:**
```python
from fairseq2.data.data_pipeline import read_sequence
from fairseq2.data.audio import WaveformToFbankConverter

_convert_to_fbank = WaveformToFbankConverter(
num_mel_bins=80,
waveform_scale=2**15,
channel_last=True,
standardize=True,
device=torch.device("cpu"),
dtype=torch.float16)
def convert_to_fbank(wav):
return _convert_to_fbank({"waveform": torch.unsqueeze(wav, 1),
"sample_rate": 16_000})['fbank'].shape

xx = [torch.rand(10**5) for i in range(100)]
data_pipeline = read_sequence(xx).map(convert_to_fbank, num_parallel_calls=1).and_return()
list(iter(data_pipeline)) # this will use typically half of available cpus
```

**Describe the expected behavior:**
* a context manager to control number of threads the method uses (`with fairseq2_nb_threads(2): ...`)
* make `WaveformToFbankConverter` respect `num_parallel_calls` in data pipelining

**Environment:**
fairseq2==0.1.1+cu118
fairseq2n==0.1.1+cu118