controllable WaveformToFbankConverter multithreading

[artemru]

Describe the bug:
WaveformToFbankConverter is running in multithread parallel.
This method (as possibly some others) uses parallel_for 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:

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