ToDo: Serialize pickle byte code in numpy array, reduce multiprocessing memory usage

[boeddeker]

In [1] it is demonstrated to address the issue of the memory consumption, when multiprocessing is used. Although, we don't use multiprocessing (it's implemented, but threads are usually faster for audio data and avoid the fork issues mentioned in [1]), the idea can be integrated in our dataset implementation, with a small improvement for the memory consumption of large dataset. We have already a pickle based serialization, so there will ne no additional overhead.

Code from [1]:

class NumpySerializedList:
  def __init__(self, lst: list[Any]):
    lst = [np.frombuffer(pickle.dumps(x), dtype=np.uint8) for x in lst]
    self._addr = np.cumsum([len(x) for x in lst])
    self._lst = np.concatenate(lst)

  def __len__(self):
    return len(self._addr)

  def __getitem__(self, idx: int):
    start = 0 if idx == 0 else self._addr[idx - 1]
    end = self._addr[idx]
    return pickle.loads(memoryview(self._lst[start:end]))

[1] https://ppwwyyxx.com/blog/2022/Demystify-RAM-Usage-in-Multiprocess-DataLoader