deepextractor.model
===================

.. py:module:: deepextractor.model

.. autoapi-nested-parse::

   High-level DeepExtractor model wrapper for inference.







Module Contents
---------------

.. py:data:: logger

.. py:class:: DeepExtractorModel(checkpoint: str = 'DeepExtractor_257', checkpoint_filename: str = CHECKPOINT_BILBY, checkpoint_dir: str | None = None, device: str | torch.device | None = None, scaler_path: str | None = None, n_fft: int = 512, win_length: int = 64, hop_length: int = 32)

   High-level wrapper around a pretrained DeepExtractor UNET2D model.

   Bundles the PyTorch model, StandardScaler, and STFT parameters into a
   single object so callers don't need to manage them separately.

   :param checkpoint: Model name / checkpoint key. Defaults to ``"DeepExtractor_257"``.
   :type checkpoint: str
   :param checkpoint_filename: Checkpoint file name within the model subdirectory on HuggingFace Hub
                               or local ``checkpoint_dir``. Defaults to ``CHECKPOINT_BILBY``.
   :type checkpoint_filename: str
   :param checkpoint_dir: Local directory to search for checkpoint files before falling back to
                          HuggingFace Hub. Pass ``None`` to always use the Hub.
   :type checkpoint_dir: str | None
   :param device: Compute device. Auto-detects CUDA if available when ``None``.
   :type device: str | torch.device | None
   :param scaler_path: Path to the scaler ``.pkl`` file. Defaults to the bundled
                       ``assets/scaler_bilby.pkl``.
   :type scaler_path: str | None
   :param n_fft: STFT FFT size. Default 512.
   :type n_fft: int
   :param win_length: STFT window length. Default 64.
   :type win_length: int
   :param hop_length: STFT hop length. Default 32.
   :type hop_length: int


   .. py:attribute:: device


   .. py:attribute:: n_fft
      :value: 512



   .. py:attribute:: win_length
      :value: 64



   .. py:attribute:: hop_length
      :value: 32



   .. py:method:: background(noisy_input: numpy.ndarray) -> numpy.ndarray

      Estimate the background (noise-only) component.

      :param noisy_input: 1-D array of shape ``(T,)`` or 2-D batch of shape ``(N, T)``.
      :type noisy_input: np.ndarray

      :returns: Background estimate, same shape as ``noisy_input``.
      :rtype: np.ndarray



   .. py:method:: reconstruct(noisy_input: numpy.ndarray) -> numpy.ndarray

      Extract the transient signal by subtracting the predicted background.

      :param noisy_input: 1-D array of shape ``(T,)`` or 2-D batch of shape ``(N, T)``.
      :type noisy_input: np.ndarray

      :returns: Reconstructed signal, same shape as ``noisy_input``.
      :rtype: np.ndarray