evaluate

Evaluate the enhanced signals using HAAQI and Whisper

recipes.cad2.task1.baseline.evaluate.compute_intelligibility(enhanced_signal: ndarray, segment_metadata: dict, scorer: Module, listener: Listener, sample_rate: int, save_intermediate: bool = False, path_intermediate: str | Path | None = None, equiv_0db_spl: float = 100) → tuple[float, float, dict][source]

Compute the Intelligibility score for the enhanced signal using the Whisper model.

To the enhanced signal, we apply the MSGB hearing loss model before transcribing with Whisper.

Parameters:

  • enhanced_signal – The enhanced signal

  • segment_metadata – The metadata of the segment

  • scorer – The Whisper model

  • listener – The listener

  • sample_rate – The sample rate of the signal

  • save_intermediate – Save the intermediate signal

  • path_intermediate – The path to save the intermediate signal

  • equiv_0db_spl – The equivalent 0 dB SPL

Returns:

The intelligibility score for the left and right channels

recipes.cad2.task1.baseline.evaluate.compute_quality(reference_signal: ndarray, enhanced_signal: ndarray, listener: Listener, config: DictConfig) → tuple[float, float][source]

Compute the HAAQI score for the left and right channels

recipes.cad2.task1.baseline.evaluate.load_reference_signal(path: str | Path, start_sample: int | None, end_sample: int | None, level_luft: float = -40.0) → ndarray[source]

Load the reference signal

recipes.cad2.task1.baseline.evaluate.make_scene_listener_list(scenes_listeners: dict, small_test: bool = False) → list[source]

Make the list of scene-listener pairing to process

Parameters:

  • scenes_listeners (dict) – Dictionary of scenes and listeners.

  • small_test (bool) – Whether to use a small test set.

Returns:

List of scene-listener pairings.

Return type:

list

recipes.cad2.task1.baseline.evaluate.normalise_luft(signal: ndarray, sample_rate: float, target_luft: float = -40.0) → ndarray[source]

Normalise the signal to a target loudness level. :param signal: input signal to normalise :param sample_rate: sample rate of the signal :param target_luft: target loudness level in LUFS.

Returns:

normalised signal

Return type:

np.ndarray

recipes.cad2.task1.baseline.evaluate.run_compute_scores(config: DictConfig) → None[source]

Compute the scores for the enhanced signals