:py:mod:`medkit.audio.metrics.diarization`
==========================================

.. py:module:: medkit.audio.metrics.diarization


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

Classes
~~~~~~~

.. autoapisummary::

   medkit.audio.metrics.diarization.DiarizationEvaluatorResult
   medkit.audio.metrics.diarization.DiarizationEvaluator




.. py:class:: DiarizationEvaluatorResult


   
   Results returned by :class:`~.DiarizationEvaluator`.














   :Attributes:

       **der** : float
           Diarization Error Rate, combination of confusion, false alarm and missed
           detection

       **confusion** : float
           Ratio of time detected as speech but attributed to a wrong speaker
           (over `total_speech`)

       **false_alarm** : float
           Ratio of time corresponding to non-speech mistakenly detected as
           speech (over `total_speech`)

       **missed_detection** : float
           Ratio of time corresponding to undetected speech (over `total_speech`)

       **total_speech** : float
           Total duration of speech in the reference

       **support** : float
           Total duration of audio


   ..
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   .. py:attribute:: der
      :type: float

      

   .. py:attribute:: confusion
      :type: float

      

   .. py:attribute:: false_alarm
      :type: float

      

   .. py:attribute:: missed_detection
      :type: float

      

   .. py:attribute:: total_speech
      :type: float

      

   .. py:attribute:: support
      :type: float

      


.. py:class:: DiarizationEvaluator(turn_label: str = 'turn', speaker_label: str = 'speaker', collar: float = 0.0)


   
   Diarization Error Rate (DER) computation based on `pyannote`.

   The DER is the ratio of time that is not attributed correctly because of
   one of the following errors:

   - detected as non-speech when there was speech (missed detection);

   - detected as speech where there was none (false alarm);

   - attributed to the wrong speaker (confusion).

   This component expects as input reference documents containing the reference
   speech turn segments as well as corresponding predicted speech turn
   segments. The speech turn segments must each have a speaker attribute.

   Note that values of the reference and predicted speaker attributes (ie
   speaker labels) don't have to be the same, since they will be optimally
   remapped using the Hungarian algorithm.

   :Parameters:

       **turn_label** : str, default="turn"
           Label of the turn segments on the reference documents

       **speaker_label** : str, default="speaker"
           Label of the speaker attributes on the reference and predicted turn segments

       **collar** : float, default=0.0
           Margin of error (in seconds) around start and end of reference segments














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   .. py:method:: compute(reference: Sequence[medkit.core.audio.AudioDocument], predicted: Sequence[Sequence[medkit.core.audio.Segment]]) -> DiarizationEvaluatorResult

      
      Compute and return the DER for predicted speech turn segments, against reference annotated documents.


      :Parameters:

          **reference** : sequence of AudioDocument
              Reference documents containing speech turns segments with
              `turn_label` as label, each of them containing a speaker attribute
              with `speaker_label` as label.

          **predicted** : sequence of sequence of Segment
              Predicted segments containing each a speaker attribute with
              `speaker_label` as label. This is a list of list that must be of the
              same length and ordering as `reference`.

      :Returns:

          DiarizationEvaluatorResult
              Computed metrics













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   .. py:method:: _get_pa_annotation(segments: Sequence[medkit.core.audio.Segment]) -> pyannote.core.annotation.Annotation

      
      Convert list of medkit speech turn segments with speaker attribute to pyannote annotation object.
















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