hearing.diarize

Diarization and speaker labelling for the hearing pipeline.

Two ideas, one interface (Diarizer):

1. The channel trick (default, free). Because capture keeps the mic and the system audio on separate channels, we already know who is local: the MIC channel is “me”, the SYSTEM channel is the remote participants. ChannelTrickDiarizer just maps channel -> speaker. No model, no

   audio, no error from overlap on the local side. This beats voice fingerprinting for the “is it me?” question.

2. Real diarization (the upgrade). To separate multiple remote speakers you need acoustic diarization. PyannoteDiarizer wraps pyannote.audio and runs only on the system channel. A diarized transcript is standoff interval annotation with a speaker tier — see the annotation-systems skill.

See the hearing-diarization skill for the full landscape (pyannote vs NeMo vs WhisperX vs SpeechBrain, DER numbers, enrollment).

Classes

ChannelTrickDiarizer([me_label, them_label])	Label speakers from the capture channel — the free, reliable default.
PyannoteDiarizer([model, hf_token, ...])	Acoustic diarization via pyannote.audio (the upgrade path).

class hearing.diarize.ChannelTrickDiarizer(me_label='me', them_label='them')

Bases: object

Label speakers from the capture channel — the free, reliable default.

MIC -> me_label (the local user), SYSTEM -> them_label (a remote participant). Segments already carrying a speaker, or coming from a MIXED channel, are passed through unchanged (there is nothing to infer).

This is the architecture’s default diarizer whenever channel information is present. To distinguish individual remote speakers, compose this with (or replace it by) PyannoteDiarizer.

assign_speakers(segments, *, audio=None, sample_rate=None)

Yield segments with .speaker set from their .channel.

Return type:

Iterable[TranscriptSegment]

class hearing.diarize.PyannoteDiarizer(model='pyannote/speaker-diarization-3.1', hf_token=None, speaker_prefix='spk_')

Bases: object

Acoustic diarization via pyannote.audio (the upgrade path).

Runs the pretrained pipeline over the audio, then assigns each segment the speaker whose diarized turn overlaps it most. Intended to run on the system channel only (the mic channel is already known to be “me”).

Requires pip install hearing[diarize] and a Hugging Face token with the pyannote model licenses accepted. This implementation is provided for the diarization milestone; it is import-guarded so the package works without it.

assign_speakers(segments, *, audio=None, sample_rate=None)

Yield segments labelled with the most-overlapping diarized speaker.

Return type:

Iterable[TranscriptSegment]