Speaker Id

Speaker ID enrollment

Give diarized transcripts real names, and repair the drift that clustering diarizers produce on long recordings. Post-hoc relabeling — it does not create diarization, it relabels an existing one.

pi-voiceid analyze --srt talk.srt            # drift report, no enrollment needed
pi-voiceid enroll  --name "Alice" --srt talk.srt --label "Speaker 1"
pi-voiceid label   --srt talk.srt --dry-run  # always dry-run first
pi-voiceid label   --srt talk.srt            # writes talk.named.srt

The source SRT is never overwritten. Everything runs locally on CPU via sherpa-onnx; no audio or embedding leaves the machine.

How it works

1. Each anonymous cluster is profiled with a speaker-embedding model, sampling segments across the whole timeline (drift is temporal).
2. Cluster centroids are centered (a multi-speaker mean subtracted) and compared by cosine to the enrolled voiceprints.
3. A cluster is named only when the absolute threshold, the margin over the runner-up, and a minimum share of agreeing segments all pass. Otherwise the anonymous label is kept — never guessed.
4. Several clusters may map to one name: that is the drift repair.

Setup (one time)

The embedding model is not vendored. On first enroll/label it is fetched into ~/.pi/models/speaker/ (28 MB). Model choice is the single biggest accuracy factor — see BENCHMARK.md. Do not "upgrade" to a bigger model without re-running the benchmark: the 114 MB VoxCeleb leader scored worse than the 28 MB default on real meeting audio.

The native binding sherpa-onnx-node is an optional dependency. When it is absent, transcription still works and pi-voiceid fails with an actionable message naming the dependency and the install command.

Voiceprint library

Default store: ~/.pi/voiceprints/voiceprints.json. Override with --store or the PI_VOICEPRINT_STORE env var. This is biometric-derived data about identifiable people: it lives outside the repo by default, it is overridable, and it must never be committed. If you point --store inside a repository, add it to that repo's .gitignore.

enroll also embeds the source recording's other speakers, so the centering mean stays multi-speaker (centering one speaker by their own mean cancels the signal). Those third parties therefore end up in the store as unnamed contributions. Erase with forget:

pi-voiceid forget --name "Alice"        # drops Alice's voiceprint + her contributions
pi-voiceid forget --recording <id>      # drops a whole recording's contributions
pi-voiceid list                         # shows the cohort and which clusters are unnamed

A never-enrolled speaker swept into the pool has no name, so only forget --recording <id> reaches them. list makes that visible.

Procedure

1. Check for drift first

pi-voiceid analyze --srt talk.srt

Prints per-cluster coherence and a channel-centered cluster-to-cluster cosine matrix. Pairs at or above --drift-threshold (default 0.55) are flagged as likely the same person split in two. analyze needs no library. The 0.55 default is weakly calibrated (n = 2) — treat it as a starting point.

2. Enroll a voice

Best: from a cluster you have already identified in an SRT (plenty of audio).

pi-voiceid enroll --name "Csákány Róbert" --srt talk.srt --label "Speaker 1"

From a standalone clip:

pi-voiceid enroll --name "Kovács Dániel" --audio sample.m4a --start 12 --end 75

Re-running the same name merges into the existing voiceprint (segment-count weighted); --replace resets it instead. Enroll the same person from several recordings — the cheapest accuracy win.

3. Label a transcript

pi-voiceid label --srt talk.srt --dry-run
pi-voiceid label --srt talk.srt

Always read the dry-run decision table. --output overrides the sibling *.named.srt (and is required for the same-file refusal to be reachable). --relabel allows an already-named transcript and leaves clusters whose name is already in the library untouched, so the iterative workflow (label, enroll one more person, label again) is safe.