overcast-voiceprint

name	overcast-voiceprint
description	Voice lineup — enroll a reference speaker into a local voice-print index, then rank WHERE that speaker talks inside a clip and WHICH recordings contain them, reading the anchored 0–100 rank score and its margin, and corroborating before naming anyone.

overcast-voiceprint

Use this skill to answer "is this the same speaker, and where do they talk?" with the local voice-print DB (pyannote/wespeaker speaker embeddings — no media leaves the case, ungated windowed default). It verifies a VOICE, not a phrase. Use the broad overcast skill and overcast/reference/verbs.md for exact flags.

Prerequisites

overcast doctor --json                 # confirm uv + visual-db (pyannote) are ready
scripts/visual-db-uv.sh --voice        # install pyannote.audio (once per machine)
overcast case init --json
overcast index create voices --type voice-print --local --json

Workflow

1. Enroll the reference speaker (and any known recordings). voice add embeds the clip's voiced windows into the index:

overcast voice add ./known-speaker.wav --index voices --json     # the reference person
overcast voice add ./interview.m4a --index voices --json          # other recordings to search

2. Rank WHERE the reference speaker talks inside a clip (pairwise, windowed scan). voice match <clip> <sample> scans <clip> for the <sample> speaker; --diarize upgrades to overlap-aware diarize-then-match (HF_TOKEN + accepted pyannote license, windowed fallback if ungated):

overcast voice match ./call.wav ./known-speaker.wav --json                 # where in call.wav does the sample speaker talk?
overcast voice match ./call.wav ./known-speaker.wav --diarize --min-margin 10 --json  # overlap-aware; gate on margin

3. Rank WHICH enrolled recordings contain the reference speaker (index search):

overcast voice match ./known-speaker.wav --index voices --json   # members ranked by the speaker's presence

4. Read the score honestly and corroborate before concluding. similarity is an anchored-cosine 0–100 RANK score (never 0–1) plus a raw cosine; --min-margin gates best-vs-runner-up. Corroborate with content (a listen transcript) before naming anyone, then promote through triage:

overcast listen ./call.wav --json                                # what was said, to corroborate WHO
overcast finding list --state triage --json                      # a >=80 voice match auto-suggests a lead
overcast finding accept <id> --target <target-id> --json
overcast note "call.wav 00:38-01:12 ranks 87/100 for the known speaker (margin 22); transcript corroborates" --ref <voice-match-record-id> --at 38-72 --confidence medium --json
overcast brief --export ./voiceprint.html --json

Output

For each match: the reference sample, WHERE (time windows) or WHICH (member recordings) the speaker appears, the 0–100 rank score + raw cosine + margin, and the corroborating transcript content — every claim cited to a record.id + media.at. Report a time WINDOW, not a single frame.

Caveats

Not liveness. A cloned / TTS / impersonated voice can score high, so a voice match is a lead to corroborate, never an identification — every record carries payload.caveat; surface it verbatim. The same speaker scores LOWER across languages, heavy compression, or noise, so a miss isn't proof of a different person. --diarize LABELS overlapping speakers, it does not name them. Scores are 0–100 (percent), not 0–1 — set --min-similarity/--min-margin on that scale. Leads flow through finding triage; they stay out of ask/brief until accepted.