--- name: overcast-wiretap description: >- Work a recorded call or audio clip already in the case — separate the speakers, read the background scene for location clues, isolate and re-transcribe voices, and correlate content across recordings. --- # overcast-wiretap Use this skill to analyze audio recordings you already hold (a call, a voicemail, a field recording): how many people speak, what the background reveals about where it was recorded, and whether two clips share a voice or a phrase. Use the broad `overcast` skill and `overcast/reference/verbs.md` for exact flags. ## Workflow 1. Transcribe the recording and read its background scene on the **default backend**. `--describe` surfaces the whole audio scene (traffic, trains, a PA announcement, church bells) — the "enhance the background noise" move that places a recording — and is a tinycloud/Cloudglue multimodal feature, so do all the transcript/describe work FIRST, before binding any speech-only provider: ```bash overcast doctor --json overcast case init --json overcast listen ./call.wav --json # speech transcript + time-anchored segments overcast listen ./call.wav --describe --json # background audio scene (tinycloud describe) overcast view ./call.wav --spectrogram --json # visual inspection artifact (tones, hums, edits) ``` 2. Isolate voices from noise and re-transcribe the cleaned track — a second pass often recovers words the first missed: ```bash overcast enhance ./call.wav --ops voice-isolate,denoise --json # bundled-ffmpeg filter (fast, lossy) overcast enhance ./call.wav --ops separate --json # true source separation → a track per speaker # (local pyannote: scripts/visual-db-uv.sh --voice + HF_TOKEN, or fal) overcast listen --json ``` 3. Separate the speakers. `--diarize` needs a **diarize-capable listen provider** (the default tinycloud/Cloudglue `listen` is speech-transcript only and rejects `--diarize`). Bind ElevenLabs for this — but do it LAST, after the describe / multimodal steps above, because ElevenLabs Scribe is speech-only and drops the audio-scene describe: ```bash overcast provider setup apply --verb listen --choice elevenlabs --yes --json overcast listen ./call.wav --diarize --json # -> (speaker-labeled) ``` 4. Verify a speaker's identity across recordings with the local voice-print DB (speaker embeddings, not phrase matching — `scripts/visual-db-uv.sh --voice`, no token needed). Scores are rank scores, **not liveness**: a cloned/synthetic voice can score high, so corroborate before naming anyone: ```bash overcast index create voices --type voice-print --local --json overcast voice add ./call.wav --index voices --json # enroll each recording overcast voice match ./voicemail.m4a --index voices --json # which recordings share this voice? overcast voice match ./call.wav ./known-sample.wav --diarize --json # WHICH diarized speaker matches (HF_TOKEN) ``` `voice` answers WHO is speaking (enroll a reference, then rank where/which). To ask whether two clips are the SAME RECORDING (a re-upload/leak of an identical file — a different question), fingerprint them with the local `audio-fp` DB, which matches exact audio through transcode/noise but NOT pitch/speed: ```bash overcast index create clips --type audio-fp --local --json overcast audio add ./call.wav --index clips --json overcast audio match ./leaked.mp3 --index clips --min-margin 2 --json # same recording? time-offset aligned ``` Full drills: `overcast-voiceprint` (WHO is speaking) and `overcast-audio-match` (same recording surfaced again). 5. Record per-speaker and per-clue observations, then correlate across recordings. Cite the speaker-labeled `` for who-said-what claims (not the step-1 transcript record): ```bash overcast note "Speaker 2: PA announces 'platform 4' at 00:38 → rail station" --ref --at 38 --confidence medium --json overcast ask "which recordings share a speaker, phrase, or background cue? cite record.id + time" --verb listen --json ``` 6. Turn confirmed clues into findings and export; always leave a `tldr` note: ```bash overcast finding create "call.wav and voicemail.m4a share Speaker 2's phrasing + station PA — likely same caller/location" --ref --confidence medium --json overcast note "3 recordings; 2 speakers on call.wav; background = rail station; cross-clip voice overlap on 2 of 3" --tag tldr --json # Wait for the note result before exporting, so the TL;DR is included. overcast brief --export ./wiretap.html --json ``` ## Output A per-recording speaker breakdown with timestamps, the background-scene clues that locate or date it, any cross-clip voice/phrase overlaps, and the spectrogram artifacts — each cited by `record.id` + `media.at`. ## Caveats `--diarize` needs a diarize-capable listen provider: the default tinycloud/Cloudglue `listen` transcribes speech only and errors on `--diarize` — bind ElevenLabs (`provider setup apply --verb listen --choice elevenlabs --yes`) for speaker separation. Bind it LAST: ElevenLabs Scribe is speech-only and drops the audio-scene `--describe`, so run all transcript/describe/multimodal work on the default backend first, then rebind for the diarize pass. Diarization LABELS speakers ("Speaker 1/2"), it does not IDENTIFY them — a name is a corroborated inference, never a diarizer output. `voice match` scores speaker similarity, not liveness — a cloned/synthetic voice can score high and the same speaker scores lower across languages or heavy compression, so treat a voice match as a lead to corroborate, never an identification. `voice-isolate` on the bundled ffmpeg is a filter, not source separation — for a real per-speaker split use `enhance --ops separate` (local pyannote via `scripts/visual-db-uv.sh --voice` + `HF_TOKEN` and the accepted pyannote license, or fal), or bind ElevenLabs (`--verb enhance --choice elevenlabs`) for stronger isolation; either way re-listen to confirm the cleaned transcript rather than trusting it blind. Background-cue geolocation is suggestive, not definitive.