# Output & Data

Transcripts reach you three ways: human-readable stdout, machine-readable JSONL, and optional WAV clips.

**Stdout** (human-readable):
```
[14:23:45] (es) Hola, cómo estás?  [rms=0.041 end_of_turn]
[14:23:52] (en) What time is it?  [rms=0.038 end_of_turn]
[14:24:10] (es) Gracias por tu ayuda.  [rms=0.052 pause]
```

**JSONL** (one line per utterance; only written when `jsonl = true`):
```json
{"ts": "2025-09-20T14:23:45.123456", "lang": "es", "lang_prob": 0.95, "duration_s": 2.5, "rms": 0.04123, "end_reason": "end_of_turn", "text": "Hola, cómo estás?", "transcribe_s": 0.42}
{"ts": "2025-09-20T14:23:52.654321", "lang": "en", "lang_prob": 0.98, "duration_s": 1.8, "rms": 0.03811, "end_reason": "pause", "text": "What time is it?", "transcribe_s": 0.31}
```

`duration_s` is the length of the utterance audio; `transcribe_s` is the wall-clock
time the worker spent transcribing it (inference latency), rounded to milliseconds.
Their ratio is the real-time factor. The field is omitted if the timing was not
measured (e.g. `emit()` called directly without `transcribe_s`).

`end_reason` is one of `end_of_turn` (full `silence_ms` elapsed), `pause` (chunked at
`chunk_pause_ms` because it passed `soft_max_s`), `hard_cap` (`max_utterance_s`
reached), or `flush` (shutdown mid-utterance).

**Speaker fields** — every JSONL record carries five additive, back-compatible
speaker fields (always present; `null` when unknown):
```json
{"ts": "2026-09-24T10:01:03.123456", "lang": "en", "lang_prob": 0.98, "duration_s": 3.2, "rms": 0.031, "end_reason": "pause", "text": "turn on the lights", "seg_id": 142, "speaker_id": "spk_0001", "speaker_status": "matched", "speaker": "Efren", "speaker_score": 0.71}
```

- `seg_id` — stable correlation key for the segment (int, `null` when unknown).
- `speaker_id` — stable cluster key for cross-reference (e.g. `"spk_0001"`, `null` when unknown).
- `speaker_status` — one of:
  - `matched` — best score >= `T_id` (threshold_id).
  - `uncertain` — `T_new <= score < T_id` (threshold_new <= score < threshold_id).
  - `unknown` — below `T_new` and no profile minted this utterance.
  - `none` — no embedding computed (model off, audio too short, or error).
- `speaker` — display label: matched+named -> the name (`"Efren"`),
  matched+unnamed -> `"spk_0001"`, uncertain -> `"<top1>?"` (e.g. `"spk_0001?"`),
  unknown/none -> `"?"`.
- `speaker_score` — the winning cosine similarity, rounded to 4 dp (`null` when unknown).

Existing tooling that ignores unknown JSON keys keeps working unchanged.

**Audio files** — when `save_audio = true`, each transcribed utterance is also
written as a 16 kHz mono WAV under `audio_dir/YYYY-MM-DD/`, and the record gains
an `"audio"` field with its path:
```json
{..., "end_reason": "pause", "text": "Hola", "audio": "audio/2025-09-20/142345-123-pause.wav"}
```
Saving is best-effort: if a write fails (e.g. disk full), the transcript is still
emitted and a warning is logged.

> ⚠️ **Disk space** — saved audio is **never rotated or cleaned up** automatically.
> At 16 kHz mono 16-bit it costs 32 KB/s, i.e. **~1.9 MB/min, ~115 MB/hour,
> ~2.8 GB/day** of *continuous speech*. Silence is not written, so real usage is
> lower — but an always-on daemon in a talkative room still accumulates quickly.
> Point `audio_dir` at a volume with headroom and prune it yourself (e.g. a cron
> job removing files older than N days), and leave `save_audio = false` unless you
> actually need the clips for auditing.

**Triage I/O dump (assistant-side)** — with `[triage].dump_io = true` the assistant writes one
self-contained JSON per triage under `triage/YYYY-MM-DD/HHMMSS-mmm.json` (the Laya request/response
plus the LLM request/response). It is separate from the daemon transcript output above, off by
default, and best-effort. See [TRIAGE.md](TRIAGE.md#opt-in-io-dump-dump_io).