pastilhasandClaude Fable 5
dd3dbb611b
A probe on the torch stage worker (~/dockers/torch-worker) runs beside every transcription and decides what the recording is: solo dictation returns as paragraphs exactly as before, several voices return speaker-labeled with conversation: true. Nobody declares which — the caller cannot know. Transcription and the worker each get a serial FIFO queue on their own resource, so the probe costs a solo memo nothing and multi-user traffic stays fair. The worker is optional and best-effort throughout: unreachable or failing, everything still transcribes, just never labeled. Speaker attribution is segment-granularity by maximal turn overlap — whisper.cpp's own timestamps, no alignment stage. If boundaries prove sloppy, wav2vec2 alignment returns as a worker endpoint. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
56 lines
3.0 KiB
Markdown
56 lines
3.0 KiB
Markdown
# Transcribe
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Officer's speech-to-text, talking to a self-hosted whisper.cpp server.
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Transcribe does not run Whisper and ships no model. You point it at a whisper.cpp `/inference` server
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you already run and it forwards to it, tuned for one person talking into a phone: detect the spoken
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language, then transcribe faithfully in it — never translated, never guessed at in English when the
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audio is not.
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## What you get
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- **`POST /api/transcribe/transcribe`** — send an audio file, get back its text and the language Whisper
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detected. Two-pass under the hood (detect, then transcribe in that language) so a low-confidence guess
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never quietly defaults to English.
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- **Conversations come back as conversations.** Nobody declares what a recording is — a probe on the
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diarization worker runs alongside every transcription and finds out. Solo dictation returns as
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paragraphs, exactly as before; a recording with several voices returns speaker-labeled
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(`Speaker 1: …`) with `conversation: true` and a speaker count. The worker is optional: without it,
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or if it fails mid-request, everything still transcribes — just never labeled. See
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`~/dockers/torch-worker` for the worker itself.
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- **Fair queueing for a multi-user server.** Transcription and diarization each run one job at a time
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behind their own queue, in arrival order — and on different hardware, so the conversation probe costs
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a solo memo nothing.
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- **A file-browser task.** Right-click an `.mp3`, `.wav` or `.m4a` and transcribe it without a model
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turn — the task is a plain script that calls this plugin's own route and files the result next to the
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original.
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- **A skill**, so an agent asked to transcribe something knows the route, the shape, and what to say when
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nothing is configured, without being told.
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## What you need
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**A whisper.cpp server you already run**, reachable from this machine, answering `POST /inference` —
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the same container the platform used to talk to directly. Nothing else. No system packages, no host
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binaries; the sidecar reaches it with `fetch`.
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## Setup
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```
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PUT /api/transcribe/config
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{ "url": "http://192.168.1.50:64201" }
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```
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Validated against the server before it is stored — a wrong URL is a 400 with the reason, not a saved
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value that fails on the first real transcription.
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## What this plugin deliberately does not have
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- **No dashboard route, no dock tile.** There is nothing to look at — this is reached through the file
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browser's task menu and by an agent that has read the skill, not through a screen. Shipping no `web/`
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is a supported shape, not an oversight.
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- **No streaming.** One file in, one answer back. If you want a live microphone, that is a different
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feature built on top of this one, not something this plugin does today.
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- **No translation.** It transcribes in whatever language was spoken. Whisper's own translate mode is
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English-only, so honouring "translate into <language>" for anything else would mean silently
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handing back a rough English rendering of, say, Portuguese audio — worse than refusing.
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