transcribelisted

# transcribe Local whisper.cpp transcription. No API calls. ## Inputs - `input`: path to a video or audio file - `out` (optional): output JSON path (defaults to `<input>.transcript.json`) - `language` (optional): ISO code, default `en` ## Output JSON shaped as: ```json { "source": "<input path>", "language": "en", "words": [ {"t0": 0.42, "t1": 0.81, "w": "hello"}, ... ], "segments": [ {"t0": 0.42, "t1": 4.10, "text": "Hello, welcome to the show."}, ... ] } ``` ## How 1. Read `WHISPER_BIN` and `WHISPER_MODEL` from `.env`. 2. If input is video, extract 16kHz mono WAV via `ffmpeg -i <in> -ac 1 -ar 16000 -f wav -`. 3. Pipe to `whisper-cli --model "$WHISPER_MODEL" --output-json-full --no-prints -l <lang>`. 4. Parse whisper-cli's JSON; flatten tokens into `words[]`, group by segment into `segments[]`. ## Run ``` .claude/skills/transcribe/transcribe.sh <input> [out.json] [lang] ``` Idempotent: skips work if `out` is newer than `input`. Uses `--max-len 1 --split-on-word` for word-level segments; groups into sentence segments on `.!?` or every 18 words.