# Anibon Local Transcription

> Transcribe YouTube audio locally using whisper.cpp when YouTube has no subtitles or auto-captions. Alternative path loaded by anibon-timestamper.

- Skill: `zenithth/anibon-local-transcription` (Agent Skill, multi-file: 13 files)
- Install (CLI): `npx skillmds@latest add zenithth/anibon-local-transcription`
- Raw SKILL.md: https://api.skillmd.com/api/skills/zenithth/anibon-local-transcription/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: zenithth (https://skillmd.com/u/zenithth)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/zenithth/anibon-local-transcription

---


# Anibon Local Audio Transcription

## Overview & Triggers

Use when YouTube has no subtitles or auto-captions for the target video.

## 1. Audio Extraction

Download audio stream as mono 16kHz WAV file (required for whisper.cpp miniaudio and to prevent >4GB WAV header overflow on long streams):

```bash
yt-dlp -x --audio-format wav --exec "ffmpeg -i {} -ar 16000 -ac 1 -c:a pcm_s16le audio_16k.wav && rm {}" "VIDEO_URL" -o "temp_audio.%(ext)s"
```
Or convert existing audio:
```bash
ffmpeg -i audio.wav -ar 16000 -ac 1 -c:a pcm_s16le audio_16k.wav
```

## 2. Local Transcription

Run GPU-accelerated whisper.cpp build:

Check for `whisper-cli` in system PATH or local `$HOME/whisper.cpp` build directory:
- `$HOME/whisper.cpp/build/bin/whisper-cli`
- `$HOME/whisper.cpp/main`
- `$HOME/whisper.cpp/whisper-cli`

Model path defaults to `$HOME/whisper.cpp/models/ggml-large-v3-turbo.bin`.

**Execution:**
```bash
~/whisper.cpp/build/bin/whisper-cli -m ~/whisper.cpp/models/ggml-large-v3-turbo.bin -l th -f audio.wav --output-json -of whisper_output 2>&1
```
*(`-ot 540000` optional offset to skip silent start screens and avoid repetition loop bugs)*

For full build options and platform configurations, see [BUILD_WHISPERCPP_GUILD.md](../anibon-timestamper/references/BUILD_WHISPERCPP_GUILD.md).

## 3. Format Conversion

Convert whisper-cli raw JSON output to pipeline-standard `raw_transcript.json`:

```bash
python3 ../cleaning-auto-transcripts/scripts/clean_transcript.py whisper_output.json --format whisper --output raw_transcript.json
```

Then proceed with the standard pipeline (chunking, signal detection, subagents).

## 4. Hallucination Detection & Recovery

Detect repetition loops / hallucinations via frequency analysis and auto-trigger recovery:

```bash
python3 scripts/detect_hallucinations.py whisper_output.json --audio audio_16k.wav -o recovered_transcript.json
```