Overview
- Skill Key
- drones277/tg-voice-whisper
- Author
- drones277
- Source Repo
- openclaw/skills
- Version
- -
- Source Path
- skills/drones277/tg-voice-whisper
- Latest Commit SHA
- 3fa0d2e152e859969fe1221028d6e81226ee3f7d
name: tg voice whisper description: Auto transcribe Telegram voice messages .ogg Opus to text using local OpenAI Whisper tiny model . Reply with transcription + auto delete file for privacy. No API keys, fully offline after install. emoji: 🎙️🔊
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Status
ACTIVE
Visibility
PUBLIC
直接复制以下提示词,发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店,若未安装,请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店,然后安装 Tg Voice Whisper 技能。 若已安装,则直接安装 Tg Voice Whisper 技能。
---
name: tg-voice-whisper
description: Auto-transcribe Telegram voice messages (.ogg Opus) to text using local OpenAI Whisper (tiny model). Reply with transcription + auto-delete file for privacy. No API keys, fully offline after install.
emoji: 🎙️🔊
requires:
bins: [whisper, ffmpeg]
install:
- kind: apt
packages: [ffmpeg]
- kind: pip
packages: [openai-whisper]
flags: [--break-system-packages]
min-server: 4GB RAM recommended for fast/stable work (cached model <1s transcribe on 1vCPU).
**Safe:** Local-only, auto-rm files, no external APIs/credentials stored.
**Usage (agent):**
When voice .ogg arrives in /root/.openclaw/media/inbound/:
```
exec 'whisper PATH --model tiny --language ru --output_format txt --output_dir /tmp/whisper; text=$(cat /tmp/whisper/*.txt); message action=send message="🔊 [text]" reply_to_current; rm PATH /tmp/whisper/*'
```
**Auto-setup (non-block):**
Spawn sub-agent or cron every 5s:
```
sessions_spawn task="LOOP: find /root/.openclaw/media/inbound/*.ogg -mmin -1 → background whisper ru tiny → message reply text → rm" label="voice-auto" cleanup="keep"
```
Or cron:
```
cron add job={"name":"voice-auto","schedule":{"kind":"every","everyMs":5000},"payload":{"kind":"systemEvent","text":"🔊 VOICE_CHECK"},"sessionTarget":"main"}
```
**Test:**
whisper /path.ogg --model tiny --language ru
**Notes:**
- First run: ~15s model download (72MB ~/.cache/whisper/tiny.bin).
- Cached: <1s on 1vCPU/4GB.
- Languages: ru/en best; --language detect auto.
- Accuracy: tiny 85-95% ru speech; upgrade to base/small for better.
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