10-of-my-most-popular-text-to-image-series-prompts-78b0897e
generate a bunch of images, then you curate the results to handpick the best ones
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: 🎙️🔊
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install drones277/tg-voice-whisperExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
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:
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=$(cat /tmp/whisper/*.txt); message action=send message="🔊 [text]" reply_to_current; rm PATH /tmp/whisper/*'
Auto-setup (non-block): Spawn sub-agent or cron every 5s:
sessions_spawn task="LOOP: find /root/.openclaw/media/inbound/*.ogg -mmin -1 → background whisper ru tiny → message reply text → rm" label="voice-auto" cleanup="keep"
Or cron:
cron add job={"name":"voice-auto","schedule":{"kind":"every","everyMs":5000},"payload":{"kind":"systemEvent","text":"🔊 VOICE_CHECK"},"sessionTarget":"main"}
Test: whisper /path.ogg --model tiny --language ru
Notes:
Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
---
name: tg-voice-whisper
description: Auto-transcribe Telegram voice messages (.ogg Opus) to text using local OpenAI Whisper (tiny model). Reply with transcription + auto-delete file for privacy. No API keys, fully offline after install.
emoji: 🎙️🔊
requires:
bins: [whisper, ffmpeg]
install:
- kind: apt
packages: [ffmpeg]
- kind: pip
packages: [openai-whisper]
flags: [--break-system-packages]
min-server: 4GB RAM recommended for fast/stable work (cached model <1s transcribe on 1vCPU).
**Safe:** Local-only, auto-rm files, no external APIs/credentials stored.
**Usage (agent):**
When voice .ogg arrives in /root/.openclaw/media/inbound/:
```
exec 'whisper PATH --model tiny --language ru --output_format txt --output_dir /tmp/whisper; text=$(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.generate a bunch of images, then you curate the results to handpick the best ones
generate a bunch of images, then you curate the results to handpick the best ones
Productivity system based on Maya calendar with 13 natural tones for project management and personal development
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.