whatsapp-voice

name	whatsapp-voice
description	Transcribe WhatsApp voice messages using local Whisper CLI. Use when: owner or contact sends an audio/ogg voice message. Combines Whisper transcription + CRM update + task creation. Works offline for short clips, uses OpenAI API for long clips. Hebrew and English supported.

WhatsApp Voice — Use-Case Skill

This is a use-case skill, not a standalone integration. It combines:

• openai-whisper CLI — transcription
• personal-crm — update contact's Last Topic if a person is mentioned
• monday / task tracker — save task if needed

---

Transcription Strategy (Auto-Select by Duration)

Select model based on audio length:

Tier	Model	When	Est. Time
1 — Fast	tiny (local)	≤ 15 seconds	5-15s
2 — Balanced	small (local)	15-60 seconds	30-90s
3 — Accurate	OpenAI Whisper API	> 60 seconds	2-5s

Auto-Select Script

#!/bin/bash
FILE="$1"

# Get duration
DURATION=$(ffprobe -v quiet -show_entries format=duration -of csv=p=0 "$FILE" 2>/dev/null | cut -d. -f1)
DURATION=${DURATION:-0}

if [ "$DURATION" -le 15 ]; then
  MODEL="tiny"; TIMEOUT=30
elif [ "$DURATION" -le 60 ]; then
  MODEL="small"; TIMEOUT=120
else
  USE_API=true
fi

if [ "${USE_API:-false}" = true ]; then
  RESULT=$(curl -s https://api.openai.com/v1/audio/transcriptions \
    -H "Authorization: Bearer $OPENAI_API_KEY" \
    -F file="@$FILE" \
    -F model="whisper-1" \
    -F language="he" \
    | jq -r '.text')
else
  OUTDIR="/tmp/whisper-$$"
  mkdir -p "$OUTDIR"
  whisper "$FILE" --model "$MODEL" --language he --output_format txt --output_dir "$OUTDIR" 2>/dev/null
  RESULT=$(cat "$OUTDIR/"*.txt 2>/dev/null)

  # If tiny returned too few words, retry with small
  WORD_COUNT=$(echo "$RESULT" | wc -w)
  if [ "$MODEL" = "tiny" ] && [ "$WORD_COUNT" -le 1 ]; then
    whisper "$FILE" --model small --language he --output_format txt --output_dir "$OUTDIR" 2>/dev/null
    RESULT=$(cat "$OUTDIR/"*.txt 2>/dev/null)
  fi
fi

echo "$RESULT"

---

Installation

# Install Whisper CLI (local)
pip install openai-whisper

# ffprobe for duration detection
apt install ffmpeg   # Linux
brew install ffmpeg  # macOS

Models download automatically to ~/.cache/whisper/ on first run.

---

Full Use-Case Flow: Voice → CRM → monday

When owner sends a voice message:

1. Transcribe (Whisper) — convert OGG to text
2. Identify intent — read the text, understand what's needed
3. If a person is mentioned (personal-crm) — search CRM board, update Last Topic
4. If a task is needed — create item in monday Task Tracker
5. Execute — do what was asked

Voice OGG → [Whisper] → Text → [Intent] → [CRM update] + [monday task] + [execute]

---

Hebrew-Specific Notes

• Always pass --language he — auto-detect often defaults to English
• Names and technical terms may be transcribed phonetically
• small model handles natural Hebrew speech well

---

Known Issues

• FP16 warning on CPU = expected, not an error
• Clips < 2 seconds may hallucinate — ask to re-record
• No GPU = slow. OpenAI API is the fast path for long clips (~$0.006/min)

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File Location (OpenClaw)

WhatsApp inbound media:

/path/to/openclaw/media/inbound/<uuid>.ogg

File path is in system metadata for each inbound media attachment.