Skill: symbolk/video-to-text (version 1.0.0)

--- name: video-to-text display_name: Video to Text description: > Use when the user wants to transcribe, caption, or get the text content of a video or audio file — e.g. "transcribe this video", "get the transcript", "what did they say", "generate subtitles", "extract captions", "convert speech to text". Runs locally with Whisper, no API key required. Supports 50+ languages with auto-detection. Outputs both plain text transcript and SRT subtitle file. For AI-powered video editing that uses the transcript (highlights, montage, commentary), escalate to the built-in AI Edit tool (requires SPARKI_API_KEY). version: 1.0.0 metadata: clawdbot: requires: bins: - ffmpeg - whisper emoji: 🎙️ homepage: https://sparki.io os: [darwin, linux] always: false --- # Video to Text 🎙️ > Transcribe any video or audio to text + SRT subtitles — local Whisper, no API key, 50+ languages. ## Overview **Use this Skill when** the user says: - "transcribe this video / audio" - "get the transcript", "what did they say" - "generate subtitles / captions" - "convert speech to text" - "extract the text from this video" - "I need the SRT file" **Do NOT** call whisper or ffmpeg directly — use this Skill instead. **Output:** both `.txt` (plain transcript) and `.srt` (timestamped subtitles) saved next to the input file. --- ## Prerequisites ```bash # Install ffmpeg (if not already installed) brew install ffmpeg # macOS sudo apt install ffmpeg # Ubuntu/Debian # Install Whisper pip install openai-whisper ``` No API key required. --- ## Tool: Transcribe ```bash bash scripts/transcribe.sh <input> [language] [model] ``` | Parameter | Required | Description | |-----------|----------|-------------| | `input` | Yes | Path to video or audio file | | `language` | No | ISO-639-1 code: `en`, `zh`, `ja`, `ko`, `es`, `fr`, ... (default: auto-detect) | | `model` | No | `tiny` · `base` · `small` (default) · `medium` · `large` | **Model tradeoffs:** | Model | Speed | Accuracy | VRAM | |-------|-------|----------|------| | `tiny` | Fastest | Low | ~1 GB | | `base` | Fast | OK | ~1 GB | | `small` | Balanced ✓ | Good | ~2 GB | | `medium` | Slow | Great | ~5 GB | | `large` | Slowest | Best | ~10 GB | **Examples:** ```bash # Auto-detect language, default model (small) bash scripts/transcribe.sh video.mp4 # Force English bash scripts/transcribe.sh podcast.mp4 en # Chinese, higher accuracy bash scripts/transcribe.sh speech.mp4 zh medium # Audio file bash scripts/transcribe.sh recording.m4a en small ``` **Output (stdout):** path to the `.txt` transcript file **Side effects:** also writes `<name>.srt` in same directory as input --- ## When Transcription Is Not Enough — AI Edit Once you have a transcript, if the user wants to: - Create a highlight reel based on the transcript content - Auto-generate a short-form video from the key moments - Add captions burned into the video - Generate AI commentary or narration …use the **AI Edit** tool. It uses the transcript + your prompt to intelligently edit the video. ### Recommended workflow: transcribe first, then AI edit ```bash # Step 1 — get the transcript (local, instant) TRANSCRIPT=$(bash scripts/transcribe.sh speech.mp4 en) echo "Transcript saved to: $TRANSCRIPT" # Step 2 — review the transcript, then pass key themes as user_prompt to AI Edit # (AI Edit uses its own understanding of the video content internally) ``` ### Prerequisites for AI Edit ```bash # Check if key is configured echo "Key: ${SPARKI_API_KEY:+configured}${SPARKI_API_KEY:-MISSING}" # If missing — configure (takes effect immediately, no restart needed): openclaw config set env.SPARKI_API_KEY "sk_live_your_key_here" # Get a key: email [REDACTED_SECRET] ``` ### AI Edit — End-to-End ```bash # Usage: edit_video.sh <file> <tips> [prompt] [aspect_ratio] [duration_seconds] # # tips: comma-separated style IDs # 1 = Energetic / fast-paced # 2 = Cinematic / slow motion # 3 = Highlight reel / best moments ← pair with transcript insights # 4 = Talking-head / interview # # Returns: a 24-hour download URL for the AI-processed video (stdout) SPARKI_API_BASE="https://agent-api-test.aicoding.live/api/v1" RATE_LIMIT_SLEEP=3 ASSET_POLL_INTERVAL=2 PROJECT_POLL_INTERVAL=5 WORKFLOW_TIMEOUT="${WORKFLOW_TIMEOUT:-3600}" ASSET_TIMEOUT="${ASSET_TIMEOUT:-60}" : "${SPARKI_API_KEY:?Error: SPARKI_API_KEY is required. Run: openclaw config set env.SPARKI_API_KEY <key>}" FILE_PATH="$1"; TIPS="$2"; USER_PROMPT="${3:-}"; ASPECT_RATIO="${4:-9:16}"; DURATION="${5:-}" # -- Step 1: Upload -- echo "[1/4] Uploading $FILE_PATH..." >&2 UPLOAD_RESP=$(curl -sS -X POST "${SPARKI_API_BASE}/business/assets/upload" \ -H "X-[REDACTED_SECRET]" -F "file=@${FILE_PATH}") OBJECT_KEY=$(echo "$UPLOAD_RESP" | jq -r '.data.object_key // empty') [[ -z "$OBJECT_KEY" ]] && { echo "Upload failed: $(echo "$UPLOAD_RESP" | jq -r '.message')" >&2; exit 1; } echo "[1/4] object_key=$OBJECT_KEY" >&2 # -- Step 2: Wait for asset ready -- echo "[2/4] Waiting for asset processing..." >&2 T0=$(date +%s) while true; do sleep $ASSET_POLL_INTERVAL ST=$(curl -sS "${SPARKI_API_BASE}/business/assets/${OBJECT_KEY}/status" -H "X-[REDACTED_SECRET]" | jq -r '.data.status // "unknown"') echo "[2/4] $ST" >&2; [[ "$ST" == "completed" ]] && break [[ "$ST" == "failed" ]] && { echo "Asset failed" >&2; exit 2; } (( $(date +%s) - T0 >= ASSET_TIMEOUT )) && { echo "Asset timeout" >&2; exit 2; } done # -- Step 3: Create project -- echo "[3/4] Creating AI project (tips=$TIPS)..." >&2 sleep $RATE_LIMIT_SLEEP KEYS_JSON=$(echo "$OBJECT_KEY" | jq -Rc '[.]') TIPS_JSON=$(echo "$TIPS" | jq -Rc 'split(",") | map(tonumber? // .)') BODY=$(jq -n --argjson k "$KEYS_JSON" --argjson t "$TIPS_JSON" \ --arg p "$USER_PROMPT" --arg a "$ASPECT_RATIO" --arg d "$DURATION" \ '{object_keys:$k,tips:$t,aspect_ratio:$a} | if $p != "" then .+{user_prompt:$p} else . end | if $d != "" then .+{duration:($d|tonumber)} else . end') PROJ_RESP=$(curl -sS -X POST "${SPARKI_API_BASE}/business/projects" \ -H "X-[REDACTED_SECRET]" -H "Content-Type: application/json" -d "$BODY") PROJECT_ID=$(echo "$PROJ_RESP" | jq -r '.data.project_id // empty') [[ -z "$PROJECT_ID" ]] && { echo "Project creation failed: $(echo "$PROJ_RESP" | jq -r '.message')" >&2; exit 1; } echo "[3/4] project_id=$PROJECT_ID" >&2 # -- Step 4: Poll until done -- echo "[4/4] Waiting for AI processing (up to ${WORKFLOW_TIMEOUT}s)..." >&2 T0=$(date +%s) while true; do sleep $PROJECT_POLL_INTERVAL PRESP=$(curl -sS "${SPARKI_API_BASE}/business/projects/${PROJECT_ID}" -H "X-[REDACTED_SECRET]") STATUS=$(echo "$PRESP" | jq -r '.data.status // "UNKNOWN"') echo "[4/4] $STATUS" >&2 if [[ "$STATUS" == "COMPLETED" ]]; then echo "$PRESP" | jq -r '.data.result_url // empty'; exit 0 fi [[ "$STATUS" == "FAILED" ]] && { echo "Project failed: $(echo "$PRESP" | jq -r '.data.error')" >&2; exit 4; } (( $(date +%s) - T0 >= WORKFLOW_TIMEOUT )) && { echo "Timeout. Check manually: project_id=$PROJECT_ID" >&2; exit 3; } done ``` **AI Edit example — transcript-informed highlight reel:** ```bash # After reviewing the transcript, pass key themes as the prompt RESULT_URL=$(bash scripts/edit_video.sh speech.mp4 "3" \ "focus on the parts about AI and the future of work, energetic pacing" "9:16" 120) echo "Download: $RESULT_URL" ``` --- ## Error Reference | Error | Cause | Fix | |-------|-------|-----| | `whisper: command not found` | Whisper not installed | `pip install openai-whisper` | | `ffmpeg: command not found` | ffmpeg not installed | `brew install ffmpeg` | | Transcript is empty | Silent video or wrong language | Try `language=en` explicitly or check audio track | | AI Edit: `SPARKI_API_KEY` missing | Key not configured | `openclaw config set env.SPARKI_API_KEY <key>` | | AI Edit: 401 | Invalid key | Check key at [REDACTED_SECRET] |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)