audio-to-text-and-video-to-text: Скилл транскрибации

--- name: audio-to-text-and-video-to-text description: > Transcribe audio and video files into text using OpenAI's Whisper API. Use this skill whenever a user wants to convert any audio or video file to text — including MP3, MP4, WAV, M4A, OGG, WEBM, MOV, AVI, FLAC, and more. Trigger this skill for any request involving: "transcribe", "convert audio to text", "speech to text", "get transcript of", "extract audio from video", "meeting notes from recording", "subtitles", "captions", or similar. Also trigger when the user uploads or references a media file and asks what was said, discussed, or mentioned in it. If unsure whether audio/video transcription is involved, use this skill. --- # Transcription Skill Converts audio and video files into clean, readable text using OpenAI's Whisper API and ffmpeg for media handling. ## Overview This skill handles the full pipeline: 1. **Media extraction** — use ffmpeg to strip audio from video files and convert to a Whisper-compatible format 2. **Chunking** — split large files (>25 MB) into overlapping segments to stay within API limits 3. **Transcription** — send each chunk to OpenAI's Whisper API 4. **Assembly** — merge chunk transcripts, adjusting timestamps, into a single clean output 5. **Post-processing** — optionally clean up with Claude (punctuation, speaker labels, summaries) ## Requirements - **ffmpeg** must be installed (`which ffmpeg` to verify — it's usually pre-installed in claude.ai's environment) - **OpenAI API key** stored in the environment as `OPENAI_API_KEY` — the user must provide this - Python packages: `openai`, `pydub` (install via pip if needed) ## Quick Start When a user provides a media file, run the transcription script: ```bash # Install dependencies if missing pip install openai pydub --break-system-packages -q # Run transcription python /home/claude/transcription/scripts/transcribe.py --input "/path/to/media/file" --output "/mnt/user-data/outputs/transcript.txt" --api-key "$OPENAI_API_KEY" ``` See `scripts/transcribe.py` for the full implementation. ## Supported Formats | Category | Formats | |----------|---------| | Audio | mp3, wav, m4a, ogg, flac, aac, opus, wma | | Video | mp4, mov, avi, mkv, webm, wmv, m4v | ffmpeg handles extraction from any of these. ## Options & Flags | Flag | Default | Description | |------|---------|-------------| | `--model` | `whisper-1` | Whisper model to use (`whisper-1`, `gpt-4o-transcribe`) | | `--language` | auto-detect | ISO 639-1 language code (e.g. `en`, `ar`, `fr`) | | `--format` | `txt` | Output format: `txt`, `srt`, `vtt`, `json` | | `--timestamps` | off | Include timestamps in output | | `--chunk-size` | `20` | Max chunk size in MB (must be ≤ 25) | | `--prompt` | none | Context hint to improve accuracy (e.g. domain vocab) | ## Output Formats - **txt** — plain text, ideal for most uses - **srt** — SubRip subtitle format (for video players) - **vtt** — WebVTT format (for web video) - **json** — full Whisper JSON with segments and timestamps ## Step-by-Step Workflow ### 1. Check for the file Ask the user to upload the file or provide a local path. Check: ```bash ls /mnt/user-data/uploads/ ``` ### 2. Check ffmpeg and install deps ```bash which ffmpeg && ffmpeg -version 2>&1 | head -1 pip install openai pydub --break-system-packages -q 2>&1 | tail -3 ``` ### 3. Get the API key If `OPENAI_API_KEY` is not set in the environment, ask the user: > "Please provide your OpenAI API key — it starts with `sk-`. You can get one at https://platform.openai.com/api-keys" ### 4. Run the script ```bash python /home/claude/transcription/scripts/transcribe.py --input "<file_path>" --output "/mnt/user-data/outputs/transcript.txt" ``` ### 5. Post-process (optional but recommended) After transcription, offer to: - **Clean up** punctuation/formatting with Claude - **Summarize** the content - **Extract** action items, speakers, or key topics - **Translate** to another language Use the transcript text directly in the conversation for these steps. ## Handling Large Files The script automatically splits files > 20 MB into overlapping chunks (with 1-second overlap for continuity). Each chunk is transcribed separately and the results are merged. For very long recordings (> 1 hour), warn the user it may take a few minutes and show progress. ## Error Handling | Error | Fix | |-------|-----| | `AuthenticationError` | Invalid API key — ask user to verify | | `RateLimitError` | Wait 60s and retry, or use `--chunk-size 10` | | `InvalidRequestError: file too large` | Reduce `--chunk-size` below 25 | | `ffmpeg not found` | `sudo apt install ffmpeg` or `brew install ffmpeg` | | `No audio stream found` | File may be corrupt or wrong format | ## Example Interaction ``` User: "Can you transcribe this meeting recording?" [uploads meeting.mp4] → Check file exists in /mnt/user-data/uploads/ → Run transcribe.py on it → Save transcript to /mnt/user-data/outputs/ → present_files() to the user → Offer to summarize or extract action items ``` ## Notes for openclaw.ai - Always save output to `/mnt/user-data/outputs/` so users can download it - Use `present_files()` to share the transcript file with the user after saving - For business users, suggest the `srt` or `vtt` format if they're adding captions to video - The `--prompt` flag is useful for technical/domain-specific content: pass a few domain keywords to improve accuracy