voice-to-protocol-transcriber

# Voice-to-Protocol Transcriber ## Description Record operation steps and observations via voice commands during experiments. Suitable for laboratory environments, helping researchers transcribe experimental operations in real-time and generate structured experiment records. ## Use Cases - Chemistry experiment operation recording - Biology experiment step tracking - Physics experiment data recording - Clinical experiment operation logging - Any scenario requiring real-time step recording ## Dependencies ```bash pip install speechrecognition pyaudio pydub python-docx ``` ## Configuration Configure in `~/.openclaw/config/voice-to-protocol-transcriber.json`: ```json { "language": "zh-CN", "output_format": "markdown", "auto_save_interval": 60, "save_directory": "~/Documents/Experiment-Protocols", "experiment_name": "default", "enable_timestamp": true, "voice_commands": { "start_recording": "开始记录", "stop_recording": "停止记录", "add_observation": "观察到", "add_step": "步骤", "save_protocol": "保存记录", "add_note": "备注" } } ``` ## Usage ### Basic Usage ```bash openclaw skill voice-to-protocol-transcriber --config config.json ``` ### Quick Start ```bash # Start voice recording openclaw skill voice-to-protocol-transcriber --experiment "Cell Culture Experiment-2024-02-06" # Use specific language openclaw skill voice-to-protocol-transcriber --lang en-US ``` ### Voice Commands | Command | Description | |------|------| | "Start Recording" | Start voice recognition and recording | | "Step [content]" | Add an experiment step | | "Observed [content]" | Add observation results | | "Note [content]" | Add additional notes | | "Save Record" | Save current experiment record | | "Stop Recording" | End recording and save | ## Output Format ### Markdown Format ```markdown # Experiment Record: [Experiment Name] **Date**: 2024-02-06 **Time**: 14:30:25 **Recorder**: [User] --- ## Step 1 **Time**: 14:31:00 **Operation**: [Voice transcription content] ## Observation 1 **Time**: 14:32:15 **Content**: [Observation result] ## Note 1 **Time**: 14:35:00 **Content**: [Note information] --- *Experiment record ended at 14:45:00* ``` ## API ### Python Call ```python from skills.voice_to_protocol_transcriber import ProtocolTranscriber # Initialize transcriber = ProtocolTranscriber( experiment_name="My Experiment", language="zh-CN" ) # Start listening transcriber.start_listening() # Add manual entry transcriber.add_step("Prepare petri dish") transcriber.add_observation("Culture medium became turbid") # Save and stop transcriber.save() transcriber.stop() ``` ## Features - 🎙️ Real-time voice recognition - 📝 Automatic classification (Step/Observation/Note) - ⏱️ Automatic timestamps - 💾 Auto-save - 🌐 Multi-language support - 📄 Multiple output formats (Markdown/Word/Plain Text) - 🔇 Voice command control ## Notes - First use requires microphone permission - Recommended to use in quiet environments - Chinese recognition requires good network connection - Save regularly to avoid data loss ## Changelog ### 1.0.0 - Initial version release - Support Chinese and English voice recognition - Markdown and Word output formats - Voice command control ## Risk Assessment | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low | ## Security Checklist - [ ] No hardcoded credentials or API keys - [ ] No unauthorized file system access (../) - [ ] Output does not expose sensitive information - [ ] Prompt injection protections in place - [ ] Input file paths validated (no ../ traversal) - [ ] Output directory restricted to workspace - [ ] Script execution in sandboxed environment - [ ] Error messages sanitized (no stack traces exposed) - [ ] Dependencies audited ## Prerequisites ```bash # Python dependencies pip install -r requirements.txt ``` ## Evaluation Criteria ### Success Metrics - [ ] Successfully executes main functionality - [ ] Output meets quality standards - [ ] Handles edge cases gracefully - [ ] Performance is acceptable ### Test Cases 1. **Basic Functionality**: Standard input → Expected output 2. **Edge Case**: Invalid input → Graceful error handling 3. **Performance**: Large dataset → Acceptable processing time ## Lifecycle Status - **Current Stage**: Draft - **Next Review Date**: 2026-03-06 - **Known Issues**: None - **Planned Improvements**: - Performance optimization - Additional feature support