dvcrn 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
pip install speechrecognition pyaudio pydub python-docx
Configuration
Configure in ~/.openclaw/config/voice-to-protocol-transcriber.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
openclaw skill voice-to-protocol-transcriber --config config.json
Quick Start
# 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
# 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
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
# 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
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- 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