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

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