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#

CommandDescription"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 IndicatorAssessmentLevelCode ExecutionPython/R scripts executed locallyMediumNetwork AccessNo external API callsLowFile System AccessRead input files, write output filesMediumInstruction TamperingStandard prompt guidelinesLowData ExposureOutput files saved to workspaceLow

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