Meeting Quality Scorer

Objectively score meeting quality from a transcript. Three dimensions: decision clarity, time efficiency, participation balance. Outputs a Markdown report and an HTML visualization. Works with any OpenAI-compatible LLM endpoint (Ollama, DeepSeek, OpenAI, etc.). Pairs with meeting_whisper for a transcribe-then-score pipeline.

Score meeting quality from a transcript.

Quick Start

bash

pip install -r requirements.txt

# Score a meeting transcript
python scripts/score_meeting.py --input meeting.txt

# Or with custom output paths
python scripts/score_meeting.py --input meeting.txt --out-md my-report.md --out-html my-report.html

Configuration

Set LLM backend via environment variables:

bash

export MQS_BASE_URL=http://localhost:11434/v1  # Ollama
export MQS_API_KEY=ollama
export MQS_MODEL=qwen2.5:72b

Or create ~/.config/meeting-quality-scorer/config.yaml:

yaml

base_url: https://api.openai.com/v1
api_key: sk-...
model: gpt-4o-mini

Three Dimensions

Dimension	Weight	How scored
决议明确度	40%	LLM detects topics + decisions + owners
时间效率	30%	LLM identifies off-topic / filler segments
参与均衡度	30%	Gini coefficient on speaker word counts

If no speaker labels → participation skipped, weights redistribute to 60/40.

Privacy

Transcript is sent only to your configured LLM endpoint. No telemetry.