transmeet 0.0.7

LLM-powered meeting transcription and summarization tool.

🎙️ TransMeet — AI-Powered Meeting Summarizer

Turn your meeting recordings into clear, structured minutes using LLMs like Groq Whisper and Google Speech Recognition.

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🚀 Features

• ✅ Audio Transcription — Automatically convert .wav or .mp3 files into text
• 🧠 LLM-Powered Summarization — Generate concise and structured meeting minutes
• 🔍 Groq & Google Support — Choose between Groq Whisper models or Google Speech API
• 🪓 Automatic Chunking — Splits large files intelligently for smoother transcription
• ⚙️ Fully Customizable — Pick your preferred transcription and summarization models
• 🧾 CLI & Python API — Use it from the terminal or integrate in your Python workflows
• 📁 Clean Output — Saves transcripts and summaries neatly in your desired folder

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📦 Installation

pip install transmeet

Dependencies

sudo apt-get update && sudo apt-get install -y ffmpeg gcc && sudo apt-get clean && sudo rm -rf /var/lib/apt/lists/*

🔐 Setup

Set your GROQ API Key/OPENAI API Key in your environment variables.

export GROQ_API_KEY=your_groq_api_key

To make this permanent:

echo 'export GROQ_API_KEY=your_groq_api_key' >> ~/.bashrc

If using OPENAI, set the OPENAI_API_KEY similarly. For Google Speech, no API key is needed; it uses the default model.

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🧑‍💻 How to Use

✅ Option 1: Import as a Python Module

from transmeet import generate_meeting_transcript_and_minutes

generate_meeting_transcript_and_minutes(
    meeting_audio_file="/path/to/audio.wav",
    output_dir="complete_path_to_output_dir/",
    transcription_client="groq",  # or "openai"
    transcription_model="whisper-large-v3-turbo", # change as per your need
    llm_client="groq",  # or "openai"
    llm_model="llama-3.3-70b-versatile", # change as per your need
)

This will save two files in your output directory:

• transcription_<timestamp>.txt
• meeting_minutes_<timestamp>.md

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🔧 Option 2: Use the CLI

🔹 Basic Usage (Default: GROQ)

transmeet -i /path/to/audio.wav -o output/

🔸 Advanced Usage

transmeet \
  -i /path/to/audio.wav \
  -o output/ \
  --transcription-client groq \
  --transcription-model whisper-large-v3-turbo \
  --llm-client groq \
  --llm-model llama-3.3-70b-versatile \

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🗂️ Output Structure

output/
├── transcriptions/
│   └── transcription_20250510_213038.txt
├── meeting_minutes/
│   └── meeting_minutes_20250510_213041.md

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🧪 Supported Formats

• .wav
• .mp3

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⚙️ CLI Options

Argument	Description
-i, --audio-path	Path to the input audio file
-o, --output-dir	Output directory (default: output/)
--transcription-client	groq or google (default: groq)
--transcription-model	e.g., whisper-large-v3-turbo
--llm-client	groq or openai (default: groq)
--llm-model	e.g., llama-3.3-70b-versatile

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🤖 LLM Models

• Groq Whisper: whisper-large, whisper-large-v3-turbo, etc.
• Google Speech: Model defaults to their API standard
• LLMs for minutes: llama-3, mixtral, gpt-4, etc. (Groq/OpenAI)

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📋 Roadmap

• Add support for multi-language meetings
• Speaker diarization support
• Upload directly to Notion or Google Docs
• Slack/Discord bots

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🧑‍🎓 Author

Deepak Raj 👨‍💻 GitHub • 🌐 LinkedIN

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🤝 Contributing

Pull requests are welcome! Found a bug or need a feature? Open an issue or submit a PR.

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⚖️ License

MIT License