meeting-recap 0.1.0

Transcribe and summarize audio recordings using Groq API (Whisper + LLM)

Meeting Recap

A CLI tool and Python library that transcribes audio recordings and generates structured meeting summaries using Groq API. Powered by Whisper for speech-to-text and Llama for summarization.

Features

• Speech-to-text transcription via Groq's Whisper models (near-instant on Groq hardware)
• AI-powered summarization -- key points, decisions, and action items extracted automatically
• Automatic chunking for large files (>25 MB free tier limit) with overlapping segments so no words are lost
• Multilingual support -- works with any language Whisper supports (Russian, English, Spanish, French, German, and many more)
• Batch processing -- drop multiple audio files into input/ and process them all at once
• Importable library -- use it in your own Python projects with a clean API

Prerequisites

• Python 3.10+
• ffmpeg installed and available on your PATH (download)
• A free Groq API key from console.groq.com

---

Install from PyPI

pip install meeting-recap

Python 3.13+ also requires the audioop-lts shim (automatically installed as a dependency).

---

CLI Usage

From a cloned repo

   git clone https://github.com/talhatek/meeting-recap.git
cd meeting-recap
python -m venv venv && venv\Scripts\activate  # Windows
pip install -e .

Create a .env file with your API key:

GROQ_API_KEY=your_api_key_here

Place one or more audio files into the input/ folder, then run:

# Transcribe & summarize (default: English)
meeting-recap
# or
python main.py

# Specify a different language (ISO-639-1 code)
meeting-recap --language ru

# Use a different Whisper model
meeting-recap --model whisper-large-v3

# Full example with all options
meeting-recap \
  --language en \
  --model whisper-large-v3-turbo \
  --summary-model llama-3.3-70b-versatile \
  --input-dir input \
  --output-dir output

CLI Options

Flag	Default	Description
-l, --language	en	ISO-639-1 language code of the audio
-m, --model	whisper-large-v3-turbo	Whisper model for transcription
--summary-model	llama-3.3-70b-versatile	LLM model for summarization
--input-dir	input	Directory containing audio files
--output-dir	output	Directory for output files

Supported Audio Formats

flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, webm

Output

For each audio file, two files are generated in the output/ directory:

output/
  meeting_transcription.txt   # Full transcription text
  meeting_summary.txt         # Structured summary with key points

---

Library Usage

Install and import in any Python project:

pip install meeting-recap

Full pipeline (transcribe + summarize)

from meeting_recap import process

result = process("meeting.mp3", api_key="gsk_...")
print(result.transcription)
print(result.summary)

Transcription only

from meeting_recap import transcribe

text = transcribe("meeting.mp3", api_key="gsk_...", language="ru")
print(text)

Summarization only

from meeting_recap import summarize

summary = summarize(text, api_key="gsk_...", language="ru")
print(summary)

Full API reference

from meeting_recap import process, transcribe, summarize, RecapResult

# process() -- full pipeline
result: RecapResult = process(
    audio_path="meeting.mp3",
    api_key="gsk_...",
    language="en",                        # ISO-639-1 language code
    model="whisper-large-v3-turbo",       # Whisper model
    summary_model="llama-3.3-70b-versatile",  # LLM model
    verbose=True,                         # print progress
)
result.audio_path       # pathlib.Path to the source file
result.transcription    # full transcription string
result.summary          # structured summary string

# transcribe() -- speech-to-text only
text = transcribe(
    audio_path="meeting.mp3",
    api_key="gsk_...",
    language="en",
    model="whisper-large-v3-turbo",
    verbose=True,
)

# summarize() -- summarize existing text
summary = summarize(
    text="...",
    api_key="gsk_...",
    language="en",
    model="llama-3.3-70b-versatile",
)

---

How It Works

input/*.mp3
    |
    v
[File size check]
    |
    +--> <= 25 MB --> Groq Whisper API --> Transcription
    |
    +--> > 25 MB ---> Split into overlapping chunks (pydub + ffmpeg)
                        |
                        +--> Chunk 1 --> Groq Whisper API --+
                        +--> Chunk 2 --> Groq Whisper API --+--> Merge --> Transcription
                        +--> Chunk N --> Groq Whisper API --+
                                                                    |
                                                                    v
                                                            Groq LLM (summarize)
                                                                    |
                                                                    v
                                                            output/*_summary.txt
                                                            output/*_transcription.txt

Chunking details:

• Files over 25 MB are split into segments that fit within Groq's free tier upload limit
• 5-second overlap between chunks prevents words from being cut off at boundaries
• Chunks are exported as 64 kbps mono MP3 at 16 kHz (optimal for speech recognition)
• Temporary chunk files are automatically cleaned up after processing

---

Supported Languages

Any language supported by OpenAI Whisper. Common examples:

Code	Language	Code	Language
ru	Russian	en	English
es	Spanish	fr	French
de	German	it	Italian
pt	Portuguese	zh	Chinese
ja	Japanese	ko	Korean
uk	Ukrainian	tr	Turkish
ar	Arabic	pl	Polish

Full list: Whisper supported languages

---

Publishing to PyPI

# Build
python -m build

# Upload (requires a PyPI account and API token)
python -m twine upload dist/*

License

MIT