Transcribe real-world speech with an API call.

meetlyai.audio is an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to build speaker diarization pipelines.

What inspired me to build this Transcription and diarization (speaker identification)

Main page: ai.meetly.so

Demo page: ai.meetly.so/playground

Current Capabilities

• Generate transcript for speaker speeches using OpenAI Whisper
• List speakers from an audio source

Todos

• Split audio for speaker diarization using pyannote.audio
• Audio chunking because of OpenAI Whisper limitation of 30s audio
• Generate transcript for speaker speeches using OpenAI Whisper
• API endpoint to upload audio and get speaker-wise split audio files
• API to upload speech audio and get transcript as output
• API to upload full speech with multiple speakers and get speaker wise transcript
• API to summarise transcript using OpenAI
• Implement a demo page for anyone to test
• Implement realtime transcription using WebRTC

Tools Used

• pyannote.audio for speaker diarization
• OpenAI Whisper for Speech to Text transcription
• TBD for transcript to summary. Maybe Learning to Summarize from Human Feedback

Installation

Pyannote.audio

Only Python 3.8+ is officially supported (though it might work with Python 3.7)

```bash
conda create -n pyannote python=3.8
conda activate pyannote

# pytorch 1.11 is required for speechbrain compatibility
# (see https://pytorch.org/get-started/previous-versions/#v1110)
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 -c pytorch

pip install pyannote.audio

Whisper (by OpenAI)

Whisper also requires the command-line tool ffmpeg to be installed on your system, which is available from most package managers:

# on Ubuntu or Debian
sudo apt update && sudo apt install ffmpeg

# on Arch Linux
sudo pacman -S ffmpeg

# on MacOS using Homebrew (https://brew.sh/)
brew install ffmpeg

# on Windows using Chocolatey (https://chocolatey.org/)
choco install ffmpeg

# on Windows using Scoop (https://scoop.sh/)
scoop install ffmpeg

For more information on Whisper please see https://github.com/openai/whisper

Recognitions

This repository would not be possible without pyannote.audio, OpenAI Whisper Some other notable repository that I referenced to build this - Whisper Real Time, Whisper AI Show & Tell

The code in this repository is public domain.