Whisper Diarization Post-Processor

Whisper Diarization Post-Processor

Enhances OpenAI Whisper transcription output with speaker diarization using pyannote.audio pipeline and speechbrain embeddings. Aligns word-level timestamps from whisper-timestamped with speaker segments for multi-speaker meeting transcript generation.

Installation

Use the upstream install or setup path that matches your environment:

• pip install -U openai-whisper
• pip install git+https://github.com/openai/whisper.git
• pip install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git
• brew install ffmpeg

Requirements and caveats from upstream:

• We used Python 3.9.9 and PyTorch 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.11 and recent PyTorch versions. The codebase also depends o...
• Alternatively, the following command will pull and install the latest commit from this repository, along with its Python dependencies:
• It also requires the command-line tool ffmpeg to be installed on your system, which is available from most package managers:

Basic usage or getting-started notes:

• [Colab example]

• To update the package to the latest version of this repository, please run:

• bash

• Source: https://github.com/openai/whisper

• Extracted from upstream docs: https://raw.githubusercontent.com/openai/whisper/HEAD/README.md

Source

• Agent Skill Exchange