WhisperX Speech Recognition with Word-Level Timestamps and Diarization

WhisperX extends OpenAI Whisper with batched inference for 70x realtime transcription, phoneme-based word-level timestamp alignment via wav2vec2, voice activity detection, and speaker diarization. It produces accurate per-word timestamps and speaker labels from audio files.

Installation

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

• pip install whisperx
• git clone https://github.com/m-bain/whisperX.git
• uv sync --all-extras --dev

Requirements and caveats from upstream:

• 🪶 faster-whisper backend, requires <8GB gpu memory for large-v2 with beam_size=5

• Python usage 🐍

• python

Basic usage or getting-started notes:

• Phoneme-Based ASR A suite of models finetuned to recognise the smallest unit of speech distinguishing one word from another, e.g. the element p in "tap". A popular example model is [wav2vec2.0](https://huggingface...

• You may also need to install ffmpeg, rust etc. Follow openAI instructions here https://github.com/openai/whisper#setup.

• <h2 align="left" id="example">Usage 💬 (command line)</h2>

• Source: https://github.com/m-bain/whisperX

• Extracted from upstream docs: https://raw.githubusercontent.com/m-bain/whisperX/HEAD/README.md

Source

• Agent Skill Exchange