faster-whisper
Whisper, 4× faster, same accuracy. CTranslate2 backend.
OPEN SOURCE2–6 GB VRAM
VRAM fit2–6 GB
DATASHEET // WHISPERX
WhisperX
Whisper + speaker diarisation + word-level timestamps.
OPEN SOURCE4–8 GB VRAMRuns locally
Actually FreeOpen SourceWatermark-FreeHobbyist-OK
Visit WhisperXUPDATED 2026-03-12 · DIRECT LINK
HARDWARE REQUIREMENTS //
Runs locally · Entry GPU (6–8 GB)
Min VRAM
4 GB
Rec. VRAM
8 GB
Min RAM
16 GB
Rec. RAM
16 GB
Disk
12 GB
GPU class
Entry GPU
11.8+No Apple SiliconGPU RequiredQuant: INT8, FP16
Diarisation model adds ~2 GB VRAM on top of Whisper.
[ EDITORIAL PICK ]
Why we recommend WhisperX
- Open source
Source is public — you can audit it, fork it, and you'll never lose access to your workflows if WhisperX the company changes direction.
- Runs on 4 GB
Fits on entry-level cards (GTX 1660, RTX 3050, RTX 4060). Rare for this category.
- 2 quant formats
Supports INT8, FP16 — you can dial VRAM use up or down to match your card.
- Beginner-friendly
You don't need to read a paper before getting your first result — sensible defaults and a quick install.
[ EVIDENCE NOTE ]
Documentation-led datasheet
This page summarizes upstream documentation, release information, and editorially reviewed catalogue fields. It is not presented as a hands-on benchmark. Verify changing requirements at the official project; report stale data through our corrections channel.
AT-A-GLANCE SIGNALS //
DERIVED FROM THIS PAGE'S DATA- Install difficultyStandard
A standard local install — download, install dependencies, point at your GPU.
- Hardware comfortEntry-level
Fits on 4 GB cards — GTX 1660 / RTX 3050 territory.
- EcosystemOpen source
Source is public — auditable and forkable, no vendor lock.
- VerificationAgeing
126 days since the last catalogue refresh — flagged for re-verification.
[ COMMUNITY GUIDES & WORKFLOWS ]
Tutorials & deep-dives for WhisperX
Hand-picked from YouTube, Reddit, GitHub, and the wider web. Each link goes straight to the source — we don't intercept or rewrite anything.
[ MORE IN THIS NICHE ]
Other local llm runners tools we rate
Three picks across different tradeoffs — so you don't end up with three near-clones of WhisperX.
What is WhisperX?
WhisperX takes faster-whisper and adds the things production transcription actually needs: forced-alignment for word-accurate timestamps (via wav2vec2), speaker diarisation (via pyannote), and VAD-based chunking. The de facto open-source pipeline for subtitling and podcast transcripts.
Pros & cons
✓ PROS
- Word-level timestamps that are actually word-level
- Speaker diarisation in the same pipeline
- Built on faster-whisper — speed inherited
– CONS
- Diarisation step needs accepting HF model terms
- Heavier dependency stack (PyAnnote, wav2vec2)
What's actually free?
BSD-4. Note: pyannote diarisation requires HF model access (free).
✓ Actually FreeOpen SourceWatermark-Free
