Whisper vs WhisperX
Both are alternatives to Deepgram. Here's how they stack up — verified facts, no spin.
Also searched as WhisperX vs Whisper — same comparison, one verdict.
Whisper
The model that changed the category. MIT, and free to run forever.
Whisper is OpenAI's speech-recognition model, released openly under MIT — weights included. It handles around a hundred languages, is robust to accents and background noise, and does translation as well as transcription. Releasing it is what collapsed the economics of this category: a model competitive with the commercial APIs became something anyone could download. The reference implementation is slower than the optimised runtimes, but it is the simplest thing that works and the baseline everything else is measured against.
WhisperX
Accurate word timestamps and speaker labels — Whisper's two weak spots, fixed.
WhisperX wraps Whisper with forced phoneme alignment to produce genuinely accurate word-level timestamps, and adds speaker diarization so output is attributed by speaker. Those are precisely the two things plain Whisper does poorly and the two things a managed API is usually bought for. If your product needs subtitles that land on the word, or meeting transcripts that say who spoke, this closes the gap. BSD-2-Clause, though note the diarization component it uses carries its own model terms worth checking.
Side by side
| Whisper | WhisperX | |
|---|---|---|
| Sovereignty Score | 94 | 90 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | MIT | BSD-2-Clause |
| Pricing | Free — model weights and code both MIT. | Free. Diarization models may require accepting separate terms. |
Whisper edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Whisper
Strengths
- +Genuinely open weights under MIT, not a restricted community licence
- +About a hundred languages, robust to noise and accent
- +Reference implementation — simplest possible starting point
- +Translation to English included
Trade-offs
- −Slower and hungrier than faster-whisper for identical output
- −Weak word-level timestamps without help
- −No speaker diarization
WhisperX
Strengths
- +Word-level timestamps accurate enough for subtitles
- +Speaker diarization included in the pipeline
- +Batched inference makes it fast on long recordings
- +BSD-2-Clause
Trade-offs
- −Diarization models have their own licence terms to review
- −More moving parts than faster-whisper alone
- −Heavier GPU memory requirement with diarization enabled
Related alternative guides
Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.