ElevenLabs
ElevenLabs
4.5
The quality benchmark for synthetic speech, voice cloning and multilingual dubbing
Voice & AudioFreemium
Whisper
OpenAI's open-source speech recognition model, the default for self-hosted transcription
4.0
by OpenAIUpdated Overview
Whisper is an open-source automatic speech recognition model that transcribes and translates audio across many languages. Released under a permissive license, it can run locally or through hosted APIs and anchors most open transcription pipelines.
Key capabilities
- Multilingual transcription
- Speech translation
- Timestamped output
- Runs locally
- Permissive license
- Optimized runtimes
Strengths
- Runs entirely on your own hardware, which resolves most audio privacy and residency concerns
- Accuracy on accented and noisy speech holds up better than most open alternatives
- Optimized reimplementations deliver large speedups on modest hardware
- Permissive licensing allows commercial deployment without negotiation
Limitations
- No built-in speaker diarization, so multi-speaker transcripts need a second tool
- Batch-oriented by design; real-time streaming requires additional engineering
- Hallucinated text on silence and background noise is a known and persistent failure mode
- Deploying it well means owning inference infrastructure and evaluation yourself
Whisper’s release reset expectations for open speech recognition. Accuracy that previously required a commercial vendor became available to anyone with a GPU and a permissive license, and the ecosystem of optimized runtimes built on top has since made it fast enough for production.
The reason teams still choose it over hosted alternatives is usually data. Call recordings, medical dictation and internal meetings are exactly the audio that compliance teams do not want leaving the boundary, and Whisper removes that objection entirely.
Know the failure modes before you ship. It has no concept of who is speaking, so diarization is a separate problem. And it will occasionally generate plausible text over silence or noise — a quirk that matters enormously if the transcript feeds an automated decision. Filter low-confidence segments rather than trusting raw output.
Similar options
Choosing an AI stack for your team?
We help companies pick the right AI tools, wire them into existing systems and avoid the ones that quietly do not scale. Tell us what you are trying to build and we will tell you what we would use — no charge for the conversation.
The AIInsider Briefing
The AI signal without the hype — new models, tools worth your time and what actually shipped. One email, no vendor pitches.