Whisper
✓ Editorially verifiedOpenAI's open-source speech-to-text — the de-facto baseline.
Pick Whisper when you can self-host (or the OpenAI API is fine) and want strong baseline transcription at near-zero per-hour cost.
Skip it when you need turnkey diarisation, summarisation, or streaming — AssemblyAI is built for that.
Whisper is OpenAI's open-source speech recognition model. It's free to self-host, multilingual (99 languages), and the baseline against which every other STT model is measured. The large-v3 release is genuinely competitive with paid alternatives on accuracy.
For anyone with engineering capacity, Whisper is the default. Self-hosted, it costs effectively nothing per hour of audio. Available via OpenAI's API for those who don't want to operate a GPU. Hugging Face Transformers makes integration straightforward in Python.
The model has no built-in diarisation — speaker labels need a separate pipeline (pyannote, etc.). Hallucinations on silent segments are a known issue and require post-processing to clean up. For production pipelines these are solvable; out of the box they're surprising.
Whisper is the rare OpenAI release that's open-weight and excellent. It set the standard for what speech-to-text should cost, and it remains the right default for almost any team with engineering capacity.
— The AI Tool Bible editorial team
Pros
- ✅ Free, open weights
- ✅ Multilingual (99 languages)
- ✅ Strong baseline accuracy
- ✅ Available via API or self-host
Cons
- ⚠️ No diarisation built in
- ⚠️ Hallucinations on silent segments
Use cases
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