Cohere Transcribe

Cohere Transcribe is a 2B automatic speech recognition model for 14 languages. The editorial value here is that you do not have to choose between a polished demo and a real open workflow. You can start with the public WebGPU demo to sanity-check short clips in the browser, then move to the model card's documented Transformers or vLLM path when you want offline or production-style runs. A good first test is a short interview clip, meeting excerpt, or voice note where you care about named entities and general readability more than diarization.

Start with the public WebGPU Space on a recent Chromium-based browser and test one short, clean clip in the target language first. That gives you a quick read on transcript quality without setting up a full stack. If the output is promising, move to the model repo and follow the documented Transformers or vLLM examples for local or server-side inference. The API-backed Gradio Space is useful as a product demo, but the WebGPU and offline paths are the better proof that this is not just a closed hosted workflow.

Treat this as transcription, not a full speech workflow. The model does not do speaker diarization, does not provide timestamps, and works best when you already know the language you want to transcribe.

• Turn interviews, calls, or voice notes into editable text without defaulting straight to a hosted API workflow.
• Compare multilingual transcription quality across a few supported languages before committing to a larger ASR stack.
• Prototype browser-side transcription UX for internal tools, note-taking, or lightweight media workflows.
• Pressure-test whether an open ASR model is already good enough for meeting summaries, rough captions, or searchable archives.

Try the demo

View model page