Cohere
Build incredible products with world-class language AI
4.9•13 reviews•374 followers
Build incredible products with world-class language AI
4.9•13 reviews•374 followers
We build high performance, secure language models for the enterprise. Our customizable, high-performance language models work on public, private, or hybrid clouds to ensure data security & exceptional support.
This is the 5th launch from Cohere. View more
Cohere Transcribe
Launching today
Cohere Transcribe is a state-of-the-art, 2B open-weights speech recognition model. Optimized for enterprise workloads, it delivers high throughput and a leading 5.42% WER across 14 languages, making it ideal for private, local, or desktop deployment.
Free Options
Launch Team
Flowtica Scribe
Hi everyone!
Cohere just open-sourced Transcribe, and the core metrics here, especially the throughput and the 5.42% average WER, are genuinely impressive.
From an engineering point of view, this looks like a fantastic model for Mac/PC local apps or private enterprise servers. At 2B parameters, though, it still feels a bit heavy for raw on-device mobile deployment.
It is also worth noting that this is a highly optimized transcription engine, not a fully packaged meeting intelligence stack. Out of the box, you will still want to add your own layer for things like word-level timestamps and speaker diarization.
It also seems to perform best when you specify the language and avoid heavy code-switching.
But if you handle those pre- and post-processing steps and keep the audio mostly in a single language, this open-weight model looks extremely strong for privacy-first, local speech workflows.
@zaczuo Have you tested quantization or distillation tweaks to slim it down for mobile edge cases, like real-time podcast transcription on iOS?
Really glad to see this open-sourced. The WER numbers are competitive with Whisper large-v3 and the throughput advantage matters a lot for batch processing. Curious how it handles noisy real-world audio though — conference calls, street noise, etc. That's usually where models diverge from benchmark performance.
Wow Zac! Was looking for something like this. What about the pricing? Is there a benchmark to compare with current competitors?