Superwhisper adds Cohere Transcribe for private, on-device dictation

Why it matters

The integration gives Cohere an end-user channel for its open speech model and lets Superwhisper improve private voice input without training its own frontier ASR system.

Neil Chudleigh (@neilsuperduper), the founder of Superwhisper, has added Cohere's open-weight Transcribe speech recognition model to the app for local, offline dictation. Superwhisper announced the integration on July 28th and promoted the partnership in a post on X on July 29th. (superwhisper.com)

The addition advances the product thesis Chudleigh has pursued since building Superwhisper for himself: voice input should work across applications without sending every recording to a remote server. Chudleigh previously co-founded PartnerStack, the B2B partner-management platform that went through Y Combinator in 2015 and was acquired by AppDirect on April 14th, 2026. (appdirect.com)

Chudleigh has said he started Superwhisper after finding Apple's dictation inaccurate and inconsistent across apps. He initially built the product around offline models, accepting the added engineering work required to manage large models and audio processing on consumer hardware. That architecture also gave Chudleigh a clear position for sensitive legal, medical, government and software-development workflows where users may be unwilling or unable to upload speech. (deepgram.com)

A 1.3GB model inside the app

Users can install Cohere Transcribe from Superwhisper's model library as a 1.3GB download, select it as their voice model and dictate without an internet connection. Superwhisper says the audio remains on the user's machine. The integration also applies Superwhisper's vocabulary layer, which is designed to recognize user-defined names, product terms, snippets and technical jargon. (superwhisper.com)

Cohere released Transcribe on March 26th as a 2 billion-parameter Conformer model trained for 14 languages, including English, French, German, Spanish, Mandarin, Japanese, Korean and Arabic. The model weights are available under the Apache 2.0 license, giving developers permission to use and modify the model under the license's terms. (cohere.com)

Cohere's benchmark table placed Transcribe first on the Hugging Face Open ASR Leaderboard at its March launch, with an average English word error rate of 5.42%. The table showed OpenAI's Whisper Large v3 at 7.44% and ElevenLabs Scribe v2 at 5.83%. Those figures describe a standardized collection of English datasets rather than every dictation environment, accent or microphone setup. (cohere.com)

The model also carries practical constraints. Cohere's model card says Transcribe performs best when users select one supported language in advance. It lacks automatic language detection, timestamps and speaker diarization, and Cohere warns of inconsistent results on code-switched audio. The model can also attempt to transcribe background noise unless an application places voice-activity detection or a noise gate in front of it. Superwhisper's vocabulary wrapper addresses specialized words, though it does not remove those underlying model limitations. (huggingface.co)

Cohere gets an end-user distribution channel

For Superwhisper, the partnership adds a competitive open model without requiring Chudleigh to train a frontier speech system from scratch. Superwhisper can instead compete through model selection, local inference, vocabulary controls and the workflow that inserts finished text into email, coding and productivity applications.

For Cohere, Superwhisper puts Transcribe directly in front of people using speech as a daily computer input, four months after the model's release. Cohere introduced Transcribe as the first step toward speech intelligence inside North, its enterprise agent platform. Distribution through a consumer-facing dictation app gives the model a separate route into real workflows while preserving Cohere's emphasis on private deployment and infrastructure control. (cohere.com)

The Toronto connection also gives both companies a sovereignty pitch. Superwhisper describes the integration as a Canadian model running inside a Canadian-built application, with processing kept on the user's hardware. That framing is useful for organizations concerned about where audio is processed, though the immediate product benefit is simpler: users can install a comparatively accurate speech model without configuring Python, downloading weights manually or operating inference infrastructure.

Chudleigh's bet is that voice becomes a general input layer for software rather than a separate transcription destination. Adding Cohere Transcribe strengthens the part of that argument users encounter first. Dictation only saves time when the transcript requires fewer corrections than typing the sentence would have taken.