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Cohere releases open source model that tops speech recognition benchmarks

Matthias Bastian
Matthias Bastian View the LinkedIn Profile of Matthias Bastian
Mar 27, 2026

Canadian AI company Cohere has released "Transcribe," a new open-source model for automatic speech recognition. The company says it claims the top spot on the Hugging Face Open ASR Leaderboard with an average word error rate of just 5.42 percent, beating out competitors like OpenAI's Whisper Large v3, ElevenLabs Scribe v2, and Qwen3-ASR-1.7B. Cohere says Transcribe also delivers the best throughput among similarly sized models.

The chart compares seven speech recognition models with more than one billion parameters. The x-axis shows accuracy as word error rate (WER), where lower values are better. The y-axis shows throughput (RTFx), measuring how fast a model processes audio relative to real time. Cohere Transcribe leads with an RTFx of 525 and a WER of about 5.4, making it both the fastest and most accurate model. NVIDIA Canary Qwen 2.5B follows with an RTFx of 418. Models like OpenAI's Whisper Large v3 and Voxtral Realtime are significantly slower and less accurate.

Cohere Transcribe compared with seven other speech recognition models. Models closer to the upper left corner perform best, meaning faster throughput and lower word error rates. | Image: CohereThe 2 billion parameter model supports 14 languages, including English, German, French, and Japanese. It's available for download under the Apache 2.0 license on Hugging Face and can also be accessed through Cohere's API and the Model Vault platform. Cohere plans to integrate Transcribe into its AI agent platform North in the future.

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