Cohere releases 2B open-source speech model with 5.42% word error rate
Cohere has released Transcribe, a 2 billion parameter open-source automatic speech recognition model that the company claims tops the Hugging Face Open ASR Leaderboard with a 5.42% word error rate. The model supports 14 languages and is available under Apache 2.0 license, outperforming OpenAI's Whisper Large v3 and competing models on both accuracy and throughput metrics.
Cohere releases 2B open-source speech model with 5.42% word error rate
Cohere has released Transcribe, a 2 billion parameter open-source automatic speech recognition (ASR) model. According to the company, it achieves a 5.42% average word error rate on the Hugging Face Open ASR Leaderboard, claiming the top position ahead of OpenAI's Whisper Large v3, ElevenLabs Scribe v2, and Qwen3-ASR-1.7B.
Model specifications and performance
Transcribe supports 14 languages including English, German, French, and Japanese. Cohere claims the model also delivers the best throughput among similarly-sized competitors, a critical metric for production deployments where both latency and accuracy matter.
The model is available for download under the Apache 2.0 open-source license from Hugging Face, making it freely usable for commercial and non-commercial applications. It can also be accessed through Cohere's API and the company's Model Vault platform for users preferring cloud-based inference.
Deployment and integration plans
Cohere plans to integrate Transcribe into its North AI agent platform in the future. The release positions Transcribe as a viable alternative to proprietary speech models for developers and organizations seeking open-source ASR capabilities.
The 2B parameter size represents a middle ground: larger than many mobile-optimized models but smaller than massive academic benchmarks, suggesting practical hardware requirements for deployment.
What this means
Cohere's Transcribe release adds competition to the speech recognition market where Whisper has dominated open-source discussions. The claimed 5.42% WER and Apache 2.0 licensing remove licensing friction for commercial applications. However, independent verification of leaderboard results is essential—published benchmarks sometimes reflect narrow test conditions rather than real-world performance across diverse audio conditions, accents, and domains. The model's multilingual support and throughput claims warrant direct comparison testing before large-scale adoption decisions.
Related Articles
OpenAI Releases Whisper Large-v3, Cutting Speech Recognition Errors 10-20% Across Languages
OpenAI has released Whisper large-v3, an open-weight automatic speech recognition and translation model trained on 5 million hours of audio. The model reduces transcription errors by 10-20% compared to its predecessor and adds native Cantonese support.
Meta Open-Sources Muse Spark 1.2, Announces On-Device Model Family Muse Glimmer
Meta CEO Mark Zuckerberg announced the company will open-source its Muse Spark 1.2 model and launch a new on-device model family called Muse Glimmer. The move positions Meta against closed-model rivals OpenAI and Anthropic and against Chinese open-weight labs like DeepSeek and Alibaba.
Meta Releases Muse Glimmer 30B, an On-Device Agentic Model with Built-In Perception Encoder
Meta Superintelligence Lab has released Muse Glimmer, a 29.6-billion-parameter multimodal model distilled from Muse Spark for autonomous agentic tasks that run entirely on consumer hardware. The Apache 2.0-licensed model ships with a dedicated perception encoder, 131K+ token context, and speculative decoding for local speedups up to 3.1x.
Meta Releases Muse Glimmer, a 30B Open-Weight Agent Model That Runs on a Single RTX 3090
Meta released Muse Glimmer, a 30B-parameter open-weight model under Apache 2.0 built for always-on local agents, alongside a promise to release Muse Spark 1.2 weights soon. The model runs on a single RTX 3090 and scores 35 on Artificial Analysis's Intelligence Index.
Comments
Loading...