Cohere Transcribe (03-2026)

Our Take

A solid 2B-parameter dense audio model from Cohere. Treat the modality benchmarks above as the leading indicator of fit — composite scoring across modalities is still maturing.

Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.

• ~0.5M hours of curated audio-transcript pairs + synthetic augmentations.
• Non-speech background-noise augmentation (0–30 dB SNR).
• Proprietary mix-balancing and audio decontamination checks for test/train overlap.

English, German, French, Italian, Spanish, Portuguese, Greek, Dutch, Polish, Arabic, Vietnamese, Chinese (Mandarin), Japanese, Korean. No automatic language detection — language must be specified explicitly.

• #1 on the Hugging Face Open ASR Leaderboard as of 2026-03-26 with an average WER of 5.42 across 8 English benchmarks.
• ~525× real-time (RTFx ≈ 524.88), roughly 3× faster than comparably sized ASR models.
• 61% average human preference win rate vs. competing ASR models.
• Ranks 2nd among open-source models on the multilingual ASR leaderboard.

Supported natively in transformers (CohereAsrForConditionalGeneration), vLLM (/v1/audio/transcriptions), Apple Silicon, browser, and mobile; 18 quantized variants on the Hub.