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OpenASR/cohere-transcribe-03-2026
cohere-transcribe-03-2026 is a automatic speech recognition model from OpenASR. Use it when you need speech turned into text. It is set up for openasr. The card lists the license as apache-2.0.
Downloads · 30 days
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From the Hugging Face model README
Dedicated 2B ASR for 14-language transcription
Native speech-to-text in the OpenASR runtime — engineered for peak performance on CPU & GPU, no Python at inference time.
</div>.oasr packs run with no Python at inference, engineered for peak performance on CPU & GPU# 1. Install the OpenASR CLI · https://openasr.org
# 2. Pull a build (pick a quant — see the table below)
openasr pull cohere-transcribe-03-2026:q8
# 3. Transcribe
openasr transcribe audio.wav --model cohere-transcribe-03-2026
All builds for this model:
openasr pull cohere-transcribe-03-2026:fp16
openasr pull cohere-transcribe-03-2026:q8
openasr pull cohere-transcribe-03-2026:q4
| Quant | File (.oasr) | Size | RAM peak | RTF · M1 CPU | RTF · M1 GPU | JFK ΔWER vs fp16 |
|---|---|---|---|---|---|---|
| fp16 | cohere-transcribe-03-2026-fp16.oasr | 4.14 GB | 5.12 GB | 0.26× | 0.11× | 0.0% |
| q8_0 | cohere-transcribe-03-2026-q8_0.oasr | 2.42 GB | 3.42 GB | 0.32× | 0.11× | 0.0% |
| q4_k | cohere-transcribe-03-2026-q4_k.oasr | 1.51 GB | 2.49 GB | 0.25× | 0.10× | 0.0% |
<sub>RTF = real-time factor on the fixed 11s JFK clip (lower is faster); RAM peak measured per pack in an isolated subprocess. JFK ΔWER compares each quantized build's JFK transcript to this model's fp16 JFK transcript, so it measures quantization drift rather than absolute recognition accuracy. q8_0 is the recommended default — near-reference quality at a fraction of the footprint.</sub>
Cohere Transcribe 03-2026 is Cohere and Cohere Labs' open release of a
2B-parameter automatic speech recognition model. It is a dedicated audio-in,
text-out architecture with a Conformer-based acoustic encoder and a lightweight
Transformer decoder, trained from scratch for transcription. The upstream model
card lists support for 14 languages across English, European, APAC, and MENA
coverage and reports Apache-2.0 licensing. This OpenASR repo repackages the
original CohereLabs/cohere-transcribe-03-2026 weights as .oasr packs that
run natively in the OpenASR runtime with no Python at inference time. For most
users the q8_0 build is the recommended default; q4_k is for tighter memory
budgets and fp16 is for verification or maximum fidelity.
Converted from CohereLabs/cohere-transcribe-03-2026 with the OpenASR importer:
openasr model-pack import cohere <src> <out>.oasr \
--package-id cohere-transcribe-03-2026 --quantization {fp16,q8-0,q4-k}
The .oasr container is GGUF-backed; packs use zero-copy mmap weight binding and graph
buffer reuse to keep peak memory low.
These packs inherit the upstream model's license: Apache-2.0 (source). OpenASR packaging retains the upstream copyright and NOTICE; the only modifications are format conversion and quantization.
This pack is a redistribution of Cohere Transcribe 03-2026, released by
Cohere and Cohere Labs
(CohereLabs/cohere-transcribe-03-2026).
All credit for the original model, training recipe, and weights belongs to Cohere and
Cohere Labs. The upstream Hugging Face model card declares Apache-2.0 licensing;
OpenASR only converts the weights into .oasr packages and adds quantized builds
for local runtime use.