canary-qwen

About

What canary-qwen is

img { display: inline; } [](#model-architecture) Model Overview Description: NVIDIA NeMo Canary-Qwen-2.5B is an English speech recognition model that achieves state-of-the art performance on multiple English speech benchmarks. With 2.5 billion parameters and running at 418 RTFx, Canary-Qwen-2.5B supports automatic speech-to-text recognition (ASR) in English with punctuation and capitalization (PnC). The model works in two modes: as a transcription tool (ASR mode) and as an LLM (LLM mode). In ASR mode, the model is only capable of transcribing the speech into text, but does not retain any LLM-specific skills such as reasoning. In LLM mode, the model retains all of the original LLM capabilities, which can be used to post-process the transcript, e.g. summarize it or answer questions about it. In LLM mode, the model does not "understand" the raw audio anymore - only its transcript. This model is ready for commercial use. License/Terms of Use: Canary-Qwen-2.5B is released under the CC-BY-4.0 license. By using this model, you are agreeing to the terms and conditions of the license. <br Discover more from NVIDIA: For documentation, deployment guides, enterprise-ready APIs, and the latest open models—including Nemotron and other cutting-edge speech, translation, and generative AI—visit the NVIDIA Developer Portal at developer.nvidia.com. Join the community to access tools, support, and resources to accelerate your development with NVIDIA’s NeMo, Riva, NIM, and foundation models.<br Explore more from NVIDIA: <br What is Nemotron?<br NVIDIA Developer Nemotron<br NVIDIA Riva Speech<br NeMo Documentation<br References: [1] Less is More: Accurate Speech Recognition & Translation without Web-Scale Data [2] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition [3] Attention Is All You Need [4] Qwen/Qwen3-1.7B Model Card [5] Training and Inference Efficiency of Encoder-Decoder Speech Models [6] NVIDIA NeMo Toolkit [7] Granary: Speech Recognition and Translation Dataset in 25 European Languages [8] Towards Measuring Fairness in AI: the Casual Conversations Dataset [9] SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How audio & music work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys canary-qwen for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (canary-qwen below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/audio/transcriptions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -F model="canary-qwen" -F file=@audio.mp3

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

Explore

More audio & music