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MODEL Listed

Qwen3-ASR-0.6B

Qwen3 ASR 0.6B is a compact, high-efficiency automatic speech recognition model designed for low-latency transcription tasks. At 0.6 billion parameters, it is optimized for edge deployment and resource-constrained environments where full-scale models are impractical. Developers can integrate this model into real-time voice pipelines, accessibility tools, or lightweight virtual assistants without sacrificing significant accuracy. Unlike larger ASR frameworks, it offers a lean memory footprint, making it an ideal candidate for on-device processing or high-throughput server-side scaling. Licensed under Apache-2.0, it provides the flexibility needed for both commercial integration and custom fine-tuning on domain-specific audio datasets.

Qwenautomatic speech recognition
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01 / MODEL CARD

Model card

Qwen3 ASR 0.6B is a compact, high-efficiency automatic speech recognition model designed for low-latency transcription tasks. At 0.6 billion parameters, it is optimized for edge deployment and resource-constrained environments where full-scale models are impractical. Developers can integrate this model into real-time voice pipelines, accessibility tools, or lightweight virtual assistants without sacrificing significant accuracy. Unlike larger ASR frameworks, it offers a lean memory footprint, making it an ideal candidate for on-device processing or high-throughput server-side scaling. Licensed under Apache-2.0, it provides the flexibility needed for both commercial integration and custom fine-tuning on domain-specific audio datasets.

Model typeautomatic speech recognition
ProviderQwen
Licenseapache-2.0
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/Qwen/Qwen3-ASR-0.6B
View model source
Version informationUse the source repository for the latest version
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03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: Qwen/Qwen3-ASR-0.6B
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model Qwen/Qwen3-ASR-0.6B
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model Qwen/Qwen3-ASR-0.6B README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('Qwen/Qwen3-ASR-0.6B')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/Qwen/Qwen3-ASR-0.6B.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/Qwen/Qwen3-ASR-0.6B.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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