Qwen3-ASR-1.7B-GGUF is a quantized version of the Qwen3 automatic speech recognition model. It enables developers to run accurate speech-to-text inference directly on local hardware using GGUF-compatible tools such as llama.cpp or Hugging Face transformers. The repository provides multiple quantization levels for balancing performance and resource usage.
Qwen3 ASR 1.7B sits in PulseGate's Speech to text category. It focuses on running high-quality automatic speech recognition locally without cloud APIs or expensive GPUs. It is built as an open-source project for developers. The project is open source (MIT). Qwen3 ASR 1.7B is available on the web and the command line, and it can be self-hosted.
Behind Qwen3 ASR 1.7B is handy-computer, and it first shipped in 2026. Development happens publicly on GitHub with 1.4k stars and 419 commits in the last 90 days. It operates in a well-populated space: PulseGate tracks 10 similar projects. Among its 3 catalogued features are Speech Recognition, GGUF Quantization, and Local Inference.
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