All builds for this model:

Qwen3-ASR-0.6B is the compact, efficiency-optimized member of Alibaba's Qwen3-ASR family, built on the Qwen3-Omni audio-understanding foundation. It performs language identification and speech recognition across 30 languages and 22 Chinese dialects (52 in total), and stays robust on challenging audio — clean speech, singing voice, and songs with background music. A single unified checkpoint handles both offline and real-time streaming transcription and can process long audio; the 0.6B size targets a strong accuracy-vs-efficiency trade-off (the Qwen team reports up to ~2000× throughput at high concurrency), making it the family's go-to for lightweight, high-throughput deployments. This OpenASR repo repackages the original weights as .oasr packs that run natively in the OpenASR runtime — no Python at inference time. The q8_0 build is the recommended default (near-reference accuracy at roughly half the footprint); q4_k suits tight-memory devices and fp16 is for verification or maximum fidelity. For word-level timestamps, pair it upstream with Qwen3-ForcedAligner-0.6B.

Converted from Qwen/Qwen3-ASR-0.6B with the OpenASR importer:

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 Qwen3-ASR-0.6B, created and open-sourced by the Qwen team at Alibaba (Qwen/Qwen3-ASR-0.6B). All credit for the original architecture, training, and weights belongs to them; the license is inherited from and identical to the upstream model (Apache-2.0). The GGUF quantization recipe and bit-identity verification methodology were informed by the community GGUF work at cstr/qwen3-asr-1.7b-GGUF. Thank you to both teams for releasing their work openly.