All builds for this model:

Qwen3-ASR-1.7B is a multilingual automatic speech recognition model (~2B parameters, ~1.7B active, BF16) from Alibaba's Qwen3-ASR family that transcribes speech while identifying the spoken language across 30 languages, 22 Chinese dialects, and a range of regional English accents — and it holds up on hard audio including singing voice and songs over background music. A single unified checkpoint serves both offline and real-time streaming transcription, with word-level timestamps available via the companion Qwen3-ForcedAligner-0.6B; the Qwen team reports state-of-the-art quality among open-source ASR models and accuracy competitive with commercial APIs. This OpenASR repo repackages the original Qwen/Qwen3-ASR-1.7B 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 — near-reference accuracy at roughly half the footprint — while q4_k suits tight-memory deployments and fp16 is reserved for verification or maximum fidelity.

Converted from Qwen/Qwen3-ASR-1.7B 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-1.7B, created and open-sourced by the Qwen team at Alibaba (Qwen/Qwen3-ASR-1.7B). 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). Word-level timestamps additionally require the companion Qwen3-ForcedAligner-0.6B. The GGUF quantization recipe and bit-identity verification methodology were informed by cstr/qwen3-asr-1.7b-GGUF. Thank you to both teams for releasing their work openly.