Qwen3-ASR 0.6B
Multilingual speech recognition across 52 languages & dialects — the fast, lightweight Qwen3-ASR
Opens the desktop app and downloads this model for you.
Don't have the app yet? Download OpenASR Desktop
let's line up this week's release plan,
the beta build can ship on Friday,
then loop in design and QA,
Why it fits
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.
What you can do with it
- 🌍 52 languages & dialects — 30 languages plus 22 Chinese dialects, with built-in spoken-language identification
- 🎧 Robust on hard audio — clean speech, singing voice, and songs over background music
- ⚡ Fast & light — the efficiency-tuned member of the Qwen3-ASR family; one model for both offline and streaming
- 🦀 Native in OpenASR —
.oasrpacks run with no Python at inference, engineered for peak performance on CPU & GPU
Other models
Developer details CLI commands, file hashes, and per-quant downloads — for scripting and verification.
Model facts
Usage
These are CLI / local-server examples. The desktop app runs this model without typing a command — use Install above.
$ openasr pull qwen3-asr-0.6b:q8 ↓ qwen3-asr-0.6b.oasr 960.2 MB ✓ verified sha256 $ openasr transcribe meeting.wav --backend native --model-pack ~/.openasr/models/qwen3-asr-0.6b/q8_0/qwen3-asr-0.6b-q8_0.oasr ✓ local transcript · 0 bytes sent
$ openasr serve --backend native --model-pack ~/.openasr/models/qwen3-asr-0.6b/q8_0/qwen3-asr-0.6b-q8_0.oasr --addr 127.0.0.1:8080 ▶ http://127.0.0.1:8080 · model=qwen3-asr-0.6b · 0 bytes will leave this host
from openai import OpenAI client = OpenAI(base_url="http://127.0.0.1:8080/v1", api_key="local") audio = open("meeting.wav", "rb") text = client.audio.transcriptions.create(model="qwen3-asr-0.6b", file=audio)