unslothai/Qwen3-ASR-1.7B-GGUF

Qwen3-ASR

Overview

Introduction

The Qwen3-ASR family includes Qwen3-ASR-1.7B and Qwen3-ASR-0.6B, which support language identification and ASR for 52 languages and dialects. Both leverage large-scale speech training data and the strong audio understanding capability of their foundation model, Qwen3-Omni. Experiments show that the 1.7B version achieves state-of-the-art performance among open-source ASR models and is competitive with the strongest proprietary commercial APIs. Here are the main features:

• All-in-one: Qwen3-ASR-1.7B and Qwen3-ASR-0.6B support language identification and speech recognition for 30 languages and 22 Chinese dialects, so as to English accents from multiple countries and regions.

• Excellent and Fast: The Qwen3-ASR family ASR models maintains high-quality and robust recognition under complex acoustic environments and challenging text patterns. Qwen3-ASR-1.7B achieves strong performance on both open-sourced and internal benchmarks. While the 0.6B version achieves accuracy-efficient trade-off, it reaches 2000 times throughput at a concurrency of 128. They both achieve streaming / offline unified inference with single model and support transcribe long audio.

• Novel and strong forced alignment Solution: We introduce Qwen3-ForcedAligner-0.6B, which supports timestamp prediction for arbitrary units within up to 5 minutes of speech in 11 languages. Evaluations show its timestamp accuracy surpasses E2E based forced-alignment models.

• Comprehensive inference toolkit: In addition to open-sourcing the architectures and weights of the Qwen3-ASR series, we also release a powerful, full-featured inference framework that supports vLLM-based batch inference, asynchronous serving, streaming inference, timestamp prediction, and more.

Model Architecture

Released Models Description and Download

Below is an introduction and download information for the Qwen3-ASR models. Please select and download the model that fits your needs.

Model	Supported Languages	Supported Dialects	Inference Mode	Audio Types
Qwen3-ASR-1.7B & Qwen3-ASR-0.6B	Chinese (zh), English (en), Cantonese (yue), Arabic (ar), German (de), French (fr), Spanish (es), Portuguese (pt), Indonesian (id), Italian (it), Korean (ko), Russian (ru), Thai (th), Vietnamese (vi), Japanese (ja), Turkish (tr), Hindi (hi), Malay (ms), Dutch (nl), Swedish (sv), Danish (da), Finnish (fi), Polish (pl), Czech (cs), Filipino (fil), Persian (fa), Greek (el), Hungarian (hu), Macedonian (mk), Romanian (ro)	Anhui, Dongbei, Fujian, Gansu, Guizhou, Hebei, Henan, Hubei, Hunan, Jiangxi, Ningxia, Shandong, Shaanxi, Shanxi, Sichuan, Tianjin, Yunnan, Zhejiang, Cantonese (Hong Kong accent), Cantonese (Guangdong accent), Wu language, Minnan language.	Offline / Streaming	Speech, Singing Voice, Songs with BGM
Qwen3-ForcedAligner-0.6B	Chinese, English, Cantonese, French, German, Italian, Japanese, Korean, Portuguese, Russian, Spanish	--	NAR	Speech

Evaluation

During evaluation, we ran inference for all models with dtype=torch.bfloat16 and set max_new_tokens=1024 using vLLM. Greedy search was used for all decoding, and none of the tests specified a language parameter. The detailed evaluation results are shown below.

ASR Benchmarks on Public Datasets (WER ↓)

		GPT-4o -Transcribe	Gemini-2.5 -Pro	Doubao-ASR	Whisper -large-v3	Fun-ASR -MLT-Nano	Qwen3-ASR -0.6B	Qwen3-ASR -1.7B
English (en)
Librispeech clean | other		1.39 | 3.75	2.89 | 3.56	2.78 | 5.70	1.51 | 3.97	1.68 | 4.03	2.11 | 4.55	1.63 | 3.38
GigaSpeech		25.50	9.37	9.55	9.76	-	8.88	8.45
CV-en		9.08	14.49	13.78	9.90	9.90	9.92	7.39
Fleurs-en		2.40	2.94	6.31	4.08	5.49	4.39	3.35
MLS-en		5.12	3.68	7.09	4.87	-	6.00	4.58
Tedlium		7.69	6.15	4.91	6.84	-	3.85	4.50
VoxPopuli		10.29	11.36	12.12	12.05	-	9.96	9.15
Chinese (zh)
WenetSpeech net | meeting		15.30 | 32.27	14.43 | 13.47	N/A	9.86 | 19.11	6.35 | -	5.97 | 6.88	4.97 | 5.88
AISHELL-2-test		4.24	11.62	2.85	5.06	-	3.15	2.71
SpeechIO		12.86	5.30	2.93	7.56	-	3.44	2.88
Fleurs-zh		2.44	2.71	2.69	4.09	3.51	2.88	2.41
CV-zh		6.32	7.70	5.95	12.91	6.20	6.89	5.35
Chinese Dialect
KeSpeech		26.87	24.71	5.27	28.79	-	7.08	5.10
Fleurs-yue		4.98	9.43	4.98	9.18	-	5.79	3.98
CV-yue		11.36	18.76	13.20	16.23	-	9.50	7.57
CV-zh-tw		6.32	7.31	4.06	7.84	-	5.59	3.77
WenetSpeech-Yue short | long		15.62 | 25.29	25.19 | 11.23	9.74 | 11.40	32.26 | 46.64	- | -	7.54 | 9.92	5.82 | 8.85
WenetSpeech-Chuan easy | hard		34.81 | 53.98	43.79 | 67.30	11.40 | 20.20	14.35 | 26.80	- | -	13.92 | 24.45	11.99 | 21.63

ASR Benchmarks on Internal Datasets (WER ↓)

	GPT-4o -Transcribe	Gemini-2.5 -Pro	Doubao-ASR	Whisper -large-v3	Fun-ASR -MLT-Nano	Qwen3-ASR -0.6B	Qwen3-ASR -1.7B
Accented English
Dialog-Accented English	28.56	23.85	20.41	21.30	19.96	16.62	16.07
Chinese Mandarin
Elders&Kids	14.27	36.93	4.17	10.61	4.54	4.48	3.81
ExtremeNoise	36.11	29.06	17.04	63.17	36.55	17.88	16.17
TongueTwister	20.87	4.97	3.47	16.63	9.02	4.06	2.44
Dialog-Mandarin	20.73	12.50	6.61	14.01	7.32	7.06	6.54
Chinese Dialect
Dialog-Cantonese	16.05	14.98	7.56	31.04	5.85	4.80	4.12
Dialog-Chinese Dialects	45.37	47.70	19.85	44.55	19.41	18.24	15.94

Dialect coverage: Results for Dialog-Accented English are averaged over 16 accents, and results for Dialog-Chinese Dialects are averaged over 22 Chinese dialects.

Multilingual ASR Benchmarks (WER ↓)

	GLM-ASR -Nano-2512	Whisper -large-v3	Fun-ASR -MLT-Nano	Qwen3-ASR -0.6B	Qwen3-ASR -1.7B
Open-sourced Benchmarks
MLS	13.32	8.62	28.70	13.19	8.55
CommonVoice	19.40	10.77	17.25	12.75	9.18
MLC-SLM	34.93	15.68	29.94	15.84	12.74
Fleurs	16.08	5.27	10.03	7.57	4.90
Fleurs†	20.05	6.85	31.89	10.37	6.62
Fleurs††	24.83	8.16	47.84	21.80	12.60
Qwen-ASR Internal Benchmarks
News-Multilingual	49.40	14.80	65.07	17.39	12.80

Language coverage: MLS includes 8 languages: {da, de, en, es, fr, it, pl, pt}.
CommonVoice includes 13 languages: {en, zh, yue, zh_TW, ar, de, es, fr, it, ja, ko, pt, ru}.
MLC-SLM includes 11 languages: {en, fr, de, it, pt, es, ja, ko, ru, th, vi}.
Fleurs includes 12 languages: {en, zh, yue, ar, de, es, fr, it, ja, ko, pt, ru }.
Fleurs^† includes 8 additional languages beyond Fleurs: {hi, id, ms, nl, pl, th, tr, vi}.
Fleurs^†† includes 10 additional languages beyond Fleurs^†: {cs, da, el, fa, fi, fil, hu, mk, ro, sv}.
News-Multilingual includes 15 languages: {ar, de, es, fr, hi, id, it, ja, ko, nl, pl, pt, ru, th, vi}.

Language Identification Accuracy (%) ↑

	Whisper-large-v3	Qwen3-ASR-0.6B	Qwen3-ASR-1.7B
MLS	99.9	99.3	99.9
CommonVoice	92.7	98.2	98.7
MLC-SLM	89.2	92.7	94.1
Fleurs	94.6	97.1	98.7
Avg.	94.1	96.8	97.9

Language coverage: The language sets follow Multilingual ASR Benchmarks. Here, Fleurs corresponds to Fleurs^†† in Multilingual ASR Benchmarks and covers 30 languages.

Singing Voice & Song Transcription (WER ↓)

	GPT-4o -Transcribe	Gemini-2.5 -Pro	Doubao-ASR -1.0	Whisper -large-v3	Fun-ASR-MLT -Nano	Qwen3-ASR -1.7B
Singing
M4Singer	16.77	20.88	7.88	13.58	7.29	5.98
MIR-1k-vocal	11.87	9.85	6.56	11.71	8.17	6.25
Opencpop	7.93	6.49	3.80	9.52	2.98	3.08
Popcs	32.84	15.13	8.97	13.77	9.42	8.52
Songs with BGM
EntireSongs-en	30.71	12.18	33.51	N/A	N/A	14.60
EntireSongs-zh	34.86	18.68	23.99	N/A	N/A	13.91

ASR Inference Mode Performance (WER ↓)

Model	Infer. Mode	Librispeech	Fleurs-en	Fleurs-zh	Avg.
Qwen3-ASR-1.7B	Offline	1.63 | 3.38	3.35	2.41	2.69
	Streaming	1.95 | 4.51	4.02	2.84	3.33
Qwen3-ASR-0.6B	Offline	2.11 | 4.55	4.39	2.88	3.48
	Streaming	2.54 | 6.27	5.38	3.40	4.40

Forced Alignment Benchmarks (AAS ms ↓)

	Monotonic-Aligner	NFA	WhisperX	Qwen3-ForcedAligner-0.6B
MFA-Labeled Raw
Chinese	161.1	109.8	-	33.1
English	-	107.5	92.1	37.5
French	-	100.7	145.3	41.7
German	-	122.7	165.1	46.5
Italian	-	142.7	155.5	75.5
Japanese	-	-	-	42.2
Korean	-	-	-	37.2
Portuguese	-	-	-	38.4
Russian	-	200.7	-	40.2
Spanish	-	124.7	108.0	36.8
Avg.	161.1	129.8	133.2	42.9
MFA-Labeled Concat-300s
Chinese	1742.4	235.0	-	36.5
English	-	226.7	227.2	58.6
French	-	230.6	2052.2	53.4
German	-	220.3	993.4	62.4
Italian	-	290.5	5719.4	81.6
Japanese	-	-	-	81.3
Korean	-	-	-	42.2
Portuguese	-	-	-	50.0
Russian	-	283.3	-	43.0
Spanish	-	240.2	4549.9	39.6
Cross-lingual	-	-	-	34.2
Avg.	1742.4	246.7	2708.4	52.9
Human-Labeled
Raw	49.9	88.6	-	27.8
Raw-Noisy	53.3	89.5	-	41.8
Concat-60s	51.1	86.7	-	25.3
Concat-300s	410.8	140.0	-	24.8
Concat-Cross-lingual	-	-	-	42.5
Avg.	141.3	101.2	-	32.4

Citation

If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil: :)

@article{Qwen3-ASR,
  title={Qwen3-ASR Technical Report},
  author={Xian Shi, Xiong Wang, Zhifang Guo, Yongqi Wang, Pei Zhang, Xinyu Zhang, Zishan Guo, Hongkun Hao, Yu Xi, Baosong Yang, Jin Xu, Jingren Zhou, Junyang Lin},
  journal={arXiv preprint arXiv:2601.21337},
  year={2026}
}