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sunbv56/
Qwen3-ASR-0.6B-Lyrics-Viet

Name: Qwen3-ASR-0.6B-Lyrics-Viet
Availability: InStock

safetensors

qwen3_asr

automatic-speech-recognition

sunbv56/song_dataset_training_20s_cleaned

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Qwen3-ASR-Lyrics-Viet

Overview

Introduction

These models (sunbv56/Qwen3-ASR-0.6B-Lyrics-Viet and sunbv56/Qwen3-ASR-1.7B-Lyrics-Viet) are fine-tuned versions of the original Qwen3-ASR models. They have been fine-tuned using LoRA on a dataset of 50,000 Vietnamese music samples to specialize in accurately transcribing Vietnamese lyrics. While they retain the powerful capabilities of the original models, they are heavily optimized for Vietnamese singing voices, speech, and songs with BGM.

Note on model size: After fine-tuning, the overall parameter count of the base models has increased. The 0.6B model has grown to approximately 0.8B parameters, and the 1.7B model has grown to approximately 2B parameters.

Fine-Tuning Performance on Vietnamese Lyrics

Model Name	WER (%) Case-Sensitive	WER (%) Lowercase	CER (%) Case-Sensitive	CER (%) Lowercase
Qwen3-ASR-0.6B-Base	41.76	29.45	19.86	16.3
Qwen3-ASR-0.6B-Lyrics-Viet	29.19	24.91	15.98	14.74
Qwen3-ASR-1.7B-Base	34.28	21.27	15.61	12.04
Qwen3-ASR-1.7B-Lyrics-Viet	24.57	19.26	13.19	11.72

Here are the main features of the Qwen3-ASR family:

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

During model loading in the qwen-asr package or vLLM, model weights will be downloaded automatically based on the model name. However, if your runtime environment does not allow downloading weights during execution, you can use the following commands to manually download the model weights to a local directory:

bash

# Download through Hugging Face
pip install -U "huggingface_hub[cli]"
huggingface-cli download sunbv56/Qwen3-ASR-1.7B-Lyrics-Viet --local-dir ./Qwen3-ASR-1.7B
huggingface-cli download sunbv56/Qwen3-ASR-0.6B-Lyrics-Viet --local-dir ./Qwen3-ASR-0.6B
huggingface-cli download Qwen/Qwen3-ForcedAligner-0.6B --local-dir ./Qwen3-ForcedAligner-0.6B

Quickstart

Environment Setup

The easiest way to use Qwen3-ASR is to install the qwen-asr Python package from PyPI. This will pull in the required runtime dependencies and allow you to load any released Qwen3-ASR model. If you’d like to simplify environment setup further, you can also use our official Docker image. The qwen-asr package provides two backends: the transformers backend and the vLLM backend. For usage instructions for different backends, please refer to Python Package Usage. We recommend using a fresh, isolated environment to avoid dependency conflicts with existing packages. You can create a clean Python 3.12 environment like this:

bash

conda create -n qwen3-asr python=3.12 -y
conda activate qwen3-asr

Run the following command to get the minimal installation with transformers-backend support:

pip install -U qwen-asr

To enable the vLLM backend for faster inference and streaming support, run:

pip install -U qwen-asr[vllm]

If you want to develop or modify the code locally, install from source in editable mode:

bash

git clone https://github.com/QwenLM/Qwen3-ASR.git
cd Qwen3-ASR
pip install -e .
# support vLLM backend
# pip install -e ".[vllm]"

Additionally, we recommend using FlashAttention 2 to reduce GPU memory usage and accelerate inference speed, especially for long inputs and large batch sizes.

pip install -U flash-attn --no-build-isolation

If your machine has less than 96GB of RAM and lots of CPU cores, run:

MAX_JOBS=4 pip install -U flash-attn --no-build-isolation

Also, you should have hardware that is compatible with FlashAttention 2. Read more about it in the official documentation of the FlashAttention repository. FlashAttention 2 can only be used when a model is loaded in torch.float16 or torch.bfloat16.

Python Package Usage

Quick Inference

The qwen-asr package provides two backends: transformers backend and vLLM backend. You can pass audio inputs as a local path, a URL, base64 data, or a (np.ndarray, sr) tuple, and run batch inference. To quickly try Qwen3-ASR, you can use Qwen3ASRModel.from_pretrained(...) for the transformers backend with the following code:

python

import torch
from qwen_asr import Qwen3ASRModel

model = Qwen3ASRModel.from_pretrained(
    "sunbv56/Qwen3-ASR-1.7B-Lyrics-Viet",
    dtype=torch.bfloat16,
    device_map="cuda:0",
    # attn_implementation="flash_attention_2",
    max_inference_batch_size=32, # Batch size limit for inference. -1 means unlimited. Smaller values can help avoid OOM.
    max_new_tokens=256, # Maximum number of tokens to generate. Set a larger value for long audio input.
)

results = model.transcribe(
    audio="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav",
    language="Vietnamese", # set "None" to auto-detect the language
)

print(results[0].language)
print(results[0].text)

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: :)

BibTeX

@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}
}