sunbv56/Qwen3-ASR-Lyrics-Viet-GGUF

Qwen3-ASR-Lyrics-Viet (GGUF Format)

🚨 IMPORTANT NOTE: This repository contains the GGUF-format weights for the Qwen3-ASR-Lyrics-Viet models. These files are highly optimized for CPU and edge-device inference using pure C/C++ without requiring heavy Python dependencies or dedicated GPUs.

For the original PyTorch/Safetensors weights (for fine-tuning or vLLM deployment), please visit the base repositories:

• sunbv56/Qwen3-ASR-0.6B-Lyrics-Viet
• sunbv56/Qwen3-ASR-1.7B-Lyrics-Viet

---

🌟 Overview

These models 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 (supporting 30+ languages), they are heavily optimized for Vietnamese singing voices, speech, and songs with BGM.

Available GGUF Files

We provide both f16 (highest accuracy) and q8_0 (8-bit quantized, fast & lightweight) formats. Choose the one that best fits your hardware:

Model Version	File Name	Size	Recommendation
0.6B (Fastest)	Qwen3-ASR-0.6B-Lyrics-Viet-q8_0.gguf	~630 MB	Best for standard laptops/PCs. Ultra-fast inference.
0.6B (High Acc)	Qwen3-ASR-0.6B-Lyrics-Viet-f16.gguf	~1.8 GB	Best if you want original accuracy with small memory footprint.
1.7B (Smarter)	Qwen3-ASR-1.7B-Lyrics-Viet-q8_0.gguf	~1.8 GB	Best balance between high accuracy and performance.
1.7B (Max Acc)	Qwen3-ASR-1.7B-Lyrics-Viet-f16.gguf	~3.4 GB	Best for powerful machines or GPU offloading.

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

---

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:

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:

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:

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: :)

@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}
}