Qwen3-Omni-Captioner

Overview

Introduction

Since the research community currently lacks a general-purpose audio captioning model, we fine-tuned Qwen3-Omni-30B-A3B to obtain Qwen3-Omni-30B-A3B-Captioner, which produces detailed, low-hallucination captions for arbitrary audio inputs.

Qwen3-Omni-30B-A3B-Captioner is a powerful fine-grained audio analysis model, built upon the Qwen3-Omni-30B-A3B-Instruct base model. It is specifically designed to generate accurate and comprehensive content descriptions in complex and diverse audio scenarios. Without requiring any additional prompting, the model can automatically parse and describe various types of audio content, ranging from complex speech and environmental sounds to music and cinematic sound effects, delivering stable and reliable outputs even in multi-source, mixed audio environments.

In terms of speech understanding, Qwen3-Omni-30B-A3B-Captioner excels at identifying multiple speaker emotions, multilingual expressions, and layered intentions. It can also perceive cultural context and implicit information within the audio, enabling a deep comprehension of the underlying meaning behind the spoken words. In non-speech scenarios, the model demonstrates exceptional sound recognition and analysis capabilities, accurately distinguishing and describing intricate layers of real-world sounds, ambient atmospheres, and dynamic audio details in film and media.

Note: Qwen3-Omni-30B-A3B-Captioner is a single-turn model that accepts only one audio input per inference. It does not accept any text prompts and supports audio input only, with text output only. As Qwen3-Omni-30B-A3B-Captioner is designed for generating fine‑grained descriptions of audio, excessively long audio clips may diminish detail perception. We recommend, as a best practice, limiting audio length to no more than 30 seconds.

QuickStart

Model Description and Download

During loading in Hugging Face Transformers or vLLM, model weights will be automatically downloaded based on the model name. However, if your runtime environment is not conducive to downloading weights during execution, you can refer to the following commands to manually download the model weights to a local directory:

# Download through ModelScope (recommended for users in Mainland China)
pip install -U modelscope
modelscope download --model Qwen/Qwen3-Omni-30B-A3B-Captioner --local_dir ./Qwen3-Omni-30B-A3B-Captioner

# Download through Hugging Face
pip install -U "huggingface_hub[cli]"
huggingface-cli download Qwen/Qwen3-Omni-30B-A3B-Captioner --local-dir ./Qwen3-Omni-30B-A3B-Captioner

Transformers Usage

Installation

The Hugging Face Transformers code for Qwen3-Omni has been successfully merged, but the PyPI package has not yet been released. Therefore, you need to install it from source using the following command. We strongly recommend that you create a new Python environment to avoid environment runtime issues.

# If you already have transformers installed, please uninstall it first, or create a new Python environment
# pip uninstall transformers
pip install git+https://github.com/huggingface/transformers
pip install accelerate

We offer a toolkit to help you handle various types of audio and visual input more conveniently, providing an API-like experience. This includes support for base64, URLs, and interleaved audio, images, and videos. You can install it using the following command and make sure your system has ffmpeg installed:

pip install qwen-omni-utils -U

Additionally, we recommend using FlashAttention 2 when running with Hugging Face Transformers to reduce GPU memory usage. However, if you are primarily using vLLM for inference, this installation is not necessary, as vLLM includes FlashAttention 2 by default.

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.

Code Snippet

Here is a code snippet to show you how to use Qwen3-Omni-30B-A3B-Captioner with transformers and qwen_omni_utils:

import soundfile as sf

from transformers import Qwen3OmniMoeForConditionalGeneration, Qwen3OmniMoeProcessor
from qwen_omni_utils import process_mm_info

MODEL_PATH = "Qwen/Qwen3-Omni-30B-A3B-Captioner"

model = Qwen3OmniMoeForConditionalGeneration.from_pretrained(
    MODEL_PATH,
    dtype="auto",
    device_map="auto",
    attn_implementation="flash_attention_2",
)

processor = Qwen3OmniMoeProcessor.from_pretrained(MODEL_PATH)

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "audio", "audio": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Omni/cookbook/caption2.mp3"},
        ],
    },
]

# Preparation for inference
text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
audios, _, _ = process_mm_info(conversation, use_audio_in_video=False)
inputs = processor(text=text,
                   audio=audios,
                   return_tensors="pt",
                   padd