From 01915eaf4015490394d4360318eb07951adb02c4 Mon Sep 17 00:00:00 2001
From: Zhangchi Feng <64362896+BUAADreamer@users.noreply.github.com>
Date: Wed, 5 Feb 2025 04:59:09 +0800
Subject: [PATCH] [model] support audio (#6701)

* support qwen2_audio

* improve code

* lint

* fix

* fix

* fix

---------

Co-authored-by: hiyouga <hiyouga@buaa.edu.cn>
Former-commit-id: 24c78429489809873a1269a735ea5421340b32a2
---
 README.md                                     |   6 +-
 README_zh.md                                  |   6 +-
 data/README.md                                |  48 ++-
 data/README_zh.md                             |  47 +++
 data/dataset_info.json                        |  14 +
 data/mllm_audio_demo.json                     |  47 +++
 data/mllm_demo_data/1.mp3                     | Bin 0 -> 129024 bytes
 data/mllm_demo_data/2.wav                     | Bin 0 -> 92886 bytes
 data/mllm_demo_data/3.flac                    | Bin 0 -> 120041 bytes
 requirements.txt                              |   1 +
 scripts/stat_utils/cal_ppl.py                 |   1 +
 setup.py                                      |   1 -
 src/llamafactory/chat/base_engine.py          |   4 +-
 src/llamafactory/chat/chat_model.py           |  16 +-
 src/llamafactory/chat/hf_engine.py            |  33 +-
 src/llamafactory/chat/vllm_engine.py          |  20 +-
 src/llamafactory/data/aligner.py              |  65 ++--
 src/llamafactory/data/collator.py             |  57 ++-
 src/llamafactory/data/mm_plugin.py            | 344 ++++++++++++++----
 src/llamafactory/data/parser.py               |   3 +-
 src/llamafactory/data/processors/feedback.py  |  17 +-
 src/llamafactory/data/processors/pairwise.py  |  13 +-
 .../data/processors/supervised.py             |  17 +-
 .../data/processors/unsupervised.py           |   9 +-
 src/llamafactory/data/template.py             |  16 +-
 src/llamafactory/extras/constants.py          |  68 ++--
 src/llamafactory/extras/packages.py           |   4 +
 src/llamafactory/hparams/data_args.py         |   8 +-
 src/llamafactory/model/loader.py              |  11 +-
 src/llamafactory/model/model_utils/visual.py  |   6 +
 src/llamafactory/model/patcher.py             |  15 +-
 src/llamafactory/webui/chatter.py             |   2 +
 src/llamafactory/webui/common.py              |  16 +-
 src/llamafactory/webui/components/chatbot.py  |  10 +-
 src/llamafactory/webui/components/infer.py    |   4 +-
 tests/data/test_mm_plugin.py                  |  18 +-
 tests/data/test_template.py                   |   2 +-
 37 files changed, 736 insertions(+), 213 deletions(-)
 create mode 100644 data/mllm_audio_demo.json
 create mode 100644 data/mllm_demo_data/1.mp3
 create mode 100644 data/mllm_demo_data/2.wav
 create mode 100644 data/mllm_demo_data/3.flac

diff --git a/README.md b/README.md
index efb4a55b..b79c039c 100644
--- a/README.md
+++ b/README.md
@@ -76,8 +76,9 @@ Choose your path:
 - **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, DeepSeek, Yi, Gemma, ChatGLM, Phi, etc.
 - **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
 - **Scalable resources**: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
-- **Advanced algorithms**: [GaLore](https://github.com/jiaweizzhao/GaLore), [BAdam](https://github.com/Ledzy/BAdam), [APOLLO](https://github.com/zhuhanqing/APOLLO), [Adam-mini](https://github.com/zyushun/Adam-mini), DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning.
+- **Advanced algorithms**: [GaLore](https://github.com/jiaweizzhao/GaLore), [BAdam](https://github.com/Ledzy/BAdam), [APOLLO](https://github.com/zhuhanqing/APOLLO), [Adam-mini](https://github.com/zyushun/Adam-mini), DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and PiSSA.
 - **Practical tricks**: [FlashAttention-2](https://github.com/Dao-AILab/flash-attention), [Unsloth](https://github.com/unslothai/unsloth), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), RoPE scaling, NEFTune and rsLoRA.
+- **Wide tasks**: Multi-turn dialogue, tool using, image understanding, visual grounding, video recognition, audio understanding, etc.
 - **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, SwanLab, etc.
 - **Faster inference**: OpenAI-style API, Gradio UI and CLI with vLLM worker.
 
@@ -105,6 +106,8 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 
 ## Changelog
 
+[25/02/05] We supported fine-tuning the **[Qwen2-Audio](Qwen/Qwen2-Audio-7B-Instruct)** and **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** on audio understanding tasks.
+
 [25/01/31] We supported fine-tuning the **[DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1)** and **[Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)** model.
 
 [25/01/15] We supported **[APOLLO](https://arxiv.org/abs/2412.05270)** optimizer. See [examples](examples/README.md) for usage.
@@ -247,6 +250,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 | [Phi-4](https://huggingface.co/microsoft)                         | 14B                              | phi4             |
 | [Pixtral](https://huggingface.co/mistralai)                       | 12B                              | pixtral          |
 | [Qwen/QwQ (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen)   | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen             |
+| [Qwen2-Audio](https://huggingface.co/Qwen)                        | 7B                               | qwen2_audio      |
 | [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen)            | 2B/3B/7B/72B                     | qwen2_vl         |
 | [Skywork o1](https://huggingface.co/Skywork)                      | 8B                               | skywork_o1       |
 | [StarCoder 2](https://huggingface.co/bigcode)                     | 3B/7B/15B                        | -                |
diff --git a/README_zh.md b/README_zh.md
index ac736767..ae6a68dc 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -78,8 +78,9 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
 - **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Qwen2-VL、DeepSeek、Yi、Gemma、ChatGLM、Phi 等等。
 - **集成方法**：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练、KTO 训练、ORPO 训练等等。
 - **多种精度**：16 比特全参数微调、冻结微调、LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ 的 2/3/4/5/6/8 比特 QLoRA 微调。
-- **先进算法**：[GaLore](https://github.com/jiaweizzhao/GaLore)、[BAdam](https://github.com/Ledzy/BAdam)、[APOLLO](https://github.com/zhuhanqing/APOLLO)、[Adam-mini](https://github.com/zyushun/Adam-mini)、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 和 Agent 微调。
+- **先进算法**：[GaLore](https://github.com/jiaweizzhao/GaLore)、[BAdam](https://github.com/Ledzy/BAdam)、[APOLLO](https://github.com/zhuhanqing/APOLLO)、[Adam-mini](https://github.com/zyushun/Adam-mini)、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ 和 PiSSA。
 - **实用技巧**：[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)、[Unsloth](https://github.com/unslothai/unsloth)、[Liger Kernel](https://github.com/linkedin/Liger-Kernel)、RoPE scaling、NEFTune 和 rsLoRA。
+- **广泛任务**：多轮对话、工具调用、图像理解、视觉定位、视频识别和语音理解等等。
 - **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow、SwanLab 等等。
 - **极速推理**：基于 vLLM 的 OpenAI 风格 API、浏览器界面和命令行接口。
 
@@ -115,6 +116,8 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
 
 <details><summary>展开日志</summary>
 
+[25/02/05] 我们支持了在语音理解任务上微调 **[Qwen2-Audio](Qwen/Qwen2-Audio-7B-Instruct)** 和 **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** 模型。
+
 [25/01/14] 我们支持了 **[InternLM3](https://huggingface.co/collections/internlm/)** 模型的微调。感谢 [@hhaAndroid](https://github.com/hhaAndroid) 的 PR。
 
 [25/01/10] 我们支持了 **[Phi-4](https://huggingface.co/microsoft/phi-4)** 模型的微调。
@@ -249,6 +252,7 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
 | [Phi-4](https://huggingface.co/microsoft)                         | 14B                              | phi4             |
 | [Pixtral](https://huggingface.co/mistralai)                       | 12B                              | pixtral          |
 | [Qwen/QwQ (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen)   | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen             |
+| [Qwen2-Audio](https://huggingface.co/Qwen)                        | 7B                               | qwen2_audio      |
 | [Qwen2-VL/Qwen2.5-VL/QVQ](https://huggingface.co/Qwen)            | 2B/3B/7B/72B                     | qwen2_vl         |
 | [Skywork o1](https://huggingface.co/Skywork)                      | 8B                               | skywork_o1       |
 | [StarCoder 2](https://huggingface.co/bigcode)                     | 3B/7B/15B                        | -                |
diff --git a/data/README.md b/data/README.md
index 1786804f..913a15a5 100644
--- a/data/README.md
+++ b/data/README.md
@@ -24,6 +24,7 @@ Currently we support datasets in **alpaca** and **sharegpt** format.
     "tools": "the column name in the dataset containing the tool description. (default: None)",
     "images": "the column name in the dataset containing the image inputs. (default: None)",
     "videos": "the column name in the dataset containing the videos inputs. (default: None)",
+    "audios": "the column name in the dataset containing the audios inputs. (default: None)",
     "chosen": "the column name in the dataset containing the chosen answers. (default: None)",
     "rejected": "the column name in the dataset containing the rejected answers. (default: None)",
     "kto_tag": "the column name in the dataset containing the kto tags. (default: None)"
@@ -150,6 +151,10 @@ An additional column `images` is required. Please refer to the [sharegpt](#share
 
 An additional column `videos` is required. Please refer to the [sharegpt](#sharegpt-format) format for details.
 
+### Multimodal Audio Dataset
+
+An additional column `audios` is required. Please refer to the [sharegpt](#sharegpt-format) format for details.
+
 ## Sharegpt Format
 
 ### Supervised Fine-Tuning Dataset
@@ -296,7 +301,7 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
 
 - [Example dataset](mllm_demo.json)
 
-Multimodal image datasets require a `images` column containing the paths to the input images.
+Multimodal image datasets require an `images` column containing the paths to the input images.
 
 The number of images should be identical to the `<image>` tokens in the conversations.
 
@@ -374,6 +379,47 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
 }
 ```
 
+### Multimodal Audio Dataset
+
+- [Example dataset](mllm_audio_demo.json)
+
+Multimodal audio datasets require an `audios` column containing the paths to the input audios.
+
+The number of audios should be identical to the `<audio>` tokens in the conversations.
+
+```json
+[
+  {
+    "conversations": [
+      {
+        "from": "human",
+        "value": "<audio>human instruction"
+      },
+      {
+        "from": "gpt",
+        "value": "model response"
+      }
+    ],
+    "audios": [
+      "audio path (required)"
+    ]
+  }
+]
+```
+
+Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
+
+```json
+"dataset_name": {
+  "file_name": "data.json",
+  "formatting": "sharegpt",
+  "columns": {
+    "messages": "conversations",
+    "audios": "audios"
+  }
+}
+```
+
 ### OpenAI Format
 
 The openai format is simply a special case of the sharegpt format, where the first message may be a system prompt.
diff --git a/data/README_zh.md b/data/README_zh.md
index 5cfa4b80..32602201 100644
--- a/data/README_zh.md
+++ b/data/README_zh.md
@@ -24,6 +24,7 @@
     "tools": "数据集代表工具描述的表头名称（默认：None）",
     "images": "数据集代表图像输入的表头名称（默认：None）",
     "videos": "数据集代表视频输入的表头名称（默认：None）",
+    "audios": "数据集代表音频输入的表头名称（默认：None）",
     "chosen": "数据集代表更优回答的表头名称（默认：None）",
     "rejected": "数据集代表更差回答的表头名称（默认：None）",
     "kto_tag": "数据集代表 KTO 标签的表头名称（默认：None）"
@@ -150,6 +151,10 @@ KTO 数据集需要提供额外的 `kto_tag` 列。详情请参阅 [sharegpt](#s
 
 多模态视频数据集需要提供额外的 `videos` 列。详情请参阅 [sharegpt](#sharegpt-格式)。
 
+### 多模态音频数据集
+
+多模态音频数据集需要提供额外的 `audios` 列。详情请参阅 [sharegpt](#sharegpt-格式)。
+
 ## Sharegpt 格式
 
 ### 指令监督微调数据集
@@ -374,6 +379,48 @@ KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人
 }
 ```
 
+### 多模态音频数据集
+
+- [样例数据集](mllm_audio_demo.json)
+
+多模态音频数据集需要额外添加一个 `audios` 列，包含输入音频的路径。
+
+注意音频的数量必须与文本中所有 `<audio>` 标记的数量严格一致。
+
+```json
+[
+  {
+    "conversations": [
+      {
+        "from": "human",
+        "value": "<audio>人类指令"
+      },
+      {
+        "from": "gpt",
+        "value": "模型回答"
+      }
+    ],
+    "audios": [
+      "音频路径（必填）"
+    ]
+  }
+]
+```
+
+对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
+
+```json
+"数据集名称": {
+  "file_name": "data.json",
+  "formatting": "sharegpt",
+  "columns": {
+    "messages": "conversations",
+    "audios": "audios"
+  }
+}
+```
+
+
 ### OpenAI 格式
 
 OpenAI 格式仅仅是 sharegpt 格式的一种特殊情况，其中第一条消息可能是系统提示词。
diff --git a/data/dataset_info.json b/data/dataset_info.json
index 7c980b97..291c077d 100644
--- a/data/dataset_info.json
+++ b/data/dataset_info.json
@@ -38,6 +38,20 @@
       "assistant_tag": "assistant"
     }
   },
+  "mllm_audio_demo": {
+    "file_name": "mllm_audio_demo.json",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages",
+      "audios": "audios"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
   "mllm_video_demo": {
     "file_name": "mllm_video_demo.json",
     "formatting": "sharegpt",
diff --git a/data/mllm_audio_demo.json b/data/mllm_audio_demo.json
new file mode 100644
index 00000000..30a50b7d
--- /dev/null
+++ b/data/mllm_audio_demo.json
@@ -0,0 +1,47 @@
+[
+  {
+    "messages": [
+      {
+        "content": "<audio>What's that sound?",
+        "role": "user"
+      },
+      {
+        "content": "It is the sound of glass shattering.",
+        "role": "assistant"
+      }
+    ],
+    "audios": [
+      "mllm_demo_data/1.mp3"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "<audio>What can you hear?",
+        "role": "user"
+      },
+      {
+        "content": "A woman is coughing.",
+        "role": "assistant"
+      }
+    ],
+    "audios": [
+      "mllm_demo_data/2.wav"
+    ]
+  },
+  {
+    "messages": [
+      {
+        "content": "<audio>What does the person say?",
+        "role": "user"
+      },
+      {
+        "content": "Mister Quiller is the apostle of the middle classes and we are glad to welcome his gospel.",
+        "role": "assistant"
+      }
+    ],
+    "audios": [
+      "mllm_demo_data/3.flac"
+    ]
+  }
+]
diff --git a/data/mllm_demo_data/1.mp3 b/data/mllm_demo_data/1.mp3
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literal 0
HcmV?d00001

diff --git a/requirements.txt b/requirements.txt
index d57ed94b..5b9a9684 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,4 +22,5 @@ packaging
 pyyaml
 numpy<2.0.0
 av
+librosa
 tyro<0.9.0
diff --git a/scripts/stat_utils/cal_ppl.py b/scripts/stat_utils/cal_ppl.py
index 768202f9..f4bce61f 100644
--- a/scripts/stat_utils/cal_ppl.py
+++ b/scripts/stat_utils/cal_ppl.py
@@ -49,6 +49,7 @@ class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
                     "labels": feature["chosen_input_ids"] if self.train_on_prompt else feature["chosen_labels"],
                     "images": feature["images"],
                     "videos": feature["videos"],
+                    "audios": feature["audios"],
                 }
             )
 
diff --git a/setup.py b/setup.py
index 0802efbe..f4230444 100644
--- a/setup.py
+++ b/setup.py
@@ -69,7 +69,6 @@ extra_require = {
         "msgpack",
         "referencing",
         "jsonschema_specifications",
-        "librosa",
     ],
     "modelscope": ["modelscope"],
     "openmind": ["openmind"],
diff --git a/src/llamafactory/chat/base_engine.py b/src/llamafactory/chat/base_engine.py
index 60a8e007..9495a566 100644
--- a/src/llamafactory/chat/base_engine.py
+++ b/src/llamafactory/chat/base_engine.py
@@ -22,7 +22,7 @@ if TYPE_CHECKING:
     from vllm import AsyncLLMEngine
 
     from ..data import Template
-    from ..data.mm_plugin import ImageInput, VideoInput
+    from ..data.mm_plugin import AudioInput, ImageInput, VideoInput
     from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
 
 
@@ -68,6 +68,7 @@ class BaseEngine(ABC):
         tools: Optional[str] = None,
         images: Optional[Sequence["ImageInput"]] = None,
         videos: Optional[Sequence["VideoInput"]] = None,
+        audios: Optional[Sequence["AudioInput"]] = None,
         **input_kwargs,
     ) -> List["Response"]:
         r"""
@@ -83,6 +84,7 @@ class BaseEngine(ABC):
         tools: Optional[str] = None,
         images: Optional[Sequence["ImageInput"]] = None,
         videos: Optional[Sequence["VideoInput"]] = None,
+        audios: Optional[Sequence["AudioInput"]] = None,
         **input_kwargs,
     ) -> AsyncGenerator[str, None]:
         r"""
diff --git a/src/llamafactory/chat/chat_model.py b/src/llamafactory/chat/chat_model.py
index 28f5e439..1049c02f 100644
--- a/src/llamafactory/chat/chat_model.py
+++ b/src/llamafactory/chat/chat_model.py
@@ -27,7 +27,7 @@ from .vllm_engine import VllmEngine
 
 
 if TYPE_CHECKING:
-    from ..data.mm_plugin import ImageInput, VideoInput
+    from ..data.mm_plugin import AudioInput, ImageInput, VideoInput
     from .base_engine import BaseEngine, Response
 
 
@@ -66,13 +66,14 @@ class ChatModel:
         tools: Optional[str] = None,
         images: Optional[Sequence["ImageInput"]] = None,
         videos: Optional[Sequence["VideoInput"]] = None,
+        audios: Optional[Sequence["AudioInput"]] = None,
         **input_kwargs,
     ) -> List["Response"]:
         r"""
         Gets a list of responses of the chat model.
         """
         task = asyncio.run_coroutine_threadsafe(
-            self.achat(messages, system, tools, images, videos, **input_kwargs), self._loop
+            self.achat(messages, system, tools, images, videos, audios, **input_kwargs), self._loop
         )
         return task.result()
 
@@ -83,12 +84,13 @@ class ChatModel:
         tools: Optional[str] = None,
         images: Optional[Sequence["ImageInput"]] = None,
         videos: Optional[Sequence["VideoInput"]] = None,
+        audios: Optional[Sequence["AudioInput"]] = None,
         **input_kwargs,
     ) -> List["Response"]:
         r"""
         Asynchronously gets a list of responses of the chat model.
         """
-        return await self.engine.chat(messages, system, tools, images, videos, **input_kwargs)
+        return await self.engine.chat(messages, system, tools, images, videos, audios, **input_kwargs)
 
     def stream_chat(
         self,
@@ -97,12 +99,13 @@ class ChatModel:
         tools: Optional[str] = None,
         images: Optional[Sequence["ImageInput"]] = None,
         videos: Optional[Sequence["VideoInput"]] = None,
+        audios: Optional[Sequence["AudioInput"]] = None,
         **input_kwargs,
     ) -> Generator[str, None, None]:
         r"""
         Gets the response token-by-token of the chat model.
         """
-        generator = self.astream_chat(messages, system, tools, images, videos, **input_kwargs)
+        generator = self.astream_chat(messages, system, tools, images, videos, audios, **input_kwargs)
         while True:
             try:
                 task = asyncio.run_coroutine_threadsafe(generator.__anext__(), self._loop)
@@ -117,12 +120,15 @@ class ChatModel:
         tools: Optional[str] = None,
         images: Optional[Sequence["ImageInput"]] = None,
         videos: Optional[Sequence["VideoInput"]] = None,
+        audios: Optional[Sequence["AudioInput"]] = None,
         **input_kwargs,
     ) -> AsyncGenerator[str, None]:
         r"""
         Asynchronously gets the response token-by-token of the chat model.
         """
-        async for new_token in self.engine.stream_chat(messages, system, tools, images, videos, **input_kwargs):
+        async for new_token in self.engine.stream_chat(
+            messages, system, tools, images, videos, audios, **input_kwargs
+        ):
             yield new_token
 
     def get_scores(
diff --git a/src/llamafactory/chat/hf_engine.py b/src/llamafactory/chat/hf_engine.py
index 88e22aaa..260b1dc5 100644
--- a/src/llamafactory/chat/hf_engine.py
+++ b/src/llamafactory/chat/hf_engine.py
@@ -24,7 +24,7 @@ from typing_extensions import override
 
 from ..data import get_template_and_fix_tokenizer
 from ..extras import logging
-from ..extras.constants import IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
+from ..extras.constants import AUDIO_PLACEHOLDER, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
 from ..extras.misc import get_logits_processor
 from ..model import load_model, load_tokenizer
 from .base_engine import BaseEngine, Response
@@ -35,7 +35,7 @@ if TYPE_CHECKING:
     from trl import PreTrainedModelWrapper
 
     from ..data import Template
-    from ..data.mm_plugin import ImageInput, VideoInput
+    from ..data.mm_plugin import AudioInput, ImageInput, VideoInput
     from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
 
 
@@ -81,9 +81,10 @@ class HuggingfaceEngine(BaseEngine):
         tools: Optional[str] = None,
         images: Optional[Sequence["ImageInput"]] = None,
         videos: Optional[Sequence["VideoInput"]] = None,
+        audios: Optional[Sequence["AudioInput"]] = None,
         input_kwargs: Optional[Dict[str, Any]] = {},
     ) -> Tuple[Dict[str, Any], int]:
-        mm_input_dict = {"images": [], "videos": [], "imglens": [0], "vidlens": [0]}
+        mm_input_dict = {"images": [], "videos": [], "audios": [], "imglens": [0], "vidlens": [0], "audlens": [0]}
         if images is not None:
             mm_input_dict.update({"images": images, "imglens": [len(images)]})
             if not any(IMAGE_PLACEHOLDER in message["content"] for message in messages):
@@ -94,14 +95,25 @@ class HuggingfaceEngine(BaseEngine):
             if not any(VIDEO_PLACEHOLDER in message["content"] for message in messages):
                 messages[0]["content"] = VIDEO_PLACEHOLDER * len(videos) + messages[0]["content"]
 
+        if audios is not None:
+            mm_input_dict.update({"audios": audios, "audlens": [len(audios)]})
+            if not any(AUDIO_PLACEHOLDER in message["content"] for message in messages):
+                messages[0]["content"] = AUDIO_PLACEHOLDER * len(audios) + messages[0]["content"]
+
         messages = template.mm_plugin.process_messages(
-            messages, mm_input_dict["images"], mm_input_dict["videos"], processor
+            messages, mm_input_dict["images"], mm_input_dict["videos"], mm_input_dict["audios"], processor
         )
         paired_messages = messages + [{"role": "assistant", "content": ""}]
         system = system or generating_args["default_system"]
         prompt_ids, _ = template.encode_oneturn(tokenizer, paired_messages, system, tools)
         prompt_ids, _ = template.mm_plugin.process_token_ids(
-            prompt_ids, None, mm_input_dict["images"], mm_input_dict["videos"], tokenizer, processor
+            prompt_ids,
+            None,
+            mm_input_dict["images"],
+            mm_input_dict["videos"],
+            mm_input_dict["audios"],
+            tokenizer,
+            processor,
         )
         prompt_length = len(prompt_ids)
         inputs = torch.tensor([prompt_ids], device=model.device)
@@ -184,6 +196,9 @@ class HuggingfaceEngine(BaseEngine):
         if getattr(model.config, "model_type", None) in ["minicpmv", "minicpmo"]:
             gen_kwargs["input_ids"] = inputs
             gen_kwargs["tokenizer"] = tokenizer
+            if "audio_feature_lens" in mm_inputs:
+                gen_kwargs["audio_feature_lens"] = mm_inputs["audio_feature_lens"]
+
             gen_kwargs.pop("image_sizes", None)
 
         return gen_kwargs, prompt_length
@@ -201,6 +216,7 @@ class HuggingfaceEngine(BaseEngine):
         tools: Optional[str] = None,
         images: Optional[Sequence["ImageInput"]] = None,
         videos: Optional[Sequence["VideoInput"]] = None,
+        audios: Optional[Sequence["AudioInput"]] = None,
         input_kwargs: Optional[Dict[str, Any]] = {},
     ) -> List["Response"]:
         gen_kwargs, prompt_length = HuggingfaceEngine._process_args(
@@ -214,6 +230,7 @@ class HuggingfaceEngine(BaseEngine):
             tools,
             images,
             videos,
+            audios,
             input_kwargs,
         )
         generate_output = model.generate(**gen_kwargs)
@@ -252,6 +269,7 @@ class HuggingfaceEngine(BaseEngine):
         tools: Optional[str] = None,
         images: Optional[Sequence["ImageInput"]] = None,
         videos: Optional[Sequence["VideoInput"]] = None,
+        audios: Optional[Sequence["AudioInput"]] = None,
         input_kwargs: Optional[Dict[str, Any]] = {},
     ) -> Callable[[], str]:
         gen_kwargs, _ = HuggingfaceEngine._process_args(
@@ -265,6 +283,7 @@ class HuggingfaceEngine(BaseEngine):
             tools,
             images,
             videos,
+            audios,
             input_kwargs,
         )
         streamer = TextIteratorStreamer(
@@ -312,6 +331,7 @@ class HuggingfaceEngine(BaseEngine):
         tools: Optional[str] = None,
         images: Optional[Sequence["ImageInput"]] = None,
         videos: Optional[Sequence["VideoInput"]] = None,
+        audios: Optional[Sequence["AudioInput"]] = None,
         **input_kwargs,
     ) -> List["Response"]:
         if not self.can_generate:
@@ -329,6 +349,7 @@ class HuggingfaceEngine(BaseEngine):
             tools,
             images,
             videos,
+            audios,
             input_kwargs,
         )
         async with self.semaphore:
@@ -343,6 +364,7 @@ class HuggingfaceEngine(BaseEngine):
         tools: Optional[str] = None,
         images: Optional[Sequence["ImageInput"]] = None,
         videos: Optional[Sequence["VideoInput"]] = None,
+        audios: Optional[Sequence["AudioInput"]] = None,
         **input_kwargs,
     ) -> AsyncGenerator[str, None]:
         if not self.can_generate:
@@ -360,6 +382,7 @@ class HuggingfaceEngine(BaseEngine):
             tools,
             images,
             videos,
+            audios,
             input_kwargs,
         )
         async with self.semaphore:
diff --git a/src/llamafactory/chat/vllm_engine.py b/src/llamafactory/chat/vllm_engine.py
index 86c36f0a..8627ab96 100644
--- a/src/llamafactory/chat/vllm_engine.py
+++ b/src/llamafactory/chat/vllm_engine.py
@@ -19,7 +19,7 @@ from typing_extensions import override
 
 from ..data import get_template_and_fix_tokenizer
 from ..extras import logging
-from ..extras.constants import IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
+from ..extras.constants import AUDIO_PLACEHOLDER, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
 from ..extras.misc import get_device_count
 from ..extras.packages import is_pillow_available, is_vllm_available
 from ..model import load_config, load_tokenizer
@@ -39,7 +39,7 @@ if is_vllm_available():
 
 
 if TYPE_CHECKING:
-    from ..data.mm_plugin import ImageInput, VideoInput
+    from ..data.mm_plugin import AudioInput, ImageInput, VideoInput
     from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
 
 
@@ -109,10 +109,11 @@ class VllmEngine(BaseEngine):
         tools: Optional[str] = None,
         images: Optional[Sequence["ImageInput"]] = None,
         videos: Optional[Sequence["VideoInput"]] = None,
+        audios: Optional[Sequence["AudioInput"]] = None,
         **input_kwargs,
     ) -> AsyncIterator["RequestOutput"]:
         request_id = f"chatcmpl-{uuid.uuid4().hex}"
-        mm_input_dict = {"images": [], "videos": [], "imglens": [0], "vidlens": [0]}
+        mm_input_dict = {"images": [], "videos": [], "audios": [], "imglens": [0], "vidlens": [0], "audlens": [0]}
         if images is not None:
             mm_input_dict.update({"images": images, "imglens": [len(images)]})
             if not any(IMAGE_PLACEHOLDER in message["content"] for message in messages):
@@ -123,8 +124,13 @@ class VllmEngine(BaseEngine):
             if not any(VIDEO_PLACEHOLDER in message["content"] for message in messages):
                 messages[0]["content"] = VIDEO_PLACEHOLDER * len(videos) + messages[0]["content"]
 
+        if audios is not None:
+            mm_input_dict.update({"audios": audios, "audlens": [len(audios)]})
+            if not any(AUDIO_PLACEHOLDER in message["content"] for message in messages):
+                messages[0]["content"] = AUDIO_PLACEHOLDER * len(audios) + messages[0]["content"]
+
         messages = self.template.mm_plugin.process_messages(
-            messages, mm_input_dict["images"], mm_input_dict["videos"], self.processor
+            messages, mm_input_dict["images"], mm_input_dict["videos"], mm_input_dict["audios"], self.processor
         )
         paired_messages = messages + [{"role": "assistant", "content": ""}]
         system = system or self.generating_args["default_system"]
@@ -202,10 +208,11 @@ class VllmEngine(BaseEngine):
         tools: Optional[str] = None,
         images: Optional[Sequence["ImageInput"]] = None,
         videos: Optional[Sequence["VideoInput"]] = None,
+        audios: Optional[Sequence["AudioInput"]] = None,
         **input_kwargs,
     ) -> List["Response"]:
         final_output = None
-        generator = await self._generate(messages, system, tools, images, videos, **input_kwargs)
+        generator = await self._generate(messages, system, tools, images, videos, audios, **input_kwargs)
         async for request_output in generator:
             final_output = request_output
 
@@ -230,10 +237,11 @@ class VllmEngine(BaseEngine):
         tools: Optional[str] = None,
         images: Optional[Sequence["ImageInput"]] = None,
         videos: Optional[Sequence["VideoInput"]] = None,
+        audios: Optional[Sequence["AudioInput"]] = None,
         **input_kwargs,
     ) -> AsyncGenerator[str, None]:
         generated_text = ""
-        generator = await self._generate(messages, system, tools, images, videos, **input_kwargs)
+        generator = await self._generate(messages, system, tools, images, videos, audios, **input_kwargs)
         async for result in generator:
             delta_text = result.outputs[0].text[len(generated_text) :]
             generated_text = result.outputs[0].text
diff --git a/src/llamafactory/data/aligner.py b/src/llamafactory/data/aligner.py
index aafe1536..f634f21e 100644
--- a/src/llamafactory/data/aligner.py
+++ b/src/llamafactory/data/aligner.py
@@ -25,57 +25,33 @@ if TYPE_CHECKING:
     from transformers import Seq2SeqTrainingArguments
 
     from ..hparams import DataArguments
-    from .mm_plugin import ImageInput, VideoInput
     from .parser import DatasetAttr
 
 
 logger = logging.get_logger(__name__)
 
 
-def _convert_images(
-    images: Union["ImageInput", Sequence["ImageInput"]],
+def _regularize_medias(
+    inputs: Union[Any, Sequence[Any]],
     dataset_attr: "DatasetAttr",
     data_args: "DataArguments",
-) -> Optional[List["ImageInput"]]:
+) -> Optional[List[Any]]:
     r"""
-    Optionally concatenates image path to dataset dir when loading from local disk.
+    Optionally concatenates media path to media dir when loading from local disk.
     """
-    if not isinstance(images, list):
-        images = [images]
-    elif len(images) == 0:
+    if not isinstance(inputs, list):
+        inputs = [inputs]
+    elif len(inputs) == 0:
         return None
     else:
-        images = images[:]
+        inputs = inputs[:]
 
     if dataset_attr.load_from in ["script", "file"]:
-        for i in range(len(images)):
-            if isinstance(images[i], str) and os.path.isfile(os.path.join(data_args.image_dir, images[i])):
-                images[i] = os.path.join(data_args.image_dir, images[i])
+        for i in range(len(inputs)):
+            if isinstance(inputs[i], str) and os.path.isfile(os.path.join(data_args.media_dir, inputs[i])):
+                inputs[i] = os.path.join(data_args.media_dir, inputs[i])
 
-    return images
-
-
-def _convert_videos(
-    videos: Union["VideoInput", Sequence["VideoInput"]],
-    dataset_attr: "DatasetAttr",
-    data_args: "DataArguments",
-) -> Optional[List["VideoInput"]]:
-    r"""
-    Optionally concatenates video path to dataset dir when loading from local disk.
-    """
-    if not isinstance(videos, list):
-        videos = [videos]
-    elif len(videos) == 0:
-        return None
-    else:
-        videos = videos[:]
-
-    if dataset_attr.load_from in ["script", "file"]:
-        for i in range(len(videos)):
-            if isinstance(videos[i], str) and os.path.isfile(os.path.join(data_args.image_dir, videos[i])):
-                videos[i] = os.path.join(data_args.image_dir, videos[i])
-
-    return videos
+    return inputs
 
 
 def convert_alpaca(
@@ -121,15 +97,15 @@ def convert_alpaca(
     else:  # unsupervised
         response = []
 
-    convert_images = partial(_convert_images, dataset_attr=dataset_attr, data_args=data_args)
-    convert_videos = partial(_convert_videos, dataset_attr=dataset_attr, data_args=data_args)
+    regularize_medias = partial(_regularize_medias, dataset_attr=dataset_attr, data_args=data_args)
     output = {
         "_prompt": prompt,
         "_response": response,
         "_system": example[dataset_attr.system] if dataset_attr.system else "",
         "_tools": example[dataset_attr.tools] if dataset_attr.tools else "",
-        "_images": convert_images(example[dataset_attr.images]) if dataset_attr.images else None,
-        "_videos": convert_videos(example[dataset_attr.videos]) if dataset_attr.videos else None,
+        "_images": regularize_medias(example[dataset_attr.images]) if dataset_attr.images else None,
+        "_videos": regularize_medias(example[dataset_attr.videos]) if dataset_attr.videos else None,
+        "_audios": regularize_medias(example[dataset_attr.audios]) if dataset_attr.audios else None,
     }
     return output
 
@@ -214,15 +190,15 @@ def convert_sharegpt(
         logger.warning_rank0("Skipping this abnormal example.")
         prompt, response = [], []
 
-    convert_images = partial(_convert_images, dataset_attr=dataset_attr, data_args=data_args)
-    convert_videos = partial(_convert_videos, dataset_attr=dataset_attr, data_args=data_args)
+    regularize_medias = partial(_regularize_medias, dataset_attr=dataset_attr, data_args=data_args)
     output = {
         "_prompt": prompt,
         "_response": response,
         "_system": system,
         "_tools": example[dataset_attr.tools] if dataset_attr.tools else "",
-        "_images": convert_images(example[dataset_attr.images]) if dataset_attr.images else None,
-        "_videos": convert_videos(example[dataset_attr.videos]) if dataset_attr.videos else None,
+        "_images": regularize_medias(example[dataset_attr.images]) if dataset_attr.images else None,
+        "_videos": regularize_medias(example[dataset_attr.videos]) if dataset_attr.videos else None,
+        "_audios": regularize_medias(example[dataset_attr.audios]) if dataset_attr.audios else None,
     }
     return output
 
@@ -241,6 +217,7 @@ def align_dataset(
         _tools: "...",
         _images: [],
         _videos: [],
+        _audios: [],
     """
     if dataset_attr.formatting == "alpaca":
         convert_func = partial(convert_alpaca, dataset_attr=dataset_attr, data_args=data_args)
diff --git a/src/llamafactory/data/collator.py b/src/llamafactory/data/collator.py
index 32037ac6..1f665904 100644
--- a/src/llamafactory/data/collator.py
+++ b/src/llamafactory/data/collator.py
@@ -18,11 +18,12 @@
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Sequence
 
+import numpy as np
 import torch
 import torch.nn.functional as F
 from transformers import DataCollatorForSeq2Seq
 
-from ..extras.constants import IGNORE_INDEX, IMAGE_PLACEHOLDER
+from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER
 from ..extras.packages import is_pillow_available
 
 
@@ -80,7 +81,7 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
     r"""
     Data collator that supports VLMs.
 
-    Features should contain input_ids, attention_mask, labels, and optionally contain images and videos.
+    Features should contain input_ids, attention_mask, labels, and optionally contain images, videos and audios.
     """
 
     template: Optional["Template"] = None
@@ -91,26 +92,54 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
             raise ValueError("Template is required for MultiModalDataCollator.")
 
     def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, "torch.Tensor"]:
-        batch_images, batch_videos, batch_imglens, batch_vidlens, batch_input_ids = [], [], [], [], []
+        batch_images, batch_videos, batch_audios = [], [], []
+        batch_imglens, batch_vidlens, batch_audlens, batch_input_ids = [], [], [], []
         for feature in features:
             images = feature.pop("images", None) or []
             videos = feature.pop("videos", None) or []
+            audios = feature.pop("audios", None) or []
             batch_images.extend(images)
             batch_videos.extend(videos)
+            batch_audios.extend(audios)
             batch_imglens.append(len(images))
             batch_vidlens.append(len(videos))
+            batch_audlens.append(len(audios))
             batch_input_ids.append(feature["input_ids"])
 
+        fake_input_ids = None
         if (
-            self.processor is not None and sum(batch_imglens) == 0 and sum(batch_vidlens) == 0
+            self.template.mm_plugin.image_token is not None and sum(batch_imglens) == 0 and sum(batch_vidlens) == 0
         ):  # avoid process hanging in zero3/fsdp case
             fake_messages = [{"role": "user", "content": IMAGE_PLACEHOLDER}]
             fake_images = [Image.new("RGB", (64, 64), (255, 255, 255))]
-            fake_messages = self.template.mm_plugin.process_messages(fake_messages, fake_images, [], self.processor)
+            fake_messages = self.template.mm_plugin.process_messages(
+                fake_messages, fake_images, [], [], self.processor
+            )
             fake_input_ids = self.tokenizer.encode(fake_messages[0]["content"], add_special_tokens=False)
             fake_input_ids, _ = self.template.mm_plugin.process_token_ids(
-                fake_input_ids, None, fake_images, [], self.tokenizer, self.processor
+                fake_input_ids, None, fake_images, [], [], self.tokenizer, self.processor
             )
+            batch_images = fake_images
+            batch_imglens[0] = 1
+            batch_input_ids[0] = features[0]["input_ids"]
+
+        if (
+            self.template.mm_plugin.audio_token is not None and sum(batch_audlens) == 0
+        ):  # avoid process hanging in zero3/fsdp case
+            fake_messages = [{"role": "user", "content": AUDIO_PLACEHOLDER}]
+            fake_audios = [np.zeros(1600)]
+            fake_messages = self.template.mm_plugin.process_messages(
+                fake_messages, [], [], fake_audios, self.processor
+            )
+            fake_input_ids = self.tokenizer.encode(fake_messages[0]["content"], add_special_tokens=False)
+            fake_input_ids, _ = self.template.mm_plugin.process_token_ids(
+                fake_input_ids, None, [], [], fake_audios, self.tokenizer, self.processor
+            )
+            batch_audios = fake_audios
+            batch_audlens[0] = 1
+            batch_input_ids[0] = features[0]["input_ids"]
+
+        if fake_input_ids is not None:
             if self.tokenizer.padding_side == "right":
                 features[0]["input_ids"] = features[0]["input_ids"] + fake_input_ids
                 features[0]["attention_mask"] = features[0]["attention_mask"] + [0] * len(fake_input_ids)
@@ -120,12 +149,15 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
                 features[0]["attention_mask"] = [0] * len(fake_input_ids) + features[0]["attention_mask"]
                 features[0]["labels"] = [IGNORE_INDEX] * len(fake_input_ids) + features[0]["labels"]
 
-            batch_images = fake_images
-            batch_imglens[0] = 1
-            batch_input_ids[0] = features[0]["input_ids"]
-
         mm_inputs = self.template.mm_plugin.get_mm_inputs(
-            batch_images, batch_videos, batch_imglens, batch_vidlens, batch_input_ids, self.processor
+            batch_images,
+            batch_videos,
+            batch_audios,
+            batch_imglens,
+            batch_vidlens,
+            batch_audlens,
+            batch_input_ids,
+            self.processor,
         )
         if "token_type_ids" in mm_inputs:
             token_type_ids = mm_inputs.pop("token_type_ids")
@@ -208,6 +240,7 @@ class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
                     "labels": feature[f"{key}_labels"],
                     "images": feature["images"],
                     "videos": feature["videos"],
+                    "audios": feature["audios"],
                 }
                 concatenated_features.append(target_feature)
 
@@ -231,6 +264,7 @@ class KTODataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
                 "labels": feature["labels"],
                 "images": feature["images"],
                 "videos": feature["videos"],
+                "audios": feature["audios"],
             }
             kl_feature = {
                 "input_ids": feature["kl_input_ids"],
@@ -238,6 +272,7 @@ class KTODataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
                 "labels": feature["kl_labels"],
                 "images": feature["images"],
                 "videos": feature["videos"],
+                "audios": feature["audios"],
             }
             target_features.append(target_feature)
             kl_features.append(kl_feature)
diff --git a/src/llamafactory/data/mm_plugin.py b/src/llamafactory/data/mm_plugin.py
index a0d03699..eed8b4fc 100644
--- a/src/llamafactory/data/mm_plugin.py
+++ b/src/llamafactory/data/mm_plugin.py
@@ -9,8 +9,17 @@ import torch
 from transformers.image_utils import get_image_size, to_numpy_array
 from typing_extensions import override
 
-from ..extras.constants import IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
-from ..extras.packages import is_pillow_available, is_pyav_available, is_transformers_version_greater_than
+from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
+from ..extras.packages import (
+    is_librosa_available,
+    is_pillow_available,
+    is_pyav_available,
+    is_transformers_version_greater_than,
+)
+
+
+if is_librosa_available():
+    import librosa
 
 
 if is_pillow_available():
@@ -31,7 +40,9 @@ if is_transformers_version_greater_than("4.45.0"):
 
 if TYPE_CHECKING:
     from av.stream import Stream
+    from numpy.typing import NDArray
     from transformers import PreTrainedTokenizer, ProcessorMixin
+    from transformers.feature_extraction_sequence_utils import SequenceFeatureExtractor
     from transformers.image_processing_utils import BaseImageProcessor
 
     class EncodedImage(TypedDict):
@@ -40,6 +51,7 @@ if TYPE_CHECKING:
 
     ImageInput = Union[str, bytes, EncodedImage, ImageObject]
     VideoInput = str
+    AudioInput = Union[str, NDArray]
 
 
 def _get_paligemma_token_type_ids(
@@ -60,15 +72,17 @@ def _get_paligemma_token_type_ids(
 
 
 class BasePlugin:
-    def __init__(self, image_token: Optional[str], video_token: Optional[str]) -> None:
+    def __init__(self, image_token: Optional[str], video_token: Optional[str], audio_token: Optional[str]) -> None:
         self.image_token = image_token
         self.video_token = video_token
+        self.audio_token = audio_token
         self.expand_mm_tokens = True
 
     def _validate_input(
         self,
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
     ) -> None:
         r"""
         Validates if this model accepts the input modalities.
@@ -83,11 +97,16 @@ class BasePlugin:
                 "This model does not support video input. Please check whether the correct `template` is used."
             )
 
+        if len(audios) != 0 and self.audio_token is None:
+            raise ValueError(
+                "This model does not support audio input. Please check whether the correct `template` is used."
+            )
+
     def _preprocess_image(self, image: "ImageObject", **kwargs) -> "ImageObject":
         r"""
         Pre-processes a single image.
         """
-        image_resolution: int = kwargs.get("image_resolution")
+        image_resolution: int = kwargs["image_resolution"]
         if (image.width * image.height) > image_resolution:
             resize_factor = math.sqrt(image_resolution / (image.width * image.height))
             width, height = int(image.width * resize_factor), int(image.height * resize_factor)
@@ -102,8 +121,8 @@ class BasePlugin:
         r"""
         Computes video sample frames according to fps.
         """
-        video_fps: float = kwargs.get("video_fps")
-        video_maxlen: int = kwargs.get("video_maxlen")
+        video_fps: float = kwargs["video_fps"]
+        video_maxlen: int = kwargs["video_maxlen"]
         total_frames = video_stream.frames
         sample_frames = float(video_stream.duration * video_stream.time_base) * video_fps
         sample_frames = min(total_frames, video_maxlen, sample_frames)
@@ -126,7 +145,7 @@ class BasePlugin:
                     image = Image.open(image["path"])
 
             if not isinstance(image, ImageObject):
-                raise ValueError(f"Expect input is a list of Images, but got {type(image)}.")
+                raise ValueError(f"Expect input is a list of images, but got {type(image)}.")
 
             results.append(self._preprocess_image(image, **kwargs))
 
@@ -154,10 +173,28 @@ class BasePlugin:
 
         return results
 
+    def _regularize_audios(self, audios: Sequence["AudioInput"], **kwargs) -> List["NDArray"]:
+        r"""
+        Regularizes audios to avoid error. Including reading and resampling.
+        """
+        results = []
+        sampling_rate = kwargs["sampling_rate"]
+        for audio in audios:
+            if isinstance(audio, str):
+                audio = librosa.load(audio, sr=sampling_rate)[0]
+
+            if not isinstance(audio, np.ndarray):
+                raise ValueError(f"Expect input is a list of audios, but got {type(audio)}.")
+
+            results.append(audio)
+
+        return results
+
     def _get_mm_inputs(
         self,
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         processor: "ProcessorMixin",
     ) -> Dict[str, "torch.Tensor"]:
         r"""
@@ -172,15 +209,17 @@ class BasePlugin:
 
         It holds num_patches == torch.prod(image_grid_thw)
         """
-        image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
+        image_processor: "BaseImageProcessor" = getattr(processor, "image_processor", None)
         video_processor: "BaseImageProcessor" = getattr(processor, "video_processor", image_processor)
-        input_dict = {"images": None}  # default key
+        feature_extractor: "SequenceFeatureExtractor" = getattr(processor, "feature_extractor", None)
+        mm_inputs = {}
+
         if len(images) != 0:
             images = self._regularize_images(
                 images,
                 image_resolution=getattr(processor, "image_resolution", 768 * 768),
             )
-            input_dict["images"] = images
+            mm_inputs.update(image_processor(images, return_tensors="pt"))
 
         if len(videos) != 0:
             videos = self._regularize_videos(
@@ -189,16 +228,23 @@ class BasePlugin:
                 video_fps=getattr(processor, "video_fps", 2.0),
                 video_maxlen=getattr(processor, "video_maxlen", 128),
             )
-            input_dict["videos"] = videos
+            mm_inputs.update(video_processor(videos, return_tensors="pt"))
 
-        mm_inputs = {}
-        if image_processor != video_processor:
-            if input_dict.get("images") is not None:
-                mm_inputs.update(image_processor(input_dict["images"], return_tensors="pt"))
-            if input_dict.get("videos") is not None:
-                mm_inputs.update(video_processor(input_dict["videos"], return_tensors="pt"))
-        elif input_dict.get("images") is not None or input_dict.get("videos") is not None:  # same processor (qwen2-vl)
-            mm_inputs.update(image_processor(**input_dict, return_tensors="pt"))
+        if len(audios) != 0:
+            audios = self._regularize_audios(
+                audios,
+                sampling_rate=getattr(feature_extractor, "sampling_rate", 16000),
+            )
+            mm_inputs.update(
+                feature_extractor(
+                    audios,
+                    sampling_rate=getattr(feature_extractor, "sampling_rate", 16000),
+                    return_attention_mask=True,
+                    padding="max_length",
+                    return_tensors="pt",
+                )
+            )
+            mm_inputs["feature_attention_mask"] = mm_inputs.pop("attention_mask")  # prevent conflicts
 
         return mm_inputs
 
@@ -207,12 +253,13 @@ class BasePlugin:
         messages: Sequence[Dict[str, str]],
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         processor: Optional["ProcessorMixin"],
     ) -> List[Dict[str, str]]:
         r"""
         Pre-processes input messages before tokenization for VLMs.
         """
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
         return messages
 
     def process_token_ids(
@@ -221,21 +268,24 @@ class BasePlugin:
         labels: Optional[List[int]],
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         tokenizer: "PreTrainedTokenizer",
         processor: Optional["ProcessorMixin"],
     ) -> Tuple[List[int], Optional[List[int]]]:
         r"""
         Pre-processes token ids after tokenization for VLMs.
         """
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
         return input_ids, labels
 
     def get_mm_inputs(
         self,
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         imglens: Sequence[int],
         vidlens: Sequence[int],
+        audlens: Sequence[int],
         batch_ids: Sequence[List[int]],
         processor: Optional["ProcessorMixin"],
     ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
@@ -247,10 +297,11 @@ class BasePlugin:
             videos: a list of video inputs, shape (num_videos,)
             imglens: number of images in each sample, shape (batch_size,)
             vidlens: number of videos in each sample, shape (batch_size,)
+            audlens: number of audios in each sample, shape (batch_size,)
             batch_ids: token ids of input samples, shape (batch_size, seq_len)
             processor: a processor for pre-processing images and videos
         """
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
         return {}
 
 
@@ -261,9 +312,10 @@ class LlavaPlugin(BasePlugin):
         messages: Sequence[Dict[str, str]],
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         processor: Optional["ProcessorMixin"],
     ) -> List[Dict[str, str]]:
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
         num_image_tokens = 0
         image_seqlen = getattr(processor, "image_seqlen") if self.expand_mm_tokens else 1
         messages = deepcopy(messages)
@@ -285,13 +337,15 @@ class LlavaPlugin(BasePlugin):
         self,
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         imglens: Sequence[int],
         vidlens: Sequence[int],
+        audlens: Sequence[int],
         batch_ids: Sequence[List[int]],
         processor: Optional["ProcessorMixin"],
     ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
-        self._validate_input(images, videos)
-        return self._get_mm_inputs(images, videos, processor)
+        self._validate_input(images, videos, audios)
+        return self._get_mm_inputs(images, videos, audios, processor)
 
 
 class LlavaNextPlugin(BasePlugin):
@@ -301,12 +355,13 @@ class LlavaNextPlugin(BasePlugin):
         messages: Sequence[Dict[str, str]],
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         processor: Optional["ProcessorMixin"],
     ) -> List[Dict[str, str]]:
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
         num_image_tokens = 0
         messages = deepcopy(messages)
-        mm_inputs = self._get_mm_inputs(images, videos, processor)
+        mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
         if "image_sizes" in mm_inputs:
             image_sizes = iter(mm_inputs["image_sizes"])
 
@@ -339,13 +394,15 @@ class LlavaNextPlugin(BasePlugin):
         self,
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         imglens: Sequence[int],
         vidlens: Sequence[int],
+        audlens: Sequence[int],
         batch_ids: Sequence[List[int]],
         processor: Optional["ProcessorMixin"],
     ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
-        self._validate_input(images, videos)
-        return self._get_mm_inputs(images, videos, processor)
+        self._validate_input(images, videos, audios)
+        return self._get_mm_inputs(images, videos, audios, processor)
 
 
 class LlavaNextVideoPlugin(BasePlugin):
@@ -355,12 +412,13 @@ class LlavaNextVideoPlugin(BasePlugin):
         messages: Sequence[Dict[str, str]],
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         processor: Optional["ProcessorMixin"],
     ) -> List[Dict[str, str]]:
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
         num_image_tokens, num_video_tokens = 0, 0
         messages = deepcopy(messages)
-        mm_inputs = self._get_mm_inputs(images, videos, processor)
+        mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
         if "pixel_values" in mm_inputs:
             image_sizes = iter(mm_inputs["image_sizes"])
             height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
@@ -408,13 +466,15 @@ class LlavaNextVideoPlugin(BasePlugin):
         self,
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         imglens: Sequence[int],
         vidlens: Sequence[int],
+        audlens: Sequence[int],
         batch_ids: Sequence[List[int]],
         processor: Optional["ProcessorMixin"],
     ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
-        self._validate_input(images, videos)
-        return self._get_mm_inputs(images, videos, processor)
+        self._validate_input(images, videos, audios)
+        return self._get_mm_inputs(images, videos, audios, processor)
 
 
 class MiniCPMVPlugin(BasePlugin):
@@ -424,26 +484,30 @@ class MiniCPMVPlugin(BasePlugin):
         messages: Sequence[Dict[str, str]],
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         processor: Optional["ProcessorMixin"],
     ) -> List[Dict[str, str]]:
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
         num_image_tokens = 0
         num_video_tokens = 0
+        num_audio_tokens = 0
         messages = deepcopy(messages)
         image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
         mm_inputs = {}
+        audio_inputs = {}
+        audio_parts = []
         if len(images) != 0 and len(videos) != 0:
             raise ValueError("MiniCPM-V model does not support input images and videos at the same time.")
 
         if len(videos) != 0:
             max_slice_nums = 2
             use_image_id = False
-            mm_inputs = self._get_mm_inputs([], videos, processor)
+            mm_inputs = self._get_mm_inputs([], videos, [], processor)
         else:
             max_slice_nums = image_processor.max_slice_nums
             use_image_id = image_processor.use_image_id
 
-        for message in messages:
+        for i, message in enumerate(messages):
             content = message["content"]
             while IMAGE_PLACEHOLDER in content:
                 content = content.replace(IMAGE_PLACEHOLDER, "{{image}}", 1)
@@ -454,15 +518,25 @@ class MiniCPMVPlugin(BasePlugin):
                 content = content.replace(VIDEO_PLACEHOLDER, "{{image}}" * video_seqlen, 1)
                 num_video_tokens += 1
 
-            message["content"] = content.replace("{{image}}", "(<image>./</image>)")
+            while AUDIO_PLACEHOLDER in content:
+                audio_parts.append(i)
+                content = content.replace(AUDIO_PLACEHOLDER, "{{audio}}", 1)
+                num_audio_tokens += 1
+
+            message["content"] = content.replace("{{image}}", "(<image>./</image>)").replace(
+                "{{audio}}", "(<audio>./</audio>)"
+            )
 
         if num_image_tokens > 0:
-            mm_inputs = self._get_mm_inputs(images, [], processor)
+            mm_inputs = self._get_mm_inputs(images, [], [], processor)
+
+        if num_audio_tokens > 0:
+            audio_parts_ls = [audio_parts]
+            audio_inputs = self._get_mm_inputs([], [], audios, processor, audio_parts_ls=audio_parts_ls, ret_phs=True)
 
         if mm_inputs:
             pattern = "(<image>./</image>)"
             image_sizes = mm_inputs["image_sizes"]
-
             for index, message in enumerate(messages):
                 text = message["content"]
                 image_tags = re.findall(pattern, text)
@@ -480,12 +554,29 @@ class MiniCPMVPlugin(BasePlugin):
                 final_text += text_chunks[-1]
                 messages[index]["content"] = final_text
 
+        if audio_inputs:
+            pattern = "(<audio>./</audio>)"
+            for index, message in enumerate(messages):
+                text = message["content"]
+                audio_tags = re.findall(pattern, text)
+                text_chunks = text.split(pattern)
+                final_text = ""
+                for i in range(len(audio_tags)):
+                    audio_placeholder = audio_inputs["audio_phs"][0][i]
+                    final_text = final_text + text_chunks[i] + audio_placeholder
+
+                final_text += text_chunks[-1]
+                messages[index]["content"] = final_text
+
         if len(images) != num_image_tokens:
             raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens.")
 
         if len(videos) != num_video_tokens:
             raise ValueError(f"The number of videos does not match the number of {VIDEO_PLACEHOLDER} tokens.")
 
+        if len(audios) != num_audio_tokens:
+            raise ValueError(f"The number of audios does not match the number of {AUDIO_PLACEHOLDER} tokens.")
+
         return messages
 
     @override
@@ -493,6 +584,7 @@ class MiniCPMVPlugin(BasePlugin):
         self,
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         processor: "ProcessorMixin",
         **kwargs,
     ) -> Dict[str, "torch.Tensor"]:
@@ -528,6 +620,30 @@ class MiniCPMVPlugin(BasePlugin):
             video_inputs = image_processor(videos, do_pad=True, max_slice_nums=2, return_tensors="pt")
             mm_inputs.update(video_inputs)
 
+        if len(audios) != 0:
+            audio_parts_ls = kwargs.get("audio_parts_ls", None)
+            new_audios = []
+            for audio in audios:
+                if not isinstance(audio, np.ndarray):
+                    audio = librosa.load(audio, sr=processor.feature_extractor.sampling_rate)[0]
+                new_audios.append(audio)
+
+            audios_ls = []
+            idx = 0
+            for audio_parts in audio_parts_ls:
+                audios_ls.append(new_audios[idx : idx + len(audio_parts)])
+                idx += len(audio_parts)
+
+            audio_features, audio_feature_lens, audio_phs = processor.audio_feature_extract(
+                audios_ls,
+                audio_parts_ls,
+                chunk_input=True,
+                sampling_rate=16000,
+            )
+            mm_inputs.update({"audio_features": audio_features, "audio_feature_lens": audio_feature_lens})
+            if kwargs.get("ret_phs", False):
+                mm_inputs.update({"audio_phs": audio_phs})
+
         return mm_inputs
 
     @override
@@ -535,12 +651,16 @@ class MiniCPMVPlugin(BasePlugin):
         self,
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         imglens: Sequence[int],
         vidlens: Sequence[int],
+        audlens: Sequence[int],
         batch_ids: Sequence[List[int]],
         processor: Optional["ProcessorMixin"],
     ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
+
+        # image bound
         image_bounds_list = []
         valid_image_nums_ls = []
         for i, input_ids in enumerate(batch_ids):
@@ -561,8 +681,38 @@ class MiniCPMVPlugin(BasePlugin):
             )
             image_bounds_list.append(image_bounds)
 
-        mm_inputs = self._get_mm_inputs(images, videos, processor, valid_image_nums_ls=valid_image_nums_ls)
+        mm_inputs = self._get_mm_inputs(images, videos, [], processor, valid_image_nums_ls=valid_image_nums_ls)
+        if "tgt_sizes" not in mm_inputs:
+            dummy_data = [torch.empty(0) for _ in range(len(batch_ids))]
+            mm_inputs.update({"tgt_sizes": dummy_data, "pixel_values": dummy_data, "image_sizes": dummy_data})
+
         mm_inputs.update({"image_bound": image_bounds_list})
+
+        if len(audios) > 0:
+            # audio bound
+            audio_bounds_ls = []
+            spk_bounds_ls = []
+            audio_parts_ls = []
+
+            for input_ids, audiolen in zip(batch_ids, audlens):
+                input_ids_ = torch.tensor(input_ids)
+                audio_start_idx = torch.where(input_ids_ == processor.tokenizer.audio_start_id)[0]
+                audio_end_idx = torch.where(input_ids_ == processor.tokenizer.audio_end_id)[0]
+                assert len(audio_start_idx) == len(audio_end_idx)
+                audio_bounds = torch.hstack([(audio_start_idx + 1).unsqueeze(-1), audio_end_idx.unsqueeze(-1)])
+                audio_bounds_ls.append(audio_bounds)
+                audio_parts_ls.append(list(range(audiolen)))
+
+                spk_start_idx = torch.where(input_ids_ == processor.tokenizer.spk_start_id)[0]
+                spk_end_idx = torch.where(input_ids_ == processor.tokenizer.spk_end_id)[0]
+                assert len(spk_start_idx) == len(spk_end_idx)
+                spk_bounds = torch.hstack([(spk_start_idx + 1).unsqueeze(-1), spk_end_idx.unsqueeze(-1)])
+                spk_bounds_ls.append(spk_bounds)
+
+            audio_inputs = self._get_mm_inputs([], [], audios, processor, audio_parts_ls=audio_parts_ls)
+            mm_inputs.update(audio_inputs)
+            mm_inputs.update({"audio_bounds": audio_bounds_ls, "spk_bounds": spk_bounds_ls})
+
         return mm_inputs
 
 
@@ -573,9 +723,10 @@ class MllamaPlugin(BasePlugin):
         messages: Sequence[Dict[str, str]],
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         processor: Optional["ProcessorMixin"],
     ) -> List[Dict[str, str]]:
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
         num_image_tokens = 0
         messages = deepcopy(messages)
         for message in messages:
@@ -593,6 +744,7 @@ class MllamaPlugin(BasePlugin):
         self,
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         processor: "ProcessorMixin",
         **kwargs,
     ) -> Dict[str, "torch.Tensor"]:
@@ -617,17 +769,20 @@ class MllamaPlugin(BasePlugin):
 
         return image_processor(batch_images, return_tensors="pt")
 
+    @override
     def get_mm_inputs(
         self,
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         imglens: Sequence[int],
         vidlens: Sequence[int],
+        audlens: Sequence[int],
         batch_ids: Sequence[List[int]],
         processor: Optional["ProcessorMixin"],
     ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
-        self._validate_input(images, videos)
-        mm_inputs = self._get_mm_inputs(images, videos, processor, imglens=imglens)
+        self._validate_input(images, videos, audios)
+        mm_inputs = self._get_mm_inputs(images, videos, audios, processor, imglens=imglens)
         num_tiles = mm_inputs.pop("num_tiles")
         image_token_id = getattr(processor, "image_token_id")
         max_image_tiles = getattr(processor.image_processor, "max_image_tiles")
@@ -652,9 +807,10 @@ class PaliGemmaPlugin(BasePlugin):
         messages: Sequence[Dict[str, str]],
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         processor: Optional["ProcessorMixin"],
     ) -> List[Dict[str, str]]:
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
         num_image_tokens = 0
         messages = deepcopy(messages)
         for message in messages:
@@ -677,10 +833,11 @@ class PaliGemmaPlugin(BasePlugin):
         labels: Optional[List[int]],
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         tokenizer: "PreTrainedTokenizer",
         processor: Optional["ProcessorMixin"],
     ) -> Tuple[List[int], Optional[List[int]]]:
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
         num_images = len(images)
         image_seqlen = num_images * getattr(processor, "image_seqlen") if self.expand_mm_tokens else 0  # skip mm token
         image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
@@ -695,14 +852,16 @@ class PaliGemmaPlugin(BasePlugin):
         self,
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         imglens: Sequence[int],
         vidlens: Sequence[int],
+        audlens: Sequence[int],
         batch_ids: Sequence[List[int]],
         processor: Optional["ProcessorMixin"],
     ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
         seqlens = [len(input_ids) for input_ids in batch_ids]
-        mm_inputs = self._get_mm_inputs(images, videos, processor)
+        mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
         mm_inputs["token_type_ids"] = _get_paligemma_token_type_ids(imglens, seqlens, processor)
         return mm_inputs
 
@@ -714,9 +873,10 @@ class PixtralPlugin(BasePlugin):
         messages: Sequence[Dict[str, str]],
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         processor: Optional["ProcessorMixin"],
     ) -> List[Dict[str, str]]:
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
         patch_size = getattr(processor, "patch_size")
         image_token = getattr(processor, "image_token")
         image_break_token = getattr(processor, "image_break_token")
@@ -724,7 +884,7 @@ class PixtralPlugin(BasePlugin):
 
         num_image_tokens = 0
         messages = deepcopy(messages)
-        mm_inputs = self._get_mm_inputs(images, videos, processor)
+        mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
         image_input_sizes = mm_inputs.get("image_sizes", None)
         for message in messages:
             content = message["content"]
@@ -759,13 +919,15 @@ class PixtralPlugin(BasePlugin):
         self,
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         imglens: Sequence[int],
         vidlens: Sequence[int],
+        audlens: Sequence[int],
         batch_ids: Sequence[List[int]],
         processor: Optional["ProcessorMixin"],
     ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
-        self._validate_input(images, videos)
-        mm_inputs = self._get_mm_inputs(images, videos, processor)
+        self._validate_input(images, videos, audios)
+        mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
         if mm_inputs.get("pixel_values"):
             mm_inputs["pixel_values"] = mm_inputs["pixel_values"][0]
 
@@ -773,6 +935,58 @@ class PixtralPlugin(BasePlugin):
         return mm_inputs
 
 
+class Qwen2AudioPlugin(BasePlugin):
+    @override
+    def process_messages(
+        self,
+        messages: Sequence[Dict[str, str]],
+        images: Sequence["ImageInput"],
+        videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
+        processor: Optional["ProcessorMixin"],
+    ) -> List[Dict[str, str]]:
+        self._validate_input(images, videos, audios)
+        bos_token: str = getattr(processor, "audio_bos_token")
+        eos_token: str = getattr(processor, "audio_eos_token")
+        mm_inputs = self._get_mm_inputs([], [], audios, processor)
+        if "feature_attention_mask" in mm_inputs:
+            audio_lengths = mm_inputs["feature_attention_mask"].sum(-1).tolist()
+
+        num_audio_tokens = 0
+        for message in messages:
+            content = message["content"]
+            while AUDIO_PLACEHOLDER in content:
+                audio_length = audio_lengths.pop(0)
+                input_length = (audio_length - 1) // 2 + 1
+                audio_seqlen = (input_length - 2) // 2 + 1 if self.expand_mm_tokens else 1
+                content = content.replace(
+                    AUDIO_PLACEHOLDER, f"{bos_token}{self.audio_token * audio_seqlen}{eos_token}", 1
+                )
+                num_audio_tokens += 1
+
+            message["content"] = content
+
+        if len(audios) != num_audio_tokens:
+            raise ValueError(f"The number of audios does not match the number of {AUDIO_PLACEHOLDER} tokens.")
+
+        return messages
+
+    @override
+    def get_mm_inputs(
+        self,
+        images: Sequence["ImageInput"],
+        videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
+        imglens: Sequence[int],
+        vidlens: Sequence[int],
+        audlens: Sequence[int],
+        batch_ids: Sequence[List[int]],
+        processor: Optional["ProcessorMixin"],
+    ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
+        self._validate_input(images, videos, audios)
+        return self._get_mm_inputs(images, videos, audios, processor)
+
+
 class Qwen2vlPlugin(BasePlugin):
     @override
     def _preprocess_image(self, image: "ImageObject", **kwargs) -> "ImageObject":
@@ -820,12 +1034,13 @@ class Qwen2vlPlugin(BasePlugin):
         messages: Sequence[Dict[str, str]],
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         processor: Optional["ProcessorMixin"],
     ) -> List[Dict[str, str]]:
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
         image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
         merge_length: int = getattr(image_processor, "merge_size") ** 2
-        mm_inputs = self._get_mm_inputs(images, videos, processor)
+        mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
         image_grid_thw = mm_inputs.get("image_grid_thw", [])
         video_grid_thw = mm_inputs.get("video_grid_thw", [])
 
@@ -868,13 +1083,15 @@ class Qwen2vlPlugin(BasePlugin):
         self,
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         imglens: Sequence[int],
         vidlens: Sequence[int],
+        audlens: Sequence[int],
         batch_ids: Sequence[List[int]],
         processor: Optional["ProcessorMixin"],
     ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
-        self._validate_input(images, videos)
-        mm_inputs = self._get_mm_inputs(images, videos, processor)
+        self._validate_input(images, videos, audios)
+        mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
         image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
         if "second_per_grid_ts" in getattr(image_processor, "model_input_names", []) and "video_grid_thw" in mm_inputs:
             video_fps = getattr(processor, "video_fps", 2.0)
@@ -892,12 +1109,13 @@ class VideoLlavaPlugin(BasePlugin):
         messages: Sequence[Dict[str, str]],
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         processor: Optional["ProcessorMixin"],
     ) -> List[Dict[str, str]]:
-        self._validate_input(images, videos)
+        self._validate_input(images, videos, audios)
         num_image_tokens, num_video_tokens = 0, 0
         messages = deepcopy(messages)
-        mm_inputs = self._get_mm_inputs(images, videos, processor)
+        mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
         num_frames = 0
         has_images = "pixel_values_images" in mm_inputs
         has_videos = "pixel_values_videos" in mm_inputs
@@ -945,13 +1163,15 @@ class VideoLlavaPlugin(BasePlugin):
         self,
         images: Sequence["ImageInput"],
         videos: Sequence["VideoInput"],
+        audios: Sequence["AudioInput"],
         imglens: Sequence[int],
         vidlens: Sequence[int],
+        audlens: Sequence[int],
         batch_ids: Sequence[List[int]],
         processor: Optional["ProcessorMixin"],
     ) -> Dict[str, Union[List[int], "torch.Tensor"]]:
-        self._validate_input(images, videos)
-        return self._get_mm_inputs(images, videos, processor)
+        self._validate_input(images, videos, audios)
+        return self._get_mm_inputs(images, videos, audios, processor)
 
 
 PLUGINS = {
@@ -963,6 +1183,7 @@ PLUGINS = {
     "mllama": MllamaPlugin,
     "paligemma": PaliGemmaPlugin,
     "pixtral": PixtralPlugin,
+    "qwen2_audio": Qwen2AudioPlugin,
     "qwen2_vl": Qwen2vlPlugin,
     "video_llava": VideoLlavaPlugin,
 }
@@ -972,9 +1193,10 @@ def get_mm_plugin(
     name: str,
     image_token: Optional[str] = None,
     video_token: Optional[str] = None,
+    audio_token: Optional[str] = None,
 ) -> "BasePlugin":
     plugin_class = PLUGINS.get(name, None)
     if plugin_class is None:
         raise ValueError(f"Multimodal plugin `{name}` not found.")
 
-    return plugin_class(image_token, video_token)
+    return plugin_class(image_token, video_token, audio_token)
diff --git a/src/llamafactory/data/parser.py b/src/llamafactory/data/parser.py
index 493eba68..c2cb8dd7 100644
--- a/src/llamafactory/data/parser.py
+++ b/src/llamafactory/data/parser.py
@@ -44,6 +44,7 @@ class DatasetAttr:
     tools: Optional[str] = None
     images: Optional[str] = None
     videos: Optional[str] = None
+    audios: Optional[str] = None
     # rlhf columns
     chosen: Optional[str] = None
     rejected: Optional[str] = None
@@ -135,7 +136,7 @@ def get_dataset_list(dataset_names: Optional[Sequence[str]], dataset_dir: str) -
         dataset_attr.set_attr("num_samples", dataset_info[name])
 
         if "columns" in dataset_info[name]:
-            column_names = ["system", "tools", "images", "videos", "chosen", "rejected", "kto_tag"]
+            column_names = ["system", "tools", "images", "videos", "audios", "chosen", "rejected", "kto_tag"]
             if dataset_attr.formatting == "alpaca":
                 column_names.extend(["prompt", "query", "response", "history"])
             else:
diff --git a/src/llamafactory/data/processors/feedback.py b/src/llamafactory/data/processors/feedback.py
index 37b8154d..2451fefb 100644
--- a/src/llamafactory/data/processors/feedback.py
+++ b/src/llamafactory/data/processors/feedback.py
@@ -24,7 +24,7 @@ if TYPE_CHECKING:
     from transformers import PreTrainedTokenizer, ProcessorMixin
 
     from ...hparams import DataArguments
-    from ..mm_plugin import ImageInput, VideoInput
+    from ..mm_plugin import AudioInput, ImageInput, VideoInput
     from ..template import Template
 
 
@@ -39,6 +39,7 @@ def _encode_feedback_example(
     tools: Optional[str],
     images: Sequence["ImageInput"],
     videos: Sequence["VideoInput"],
+    audios: Sequence["AudioInput"],
     template: "Template",
     tokenizer: "PreTrainedTokenizer",
     processor: Optional["ProcessorMixin"],
@@ -56,8 +57,8 @@ def _encode_feedback_example(
     else:
         kl_messages = prompt + [kl_response[1]]
 
-    messages = template.mm_plugin.process_messages(messages, images, videos, processor)
-    kl_messages = template.mm_plugin.process_messages(kl_messages, images, videos, processor)
+    messages = template.mm_plugin.process_messages(messages, images, videos, audios, processor)
+    kl_messages = template.mm_plugin.process_messages(kl_messages, images, videos, audios, processor)
     prompt_ids, response_ids = template.encode_oneturn(tokenizer, messages, system, tools)
     kl_prompt_ids, kl_response_ids = template.encode_oneturn(tokenizer, kl_messages, system, tools)
 
@@ -65,8 +66,12 @@ def _encode_feedback_example(
         response_ids += [tokenizer.eos_token_id]
         kl_response_ids += [tokenizer.eos_token_id]
 
-    prompt_ids, _ = template.mm_plugin.process_token_ids(prompt_ids, None, images, videos, tokenizer, processor)
-    kl_prompt_ids, _ = template.mm_plugin.process_token_ids(kl_prompt_ids, None, images, videos, tokenizer, processor)
+    prompt_ids, _ = template.mm_plugin.process_token_ids(
+        prompt_ids, None, images, videos, audios, tokenizer, processor
+    )
+    kl_prompt_ids, _ = template.mm_plugin.process_token_ids(
+        kl_prompt_ids, None, images, videos, audios, tokenizer, processor
+    )
 
     source_len, target_len = infer_seqlen(len(prompt_ids), len(response_ids), cutoff_len)
     prompt_ids = prompt_ids[:source_len]
@@ -107,6 +112,7 @@ def preprocess_feedback_dataset(
             tools=examples["_tools"][i],
             images=examples["_images"][i] or [],
             videos=examples["_videos"][i] or [],
+            audios=examples["_audios"][i] or [],
             template=template,
             tokenizer=tokenizer,
             processor=processor,
@@ -121,6 +127,7 @@ def preprocess_feedback_dataset(
         model_inputs["kto_tags"].append(kto_tag)
         model_inputs["images"].append(examples["_images"][i])
         model_inputs["videos"].append(examples["_videos"][i])
+        model_inputs["audios"].append(examples["_audios"][i])
 
     desirable_num = sum([1 for tag in model_inputs["kto_tags"] if tag])
     undesirable_num = len(model_inputs["kto_tags"]) - desirable_num
diff --git a/src/llamafactory/data/processors/pairwise.py b/src/llamafactory/data/processors/pairwise.py
index 242febfc..2445de69 100644
--- a/src/llamafactory/data/processors/pairwise.py
+++ b/src/llamafactory/data/processors/pairwise.py
@@ -24,7 +24,7 @@ if TYPE_CHECKING:
     from transformers import PreTrainedTokenizer, ProcessorMixin
 
     from ...hparams import DataArguments
-    from ..mm_plugin import ImageInput, VideoInput
+    from ..mm_plugin import AudioInput, ImageInput, VideoInput
     from ..template import Template
 
 
@@ -38,13 +38,14 @@ def _encode_pairwise_example(
     tools: Optional[str],
     images: Sequence["ImageInput"],
     videos: Sequence["VideoInput"],
+    audios: Sequence["AudioInput"],
     template: "Template",
     tokenizer: "PreTrainedTokenizer",
     processor: Optional["ProcessorMixin"],
     cutoff_len: int,
 ) -> Tuple[List[int], List[int], List[int], List[int]]:
-    chosen_messages = template.mm_plugin.process_messages(prompt + [response[0]], images, videos, processor)
-    rejected_messages = template.mm_plugin.process_messages(prompt + [response[1]], images, videos, processor)
+    chosen_messages = template.mm_plugin.process_messages(prompt + [response[0]], images, videos, audios, processor)
+    rejected_messages = template.mm_plugin.process_messages(prompt + [response[1]], images, videos, audios, processor)
     prompt_ids, chosen_ids = template.encode_oneturn(tokenizer, chosen_messages, system, tools)
     _, rejected_ids = template.encode_oneturn(tokenizer, rejected_messages, system, tools)
 
@@ -52,7 +53,9 @@ def _encode_pairwise_example(
         chosen_ids += [tokenizer.eos_token_id]
         rejected_ids += [tokenizer.eos_token_id]
 
-    prompt_ids, _ = template.mm_plugin.process_token_ids(prompt_ids, None, images, videos, tokenizer, processor)
+    prompt_ids, _ = template.mm_plugin.process_token_ids(
+        prompt_ids, None, images, videos, audios, tokenizer, processor
+    )
     # consider the response is more important
     source_len, target_len = infer_seqlen(len(prompt_ids), max(len(chosen_ids), len(rejected_ids)), cutoff_len)
     prompt_ids = prompt_ids[:source_len]
@@ -89,6 +92,7 @@ def preprocess_pairwise_dataset(
             tools=examples["_tools"][i],
             images=examples["_images"][i] or [],
             videos=examples["_videos"][i] or [],
+            audios=examples["_audios"][i] or [],
             template=template,
             tokenizer=tokenizer,
             processor=processor,
@@ -102,6 +106,7 @@ def preprocess_pairwise_dataset(
         model_inputs["rejected_labels"].append(rejected_labels)
         model_inputs["images"].append(examples["_images"][i])
         model_inputs["videos"].append(examples["_videos"][i])
+        model_inputs["audios"].append(examples["_audios"][i])
 
     return model_inputs
 
diff --git a/src/llamafactory/data/processors/supervised.py b/src/llamafactory/data/processors/supervised.py
index d7dc7379..771b3cba 100644
--- a/src/llamafactory/data/processors/supervised.py
+++ b/src/llamafactory/data/processors/supervised.py
@@ -24,7 +24,7 @@ if TYPE_CHECKING:
     from transformers import PreTrainedTokenizer, ProcessorMixin
 
     from ...hparams import DataArguments
-    from ..mm_plugin import ImageInput, VideoInput
+    from ..mm_plugin import AudioInput, ImageInput, VideoInput
     from ..template import Template
 
 
@@ -38,6 +38,7 @@ def _encode_supervised_example(
     tools: Optional[str],
     images: Sequence["ImageInput"],
     videos: Sequence["VideoInput"],
+    audios: Sequence["AudioInput"],
     template: "Template",
     tokenizer: "PreTrainedTokenizer",
     processor: Optional["ProcessorMixin"],
@@ -45,8 +46,8 @@ def _encode_supervised_example(
     train_on_prompt: bool,
     mask_history: bool,
 ) -> Tuple[List[int], List[int]]:
-    messages = template.mm_plugin.process_messages(prompt + response, images, videos, processor)
-    input_ids, labels = template.mm_plugin.process_token_ids([], [], images, videos, tokenizer, processor)
+    messages = template.mm_plugin.process_messages(prompt + response, images, videos, audios, processor)
+    input_ids, labels = template.mm_plugin.process_token_ids([], [], images, videos, audios, tokenizer, processor)
     encoded_pairs = template.encode_multiturn(tokenizer, messages, system, tools)
     total_length = len(input_ids) + (1 if template.efficient_eos else 0)
     if mask_history:
@@ -111,6 +112,7 @@ def preprocess_supervised_dataset(
             tools=examples["_tools"][i],
             images=examples["_images"][i] or [],
             videos=examples["_videos"][i] or [],
+            audios=examples["_audios"][i] or [],
             template=template,
             tokenizer=tokenizer,
             processor=processor,
@@ -123,6 +125,7 @@ def preprocess_supervised_dataset(
         model_inputs["labels"].append(labels)
         model_inputs["images"].append(examples["_images"][i])
         model_inputs["videos"].append(examples["_videos"][i])
+        model_inputs["audios"].append(examples["_audios"][i])
 
     return model_inputs
 
@@ -138,7 +141,7 @@ def preprocess_packed_supervised_dataset(
     # build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
     # and labels with format `<ignore> ... <ignore> Y1 <eos> <ignore> ... <ignore> Y2 <eos>`
     valid_num = 0
-    batch_input_ids, batch_labels, batch_images, batch_videos = [], [], [], []
+    batch_input_ids, batch_labels, batch_images, batch_videos, batch_audios = [], [], [], [], []
     lengths = []
     length2indexes = defaultdict(list)
     for i in range(len(examples["_prompt"])):
@@ -155,6 +158,7 @@ def preprocess_packed_supervised_dataset(
             tools=examples["_tools"][i],
             images=examples["_images"][i] or [],
             videos=examples["_videos"][i] or [],
+            audios=examples["_audios"][i] or [],
             template=template,
             tokenizer=tokenizer,
             processor=processor,
@@ -172,19 +176,21 @@ def preprocess_packed_supervised_dataset(
             batch_labels.append(labels)
             batch_images.append(examples["_images"][i] or [])
             batch_videos.append(examples["_videos"][i] or [])
+            batch_audios.append(examples["_audios"][i] or [])
             valid_num += 1
 
     model_inputs = defaultdict(list)
     knapsacks = greedy_knapsack(lengths, data_args.cutoff_len - 1)  # reserved for the padding token
     for knapsack in knapsacks:
         packed_input_ids, packed_attention_masks, packed_labels = [], [], []
-        packed_images, packed_videos = [], []
+        packed_images, packed_videos, packed_audios = [], [], []
         for i, length in enumerate(knapsack):
             index = length2indexes[length].pop()
             packed_input_ids += batch_input_ids[index]
             packed_labels += batch_labels[index]
             packed_images += batch_images[index]
             packed_videos += batch_videos[index]
+            packed_audios += batch_audios[index]
             if data_args.neat_packing:
                 packed_attention_masks += [i + 1] * len(batch_input_ids[index])  # start from 1
             else:
@@ -207,6 +213,7 @@ def preprocess_packed_supervised_dataset(
         model_inputs["labels"].append(packed_labels)
         model_inputs["images"].append(packed_images or None)
         model_inputs["videos"].append(packed_videos or None)
+        model_inputs["audios"].append(packed_audios or None)
 
     return model_inputs
 
diff --git a/src/llamafactory/data/processors/unsupervised.py b/src/llamafactory/data/processors/unsupervised.py
index d85f28dd..4e62d5c9 100644
--- a/src/llamafactory/data/processors/unsupervised.py
+++ b/src/llamafactory/data/processors/unsupervised.py
@@ -24,7 +24,7 @@ if TYPE_CHECKING:
     from transformers import PreTrainedTokenizer, ProcessorMixin
 
     from ...hparams import DataArguments
-    from ..mm_plugin import ImageInput, VideoInput
+    from ..mm_plugin import AudioInput, ImageInput, VideoInput
     from ..template import Template
 
 
@@ -38,6 +38,7 @@ def _encode_unsupervised_example(
     tools: Optional[str],
     images: Sequence["ImageInput"],
     videos: Sequence["VideoInput"],
+    audios: Sequence["AudioInput"],
     template: "Template",
     tokenizer: "PreTrainedTokenizer",
     processor: Optional["ProcessorMixin"],
@@ -48,12 +49,12 @@ def _encode_unsupervised_example(
     else:
         messages = prompt + [{"role": Role.ASSISTANT.value, "content": ""}]
 
-    messages = template.mm_plugin.process_messages(messages, images, videos, processor)
+    messages = template.mm_plugin.process_messages(messages, images, videos, audios, processor)
     input_ids, labels = template.encode_oneturn(tokenizer, messages, system, tools)
     if template.efficient_eos:
         labels += [tokenizer.eos_token_id]
 
-    input_ids, _ = template.mm_plugin.process_token_ids(input_ids, None, images, videos, tokenizer, processor)
+    input_ids, _ = template.mm_plugin.process_token_ids(input_ids, None, images, videos, audios, tokenizer, processor)
     source_len, target_len = infer_seqlen(len(input_ids), len(labels), cutoff_len)
     input_ids = input_ids[:source_len]
     labels = labels[:target_len]
@@ -83,6 +84,7 @@ def preprocess_unsupervised_dataset(
             tools=examples["_tools"][i],
             images=examples["_images"][i] or [],
             videos=examples["_videos"][i] or [],
+            audios=examples["_audios"][i] or [],
             template=template,
             tokenizer=tokenizer,
             processor=processor,
@@ -93,6 +95,7 @@ def preprocess_unsupervised_dataset(
         model_inputs["labels"].append(labels)
         model_inputs["images"].append(examples["_images"][i])
         model_inputs["videos"].append(examples["_videos"][i])
+        model_inputs["audios"].append(examples["_audios"][i])
 
     return model_inputs
 
diff --git a/src/llamafactory/data/template.py b/src/llamafactory/data/template.py
index 5615557c..c1701a84 100644
--- a/src/llamafactory/data/template.py
+++ b/src/llamafactory/data/template.py
@@ -890,7 +890,7 @@ _register_template(
 )
 
 
-# copied from chatml template
+# copied from qwen template
 _register_template(
     name="llava_next_qwen",
     format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
@@ -979,7 +979,7 @@ _register_template(
     format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
     format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
     stop_words=["<|im_end|>"],
-    mm_plugin=get_mm_plugin(name="minicpm_v", image_token="<image>", video_token="<video>"),
+    mm_plugin=get_mm_plugin(name="minicpm_v", image_token="<image>", video_token="<video>", audio_token="<audio>"),
 )
 
 
@@ -1144,6 +1144,18 @@ _register_template(
 
 
 # copied from chatml template
+_register_template(
+    name="qwen2_audio",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    default_system="You are a helpful assistant.",
+    stop_words=["<|im_end|>"],
+    mm_plugin=get_mm_plugin(name="qwen2_audio", audio_token="<|AUDIO|>"),
+)
+
+
+# copied from qwen template
 _register_template(
     name="qwen2_vl",
     format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
diff --git a/src/llamafactory/extras/constants.py b/src/llamafactory/extras/constants.py
index 90cd21e6..3df6d60c 100644
--- a/src/llamafactory/extras/constants.py
+++ b/src/llamafactory/extras/constants.py
@@ -22,6 +22,8 @@ from peft.utils import WEIGHTS_NAME as ADAPTER_WEIGHTS_NAME
 from transformers.utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME
 
 
+AUDIO_PLACEHOLDER = os.environ.get("AUDIO_PLACEHOLDER", "<audio>")
+
 CHECKPOINT_NAMES = {
     SAFE_ADAPTER_WEIGHTS_NAME,
     ADAPTER_WEIGHTS_NAME,
@@ -58,6 +60,8 @@ METHODS = ["full", "freeze", "lora"]
 
 MOD_SUPPORTED_MODELS = {"bloom", "falcon", "gemma", "llama", "mistral", "mixtral", "phi", "starcoder2"}
 
+MULTIMODAL_SUPPORTED_MODELS = set()
+
 PEFT_METHODS = {"lora"}
 
 RUNNING_LOG = "running_log.txt"
@@ -89,8 +93,6 @@ V_HEAD_WEIGHTS_NAME = "value_head.bin"
 
 V_HEAD_SAFE_WEIGHTS_NAME = "value_head.safetensors"
 
-VISION_MODELS = set()
-
 
 class DownloadSource(str, Enum):
     DEFAULT = "hf"
@@ -101,14 +103,16 @@ class DownloadSource(str, Enum):
 def register_model_group(
     models: Dict[str, Dict[DownloadSource, str]],
     template: Optional[str] = None,
-    vision: bool = False,
+    multimodal: bool = False,
 ) -> None:
     for name, path in models.items():
         SUPPORTED_MODELS[name] = path
-        if template is not None and (any(suffix in name for suffix in ("-Chat", "-Distill", "-Instruct")) or vision):
+        if template is not None and (
+            any(suffix in name for suffix in ("-Chat", "-Distill", "-Instruct")) or multimodal
+        ):
             DEFAULT_TEMPLATE[name] = template
-        if vision:
-            VISION_MODELS.add(name)
+        if multimodal:
+            MULTIMODAL_SUPPORTED_MODELS.add(name)
 
 
 register_model_group(
@@ -1030,7 +1034,7 @@ register_model_group(
         },
     },
     template="mllama",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -1046,7 +1050,7 @@ register_model_group(
         },
     },
     template="llava",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -1062,7 +1066,7 @@ register_model_group(
         },
     },
     template="llava_next",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -1074,7 +1078,7 @@ register_model_group(
         },
     },
     template="llava_next_mistral",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -1086,7 +1090,7 @@ register_model_group(
         },
     },
     template="llava_next_llama3",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -1098,7 +1102,7 @@ register_model_group(
         },
     },
     template="llava_next_yi",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -1114,7 +1118,7 @@ register_model_group(
         },
     },
     template="llava_next_qwen",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -1130,7 +1134,7 @@ register_model_group(
         },
     },
     template="llava_next_video",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -1142,7 +1146,7 @@ register_model_group(
         },
     },
     template="llava_next_video_mistral",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -1157,7 +1161,7 @@ register_model_group(
         },
     },
     template="llava_next_video_yi",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -1207,7 +1211,7 @@ register_model_group(
         },
     },
     template="minicpm_v",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -1219,7 +1223,7 @@ register_model_group(
         },
     },
     template="minicpm_v",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -1424,7 +1428,7 @@ register_model_group(
         },
     },
     template="paligemma",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -1468,7 +1472,7 @@ register_model_group(
         },
     },
     template="paligemma",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -1551,7 +1555,7 @@ register_model_group(
         }
     },
     template="pixtral",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -2134,6 +2138,22 @@ register_model_group(
 )
 
 
+register_model_group(
+    models={
+        "Qwen2-Audio-7B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-Audio-7B",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-Audio-7B",
+        },
+        "Qwen2-Audio-7B-Instruct": {
+            DownloadSource.DEFAULT: "Qwen/Qwen2-Audio-7B-Instruct",
+            DownloadSource.MODELSCOPE: "Qwen/Qwen2-Audio-7B-Instruct",
+        },
+    },
+    template="qwen2_audio",
+    multimodal=True,
+)
+
+
 register_model_group(
     models={
         "Qwen2-VL-2B-Instruct": {
@@ -2204,7 +2224,7 @@ register_model_group(
         },
     },
     template="qwen2_vl",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -2329,7 +2349,7 @@ register_model_group(
         },
     },
     template="video_llava",
-    vision=True,
+    multimodal=True,
 )
 
 
@@ -2556,7 +2576,7 @@ register_model_group(
         },
     },
     template="yi_vl",
-    vision=True,
+    multimodal=True,
 )
 
 
diff --git a/src/llamafactory/extras/packages.py b/src/llamafactory/extras/packages.py
index 5516d6f6..62a3615a 100644
--- a/src/llamafactory/extras/packages.py
+++ b/src/llamafactory/extras/packages.py
@@ -42,6 +42,10 @@ def is_pyav_available():
     return _is_package_available("av")
 
 
+def is_librosa_available():
+    return _is_package_available("librosa")
+
+
 def is_fastapi_available():
     return _is_package_available("fastapi")
 
diff --git a/src/llamafactory/hparams/data_args.py b/src/llamafactory/hparams/data_args.py
index b841ec52..cd28f580 100644
--- a/src/llamafactory/hparams/data_args.py
+++ b/src/llamafactory/hparams/data_args.py
@@ -41,9 +41,9 @@ class DataArguments:
         default="data",
         metadata={"help": "Path to the folder containing the datasets."},
     )
-    image_dir: Optional[str] = field(
+    media_dir: Optional[str] = field(
         default=None,
-        metadata={"help": "Path to the folder containing the images or videos. Defaults to `dataset_dir`."},
+        metadata={"help": "Path to the folder containing the images, videos or audios. Defaults to `dataset_dir`."},
     )
     cutoff_len: int = field(
         default=2048,
@@ -133,8 +133,8 @@ class DataArguments:
         self.dataset = split_arg(self.dataset)
         self.eval_dataset = split_arg(self.eval_dataset)
 
-        if self.image_dir is None:
-            self.image_dir = self.dataset_dir
+        if self.media_dir is None:
+            self.media_dir = self.dataset_dir
 
         if self.dataset is None and self.val_size > 1e-6:
             raise ValueError("Cannot specify `val_size` if `dataset` is None.")
diff --git a/src/llamafactory/model/loader.py b/src/llamafactory/model/loader.py
index 19e57099..45cc1fa5 100644
--- a/src/llamafactory/model/loader.py
+++ b/src/llamafactory/model/loader.py
@@ -16,7 +16,14 @@ import os
 from typing import TYPE_CHECKING, Any, Dict, Optional, TypedDict
 
 import torch
-from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForVision2Seq, AutoProcessor, AutoTokenizer
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    AutoModelForSeq2SeqLM,
+    AutoModelForVision2Seq,
+    AutoProcessor,
+    AutoTokenizer,
+)
 from trl import AutoModelForCausalLMWithValueHead
 
 from ..extras import logging
@@ -142,6 +149,8 @@ def load_model(
         else:
             if type(config) in AutoModelForVision2Seq._model_mapping.keys():  # assume built-in models
                 load_class = AutoModelForVision2Seq
+            elif type(config) in AutoModelForSeq2SeqLM._model_mapping.keys():
+                load_class = AutoModelForSeq2SeqLM
             else:
                 load_class = AutoModelForCausalLM
 
diff --git a/src/llamafactory/model/model_utils/visual.py b/src/llamafactory/model/model_utils/visual.py
index 10eafff6..57b6a31a 100644
--- a/src/llamafactory/model/model_utils/visual.py
+++ b/src/llamafactory/model/model_utils/visual.py
@@ -280,6 +280,12 @@ _register_composite_model(
 )
 
 
+_register_composite_model(
+    model_type="qwen2_audio",
+    vision_model_keys=["audio_tower"],
+)
+
+
 _register_composite_model(
     model_type="qwen2_vl",
     projector_key="visual.merger",
diff --git a/src/llamafactory/model/patcher.py b/src/llamafactory/model/patcher.py
index aab2052d..414bda07 100644
--- a/src/llamafactory/model/patcher.py
+++ b/src/llamafactory/model/patcher.py
@@ -78,13 +78,14 @@ def patch_processor(
     model_args: "ModelArguments",
 ) -> None:
     setattr(processor, "tokenizer", tokenizer)
-    setattr(processor, "image_seqlen", get_image_seqlen(config))
-    setattr(processor, "image_resolution", model_args.image_resolution)
-    setattr(processor, "patch_size", get_patch_size(config, processor))
-    setattr(processor, "video_resolution", model_args.video_resolution)
-    setattr(processor, "video_fps", model_args.video_fps)
-    setattr(processor, "video_maxlen", model_args.video_maxlen)
-    setattr(processor, "vision_feature_select_strategy", get_vision_feature_select_strategy(config, processor))
+    if getattr(config, "vision_config", None) is not None:  # visual models
+        setattr(processor, "image_seqlen", get_image_seqlen(config))
+        setattr(processor, "image_resolution", model_args.image_resolution)
+        setattr(processor, "patch_size", get_patch_size(config, processor))
+        setattr(processor, "video_resolution", model_args.video_resolution)
+        setattr(processor, "video_fps", model_args.video_fps)
+        setattr(processor, "video_maxlen", model_args.video_maxlen)
+        setattr(processor, "vision_feature_select_strategy", get_vision_feature_select_strategy(config, processor))
 
 
 def patch_config(
diff --git a/src/llamafactory/webui/chatter.py b/src/llamafactory/webui/chatter.py
index 39602947..7863dee3 100644
--- a/src/llamafactory/webui/chatter.py
+++ b/src/llamafactory/webui/chatter.py
@@ -172,6 +172,7 @@ class WebChatModel(ChatModel):
         tools: str,
         image: Optional[Any],
         video: Optional[Any],
+        audio: Optional[Any],
         max_new_tokens: int,
         top_p: float,
         temperature: float,
@@ -190,6 +191,7 @@ class WebChatModel(ChatModel):
             tools,
             images=[image] if image else None,
             videos=[video] if video else None,
+            audios=[audio] if audio else None,
             max_new_tokens=max_new_tokens,
             top_p=top_p,
             temperature=temperature,
diff --git a/src/llamafactory/webui/common.py b/src/llamafactory/webui/common.py
index dc30196a..f8349714 100644
--- a/src/llamafactory/webui/common.py
+++ b/src/llamafactory/webui/common.py
@@ -26,9 +26,9 @@ from ..extras import logging
 from ..extras.constants import (
     DATA_CONFIG,
     DEFAULT_TEMPLATE,
+    MULTIMODAL_SUPPORTED_MODELS,
     SUPPORTED_MODELS,
     TRAINING_ARGS,
-    VISION_MODELS,
     DownloadSource,
 )
 from ..extras.misc import use_modelscope, use_openmind
@@ -136,13 +136,6 @@ def get_template(model_name: str) -> str:
     return DEFAULT_TEMPLATE.get(model_name, "default")
 
 
-def get_visual(model_name: str) -> bool:
-    r"""
-    Judges if the model is a vision language model.
-    """
-    return model_name in VISION_MODELS
-
-
 def get_time() -> str:
     r"""
     Gets current date and time.
@@ -150,6 +143,13 @@ def get_time() -> str:
     return datetime.now().strftime(r"%Y-%m-%d-%H-%M-%S")
 
 
+def is_multimodal(model_name: str) -> bool:
+    r"""
+    Judges if the model is a vision language model.
+    """
+    return model_name in MULTIMODAL_SUPPORTED_MODELS
+
+
 def load_dataset_info(dataset_dir: str) -> Dict[str, Dict[str, Any]]:
     r"""
     Loads dataset_info.json.
diff --git a/src/llamafactory/webui/components/chatbot.py b/src/llamafactory/webui/components/chatbot.py
index 5c2e7586..05cc4a1f 100644
--- a/src/llamafactory/webui/components/chatbot.py
+++ b/src/llamafactory/webui/components/chatbot.py
@@ -64,10 +64,13 @@ def create_chat_box(
 
                     with gr.Column() as mm_box:
                         with gr.Tab("Image"):
-                            image = gr.Image(sources=["upload"], type="pil")
+                            image = gr.Image(type="pil")
 
                         with gr.Tab("Video"):
-                            video = gr.Video(sources=["upload"])
+                            video = gr.Video()
+
+                        with gr.Tab("Audio"):
+                            audio = gr.Audio(type="filepath")
 
                 query = gr.Textbox(show_label=False, lines=8)
                 submit_btn = gr.Button(variant="primary")
@@ -86,7 +89,7 @@ def create_chat_box(
         [chatbot, messages, query],
     ).then(
         engine.chatter.stream,
-        [chatbot, messages, lang, system, tools, image, video, max_new_tokens, top_p, temperature],
+        [chatbot, messages, lang, system, tools, image, video, audio, max_new_tokens, top_p, temperature],
         [chatbot, messages],
     )
     clear_btn.click(lambda: ([], []), outputs=[chatbot, messages])
@@ -102,6 +105,7 @@ def create_chat_box(
             mm_box=mm_box,
             image=image,
             video=video,
+            audio=audio,
             query=query,
             submit_btn=submit_btn,
             max_new_tokens=max_new_tokens,
diff --git a/src/llamafactory/webui/components/infer.py b/src/llamafactory/webui/components/infer.py
index c2d63ee6..48536a36 100644
--- a/src/llamafactory/webui/components/infer.py
+++ b/src/llamafactory/webui/components/infer.py
@@ -15,7 +15,7 @@
 from typing import TYPE_CHECKING, Dict
 
 from ...extras.packages import is_gradio_available
-from ..common import get_visual
+from ..common import is_multimodal
 from .chatbot import create_chat_box
 
 
@@ -66,7 +66,7 @@ def create_infer_tab(engine: "Engine") -> Dict[str, "Component"]:
     ).then(lambda: gr.Column(visible=engine.chatter.loaded), outputs=[chat_elems["chat_box"]])
 
     engine.manager.get_elem_by_id("top.model_name").change(
-        lambda model_name: gr.Column(visible=get_visual(model_name)),
+        lambda model_name: gr.Column(visible=is_multimodal(model_name)),
         [engine.manager.get_elem_by_id("top.model_name")],
         [chat_elems["mm_box"]],
     )
diff --git a/tests/data/test_mm_plugin.py b/tests/data/test_mm_plugin.py
index 7dce8118..5e0ec660 100644
--- a/tests/data/test_mm_plugin.py
+++ b/tests/data/test_mm_plugin.py
@@ -52,12 +52,16 @@ NO_IMAGES = []
 
 NO_VIDEOS = []
 
+NO_AUDIOS = []
+
 IMGLENS = [1]
 
 NO_IMGLENS = [0]
 
 NO_VIDLENS = [0]
 
+NO_AUDLENS = [0]
+
 INPUT_IDS = [0, 1, 2, 3, 4]
 
 LABELS = [0, 1, 2, 3, 4]
@@ -99,23 +103,25 @@ def _check_plugin(
     expected_no_mm_inputs: Dict[str, Any] = {},
 ) -> None:
     # test mm_messages
-    assert plugin.process_messages(MM_MESSAGES, IMAGES, NO_VIDEOS, processor) == expected_mm_messages
-    assert plugin.process_token_ids(INPUT_IDS, LABELS, IMAGES, NO_VIDEOS, tokenizer, processor) == (
+    assert plugin.process_messages(MM_MESSAGES, IMAGES, NO_VIDEOS, NO_AUDIOS, processor) == expected_mm_messages
+    assert plugin.process_token_ids(INPUT_IDS, LABELS, IMAGES, NO_VIDEOS, NO_AUDIOS, tokenizer, processor) == (
         expected_input_ids,
         expected_labels,
     )
     _is_close(
-        plugin.get_mm_inputs(IMAGES, NO_VIDEOS, IMGLENS, NO_VIDLENS, BATCH_IDS, processor),
+        plugin.get_mm_inputs(IMAGES, NO_VIDEOS, NO_AUDIOS, IMGLENS, NO_VIDLENS, NO_AUDLENS, BATCH_IDS, processor),
         expected_mm_inputs,
     )
     # test text_messages
-    assert plugin.process_messages(TEXT_MESSAGES, NO_IMAGES, NO_VIDEOS, processor) == TEXT_MESSAGES
-    assert plugin.process_token_ids(INPUT_IDS, LABELS, NO_IMAGES, NO_VIDEOS, tokenizer, processor) == (
+    assert plugin.process_messages(TEXT_MESSAGES, NO_IMAGES, NO_VIDEOS, NO_AUDIOS, processor) == TEXT_MESSAGES
+    assert plugin.process_token_ids(INPUT_IDS, LABELS, NO_IMAGES, NO_VIDEOS, NO_AUDIOS, tokenizer, processor) == (
         INPUT_IDS,
         LABELS,
     )
     _is_close(
-        plugin.get_mm_inputs(NO_IMAGES, NO_VIDEOS, NO_IMGLENS, NO_VIDLENS, BATCH_IDS, processor),
+        plugin.get_mm_inputs(
+            NO_IMAGES, NO_VIDEOS, NO_AUDIOS, NO_IMGLENS, NO_VIDLENS, NO_AUDLENS, BATCH_IDS, processor
+        ),
         expected_no_mm_inputs,
     )
 
diff --git a/tests/data/test_template.py b/tests/data/test_template.py
index d6eaeda3..dc57c7e0 100644
--- a/tests/data/test_template.py
+++ b/tests/data/test_template.py
@@ -167,7 +167,7 @@ def test_phi4_template(use_fast: bool):
     _check_template("microsoft/phi-4", "phi4", prompt_str, answer_str, use_fast)
 
 
-@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
+@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")  # TODO: why it is gated?
 @pytest.mark.parametrize("use_fast", [True, False])
 def test_qwen_template(use_fast: bool):
     prompt_str = (