diff --git a/README.md b/README.md
index f88fd4e..525ed80 100644
--- a/README.md
+++ b/README.md
@@ -718,172 +718,57 @@ jupyter notebook cookbooks/demo.ipynb
 
 We also provide a simple example on the usage of this repo. For detailed usage, please refer to [demobook.ipynb](https://github.com/inclusionAI/Ming-UniAudio/blob/main/cookbooks/demo.ipynb).
 
-```python
-import warnings
-import torch
-from transformers import AutoProcessor
-
-from modeling_bailingmm import BailingMMNativeForConditionalGeneration
+The MingAudio class is available in `ming_audio.py`. You can use it as follows:
 
-import random
-import numpy as np
+```python
+from ming_audio import MingAudio
 from loguru import logger
 
-def seed_everything(seed=1895):
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = False
-
-seed_everything()
-warnings.filterwarnings("ignore")
-
-class MingAudio:
-    def __init__(self, model_path, device="cuda:0"):
-        self.device = device
-        self.model = BailingMMNativeForConditionalGeneration.from_pretrained(
-            model_path,
-            torch_dtype=torch.bfloat16,
-            low_cpu_mem_usage=True,
-        ).eval().to(torch.bfloat16).to(self.device)
-        self.processor = AutoProcessor.from_pretrained(".", trust_remote_code=True)
-        self.tokenizer = self.processor.tokenizer
-        self.sample_rate = self.processor.audio_processor.sample_rate
-        self.patch_size = self.processor.audio_processor.patch_size
-        
-    def speech_understanding(self, messages):
-        text = self.processor.apply_chat_template(messages, add_generation_prompt=True)
-        image_inputs, video_inputs, audio_inputs = self.processor.process_vision_info(messages)
-
-        inputs = self.processor(
-            text=[text],
-            images=image_inputs,
-            videos=video_inputs,
-            audios=audio_inputs,
-            return_tensors="pt",
-        ).to(self.device)
-
-        for k in inputs.keys():
-            if k == "pixel_values" or k == "pixel_values_videos" or k == "audio_feats":
-                inputs[k] = inputs[k].to(dtype=torch.bfloat16)
-        logger.info(f"input: {self.tokenizer.decode(inputs['input_ids'].cpu().numpy().tolist()[0])}")
-
-        generated_ids = self.model.generate(
-            **inputs,
-            max_new_tokens=512,
-            eos_token_id=self.processor.gen_terminator,
-        )
-        generated_ids_trimmed = [
-            out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-        ]
-        output_text = self.processor.batch_decode(
-            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-        )[0]
-
-        return output_text
-
-    def speech_generation(
-        self, 
-        text,
-        prompt_wav_path,
-        prompt_text,
-        lang='zh',
-        output_wav_path='out.wav'
-    ):
-        waveform = self.model.generate_tts(
-            text=text,
-            prompt_wav_path=prompt_wav_path,
-            prompt_text=prompt_text,
-            patch_size=self.patch_size,
-            tokenizer=self.tokenizer,
-            lang=lang,
-            output_wav_path=output_wav_path,
-            sample_rate=self.sample_rate,
-            device=self.device
-        )
-        
-        return waveform
-
-    def speech_edit(
-        self, 
-        messages,
-        output_wav_path='out.wav'
-    ):
-        text = self.processor.apply_chat_template(messages, add_generation_prompt=True)
-        image_inputs, video_inputs, audio_inputs = self.processor.process_vision_info(messages)
-
-        inputs = self.processor(
-            text=[text],
-            images=image_inputs,
-            videos=video_inputs,
-            audios=audio_inputs,
-            return_tensors="pt",
-        ).to(self.device)
-
-        ans = torch.tensor([self.tokenizer.encode('<answer>')]).to(inputs['input_ids'].device)
-        inputs['input_ids'] = torch.cat([inputs['input_ids'], ans], dim=1)
-        attention_mask = inputs['attention_mask']
-        inputs['attention_mask'] = torch.cat((attention_mask, attention_mask[:, :1]), dim=-1)
-        for k in inputs.keys():
-            if k == "pixel_values" or k == "pixel_values_videos" or k == "audio_feats":
-                inputs[k] = inputs[k].to(dtype=torch.bfloat16)
-        logger.info(f"input: {self.tokenizer.decode(inputs['input_ids'].cpu().numpy().tolist()[0])}")
-
-        edited_speech, edited_text = self.model.generate_edit(
-            **inputs,
-            tokenizer=self.tokenizer,
-            output_wav_path=output_wav_path
-        )
-        return edited_speech, edited_text
-
-if __name__ == "__main__":
-    model = MingAudio("inclusionAI/Ming-UniAudio-16B-A3B")
-    
-    # ASR
-    messages = [
-        {
-            "role": "HUMAN",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "Please recognize the language of this speech and transcribe it. Format: oral.",
-                },
-                
-                {"type": "audio", "audio": "data/wavs/BAC009S0915W0292.wav"},
-            ],
-        },
-    ]
-    
-    response = model.speech_understanding(messages=messages)
-    logger.info(f"Generated Response: {response}")
-
-    # TTS
-    model.speech_generation(
-        text='我们的愿景是构建未来服务业的数字化基础设施，为世界带来更多微小而美好的改变。',
-        prompt_wav_path='data/wavs/10002287-00000094.wav',
-        prompt_text='在此奉劝大家别乱打美白针。',
-    )
-
-    # Edit
-    # model = MingAudio("inclusionAI/Ming-UniAudio-16B-A3B-Edit")
-    messages = [
-        {
-            "role": "HUMAN",
-            "content": [
-                {"type": "audio", "audio": "data/wavs/00004768-00000024.wav", "target_sample_rate": 16000},
-                {
-                    "type": "text",
-                    "text": "<prompt>Please recognize the language of this speech and transcribe it. And insert '实现' before the character or word at index 3.\n</prompt>",
-                },
-            ],
-        },
-    ]
-    
-    response = model.speech_edit(messages=messages)
-    logger.info(f"Generated Response: {response}")
+# Initialize model
+model = MingAudio("inclusionAI/Ming-UniAudio-16B-A3B")
+
+# ASR
+messages = [
+    {
+        "role": "HUMAN",
+        "content": [
+            {
+                "type": "text",
+                "text": "Please recognize the language of this speech and transcribe it. Format: oral.",
+            },
+
+            {"type": "audio", "audio": "data/wavs/BAC009S0915W0292.wav"},
+        ],
+    },
+]
+
+response = model.speech_understanding(messages=messages)
+logger.info(f"Generated Response: {response}")
+
+# TTS
+model.speech_generation(
+    text='我们的愿景是构建未来服务业的数字化基础设施，为世界带来更多微小而美好的改变。',
+    prompt_wav_path='data/wavs/10002287-00000094.wav',
+    prompt_text='在此奉劝大家别乱打美白针。',
+)
+
+# Edit
+# model = MingAudio("inclusionAI/Ming-UniAudio-16B-A3B-Edit")
+messages = [
+    {
+        "role": "HUMAN",
+        "content": [
+            {"type": "audio", "audio": "data/wavs/00004768-00000024.wav", "target_sample_rate": 16000},
+            {
+                "type": "text",
+                "text": "<prompt>Please recognize the language of this speech and transcribe it. And insert '实现' before the character or word at index 3.\n</prompt>",
+            },
+        ],
+    },
+]
+
+response = model.speech_edit(messages=messages)
+logger.info(f"Generated Response: {response}")
 ```
 
 Note: We test the examples on hardware of NVIDIA H800-80GB/H20-96G with CUDA 12.4.
diff --git a/ming_audio.py b/ming_audio.py
new file mode 100644
index 0000000..5b18f0b
--- /dev/null
+++ b/ming_audio.py
@@ -0,0 +1,165 @@
+import warnings
+import torch
+from transformers import AutoProcessor
+
+from modeling_bailingmm import BailingMMNativeForConditionalGeneration
+
+import random
+import numpy as np
+from loguru import logger
+
+def seed_everything(seed=1895):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+seed_everything()
+warnings.filterwarnings("ignore")
+
+class MingAudio:
+    def __init__(self, model_path, device="cuda:0"):
+        self.device = device
+        self.model = BailingMMNativeForConditionalGeneration.from_pretrained(
+            model_path,
+            torch_dtype=torch.bfloat16,
+            low_cpu_mem_usage=True,
+        ).eval().to(torch.bfloat16).to(self.device)
+        self.processor = AutoProcessor.from_pretrained(".", trust_remote_code=True)
+        self.tokenizer = self.processor.tokenizer
+        self.sample_rate = self.processor.audio_processor.sample_rate
+        self.patch_size = self.processor.audio_processor.patch_size
+
+    def speech_understanding(self, messages):
+        text = self.processor.apply_chat_template(messages, add_generation_prompt=True)
+        image_inputs, video_inputs, audio_inputs = self.processor.process_vision_info(messages)
+
+        inputs = self.processor(
+            text=[text],
+            images=image_inputs,
+            videos=video_inputs,
+            audios=audio_inputs,
+            return_tensors="pt",
+        ).to(self.device)
+
+        for k in inputs.keys():
+            if k == "pixel_values" or k == "pixel_values_videos" or k == "audio_feats":
+                inputs[k] = inputs[k].to(dtype=torch.bfloat16)
+        logger.info(f"input: {self.tokenizer.decode(inputs['input_ids'].cpu().numpy().tolist()[0])}")
+
+        generated_ids = self.model.generate(
+            **inputs,
+            max_new_tokens=512,
+            eos_token_id=self.processor.gen_terminator,
+        )
+        generated_ids_trimmed = [
+            out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        ]
+        output_text = self.processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )[0]
+
+        return output_text
+
+    def speech_generation(
+        self,
+        text,
+        prompt_wav_path,
+        prompt_text,
+        lang='zh',
+        output_wav_path='out.wav'
+    ):
+        waveform = self.model.generate_tts(
+            text=text,
+            prompt_wav_path=prompt_wav_path,
+            prompt_text=prompt_text,
+            patch_size=self.patch_size,
+            tokenizer=self.tokenizer,
+            lang=lang,
+            output_wav_path=output_wav_path,
+            sample_rate=self.sample_rate,
+            device=self.device
+        )
+
+        return waveform
+
+    def speech_edit(
+        self,
+        messages,
+        output_wav_path='out.wav'
+    ):
+        text = self.processor.apply_chat_template(messages, add_generation_prompt=True)
+        image_inputs, video_inputs, audio_inputs = self.processor.process_vision_info(messages)
+
+        inputs = self.processor(
+            text=[text],
+            images=image_inputs,
+            videos=video_inputs,
+            audios=audio_inputs,
+            return_tensors="pt",
+        ).to(self.device)
+
+        ans = torch.tensor([self.tokenizer.encode('<answer>')]).to(inputs['input_ids'].device)
+        inputs['input_ids'] = torch.cat([inputs['input_ids'], ans], dim=1)
+        attention_mask = inputs['attention_mask']
+        inputs['attention_mask'] = torch.cat((attention_mask, attention_mask[:, :1]), dim=-1)
+        for k in inputs.keys():
+            if k == "pixel_values" or k == "pixel_values_videos" or k == "audio_feats":
+                inputs[k] = inputs[k].to(dtype=torch.bfloat16)
+        logger.info(f"input: {self.tokenizer.decode(inputs['input_ids'].cpu().numpy().tolist()[0])}")
+
+        edited_speech, edited_text = self.model.generate_edit(
+            **inputs,
+            tokenizer=self.tokenizer,
+            output_wav_path=output_wav_path
+        )
+        return edited_speech, edited_text
+
+if __name__ == "__main__":
+    model = MingAudio("inclusionAI/Ming-UniAudio-16B-A3B")
+
+    # ASR
+    messages = [
+        {
+            "role": "HUMAN",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "Please recognize the language of this speech and transcribe it. Format: oral.",
+                },
+
+                {"type": "audio", "audio": "data/wavs/BAC009S0915W0292.wav"},
+            ],
+        },
+    ]
+
+    response = model.speech_understanding(messages=messages)
+    logger.info(f"Generated Response: {response}")
+
+    # TTS
+    model.speech_generation(
+        text='我们的愿景是构建未来服务业的数字化基础设施，为世界带来更多微小而美好的改变。',
+        prompt_wav_path='data/wavs/10002287-00000094.wav',
+        prompt_text='在此奉劝大家别乱打美白针。',
+    )
+
+    # Edit
+    # model = MingAudio("inclusionAI/Ming-UniAudio-16B-A3B-Edit")
+    messages = [
+        {
+            "role": "HUMAN",
+            "content": [
+                {"type": "audio", "audio": "data/wavs/00004768-00000024.wav", "target_sample_rate": 16000},
+                {
+                    "type": "text",
+                    "text": "<prompt>Please recognize the language of this speech and transcribe it. And insert '实现' before the character or word at index 3.\n</prompt>",
+                },
+            ],
+        },
+    ]
+
+    response = model.speech_edit(messages=messages)
+    logger.info(f"Generated Response: {response}")
\ No newline at end of file