diff --git a/examples/wanvideo/model_inference/Wan2.2-S2V-14B.py b/examples/wanvideo/model_inference/Wan2.2-S2V-14B.py
new file mode 100644
index 0000000..019a89d
--- /dev/null
+++ b/examples/wanvideo/model_inference/Wan2.2-S2V-14B.py
@@ -0,0 +1,38 @@
+import torch
+from PIL import Image
+import librosa
+from diffsynth import save_video, VideoData, save_video_with_audio
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="Wan2.1_VAE.pth"),
+    ],
+    audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"),
+)
+
+prompt = "a person is singing"
+input_image = Image.open("/mnt/nas1/zhanghong/project/aigc/Wan2.2_s2v/examples/pose.png").convert("RGB").resize((width, height))
+# s2v audio input, recommend 16kHz sampling rate
+audio_path = '/mnt/nas1/zhanghong/project/aigc/Wan2.2_s2v/examples/sing.MP3'
+input_audio, sample_rate = librosa.load(audio_path, sr=16000)
+
+# Speech-to-video
+video = pipe(
+    prompt=prompt,
+    input_image=input_image,
+    negative_prompt="",
+    seed=0,
+    num_frames=81,
+    height=1280,
+    width=720,
+    audio_sample_rate=sample_rate,
+    input_audio=input_audio,
+    num_inference_steps=40,
+)
+save_video_with_audio(video, "video_with_audio.mp4", audio_path, fps=16, quality=5)