Create README.md

README.md

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+```python
+import torch
+from transformers import AutoModel
+
+audio_path = ['resources/1.wav', 'resources/2.wav']  # (B,)
+caption = ["A woman speaks, dishes clanking, food frying, and music plays", 'A power tool is heard with male speech.']  # (B,)
+phrases = ['Speech', 'Dog', 'Cat', 'Frying', 'Dishes', 'Music', 'Vacuum', 'Type', 'Power tool']  # (N,)
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model = AutoModel.from_pretrained("AndreasXi/FineLAP", trust_remote_code=True).to(device)
+model.eval()
+
+with torch.no_grad():
+    global_text_embeds = model.get_global_text_embeds(caption)  # (B, d)
+    print(global_text_embeds.shape)
+
+    global_audio_embeds = model.get_global_audio_embeds(audio_path)   # (B, d)
+    print(global_audio_embeds.shape)
+
+    dense_audio_embeds = model.get_dense_audio_embeds(audio_path)  # (B, T, d)
+    print(dense_audio_embeds.shape)
+
+    clip_scores = model.get_clip_level_score(audio_path, caption)  # (B, B)
+    print(clip_scores.shape)
+
+    frame_scores = model.get_frame_level_score(audio_path, phrases)  # (B, N, T)
+    print(frame_scores.shape)
+    
+    ## (Optional) Plot frame-level similarity, only supprt single audio file
+    model.plot_frame_level_score(audio_path[1], phrases, output_path="output/output_plot.png")
+```