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@@ -121,6 +121,28 @@ similarities = torch.einsum("btd,bd->bt", audio_embeds, text_embeds)
 # similarities shape: [batch_size, num_frames]
 ```
 
+
+### Usage with 🤗 Transformers
+
+```python
+model = PeAudioFrameLevelModel.from_pretrained("facebook/pe-a-frame-large")
+processor = PeAudioProcessor.from_pretrained("facebook/pe-a-frame-large")
+
+inputs = transform(audio=[audio_file], text=descriptions, return_tensors="pt").to(device)
+
+with torch.inference_mode():
+    outputs = model(**inputs)
+
+# Access embeddings
+audio_embeds = outputs.audio_embeds  # Shape: [batch_size, num_frames, embed_dim]
+text_embeds = outputs.text_audio_embeds    # Shape: [batch_size, embed_dim]
+
+# Compute similarity between audio frames and text
+# audio_embeds is frame-level, so you can see which frames match the description
+similarities = torch.einsum("btd,bd->bt", audio_embeds, text_embeds)
+# similarities shape: [batch_size, num_frames]
+```
+
 ## Citation
 
 ```bibtex