Create handler.py

handler.py

@@ -0,0 +1,61 @@
+# handler.py
+from typing import Dict, Any
+from transformers import AutoProcessor, MusicgenForConditionalGeneration
+import torch
+import numpy as np
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # Load model and processor from path
+        self.processor = AutoProcessor.from_pretrained(path)
+        self.model = MusicgenForConditionalGeneration.from_pretrained(
+            path, 
+            torch_dtype=torch.float16
+        ).to("cuda")
+        
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Args:
+            data (Dict): The request data, containing:
+                - inputs (Dict): Contains 'prompt' and optional 'duration'
+                - parameters (Dict, optional): Generation parameters
+        """
+        # Extract inputs and parameters
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", {})
+        
+        # Get prompt and duration
+        prompt = inputs.get("prompt", "")
+        duration = inputs.get("duration", 30)  # Default 30 seconds
+        
+        # Calculate max_new_tokens based on duration
+        # MusicGen generates audio at 32000 Hz, with each token representing 1024 samples
+        samples_per_token = 1024
+        sampling_rate = 32000
+        max_new_tokens = int((duration * sampling_rate) / samples_per_token)
+        
+        # Process input text
+        inputs = self.processor(
+            text=[prompt],
+            padding=True,
+            return_tensors="pt"
+        ).to("cuda")
+        
+        # Set default generation parameters
+        generation_params = {
+            "do_sample": True,
+            "guidance_scale": 3,
+            "max_new_tokens": max_new_tokens
+        }
+        
+        # Update with any user-provided parameters
+        generation_params.update(parameters)
+        
+        # Generate audio
+        with torch.cuda.amp.autocast():
+            outputs = self.model.generate(**inputs, **generation_params)
+            
+        # Convert to list for JSON serialization
+        generated_audio = outputs.cpu().numpy().tolist()
+        
+        return [{"generated_audio": generated_audio}]