Create handler.py

handler.py

@@ -0,0 +1,69 @@
+from typing import Dict, List, Any
+import json
+import numpy as np
+from transformers import AutoProcessor, MusicgenForConditionalGeneration
+import torch
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load model and processor from path
+        self.processor = AutoProcessor.from_pretrained(path)
+        
+        # Check if CUDA is available, and set the device accordingly
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        
+        # Load the model to the device
+        self.model = MusicgenForConditionalGeneration.from_pretrained(path)
+        self.model.to(self.device)  # Correcting this line
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
+        """
+        Args:
+            data (:dict:):
+                The payload with the text prompt and generation parameters.
+        """
+        # process input
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None)
+        duration = parameters.pop("duration", None)
+        audio = parameters.pop("audio", None)
+        sampling_rate = parameters.pop("sampling_rate", None)
+
+        if audio is not None:
+            audio_list = json.loads(audio)
+            audio_array = np.array(audio_list)
+            audio = audio_array
+
+        if duration is not None:
+            # Calculate max new tokens based on duration, this is a placeholder, replace with actual logic
+            max_new_tokens = int(duration * 50)  
+        else:
+            max_new_tokens = 256  # Default value if duration is not provided
+
+        # preprocess
+        inputs = self.processor(
+            text=[inputs],
+            padding=True,
+            return_tensors="pt",
+            audio=audio,
+            sampling_rate=sampling_rate).to(self.device)
+
+        # If 'duration' is inside 'parameters', remove it
+        if parameters is not None and 'duration' in parameters:
+            parameters.pop('duration')
+        if parameters is not None and 'audio' in parameters:
+            parameters.pop('audio')
+        if parameters is not None and 'sampling_rate' in parameters:
+            parameters.pop('sampling_rate')
+
+        # pass inputs with all kwargs in data
+        if parameters is not None:
+            outputs = self.model.generate(**inputs, max_new_tokens=max_new_tokens, **parameters)
+        else:
+            outputs = self.model.generate(**inputs, max_new_tokens=max_new_tokens)
+
+        # postprocess the prediction
+        prediction = outputs[0].cpu().numpy()
+
+        return [{"generated_text": prediction}]