Add: handler

handler.py

@@ -0,0 +1,43 @@
+from typing import Any, Dict
+
+import torch
+from diffusers import AudioLDM2Pipeline, DPMSolverMultistepScheduler
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load model and processor from path
+        self.pipeline = AudioLDM2Pipeline.from_pretrained(
+            "cvssp/audioldm2-music", torch_dtype=torch.float16
+        ).to("cuda")
+        self.pipeline.unet = torch.compile(
+            self.pipeline.unet, mode="reduce-overhead", fullgraph=True
+        )
+        self.pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
+            self.pipeline.scheduler.config
+        )
+        self.pipeline.enable_model_cpu_offload()
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
+        """
+        Args:
+            data (:dict:):
+                The payload with the text prompt and generation parameters.
+        """
+        # process input
+        song_description = data.pop("inputs", data)
+        duration = data.get("duration", 30)
+        negative_prompt = data.get("negative_prompt", "Low quality, average quality.")
+
+        audio = self.pipeline(
+            song_description,
+            negative_prompt=negative_prompt,
+            num_waveforms_per_prompt=4,
+            audio_length_in_s=duration,
+            num_inference_steps=20,
+        ).audios[0]
+
+        # postprocess the prediction
+        prediction = audio.tolist()
+
+        return [{"generated_audio": prediction}]