add custom handler

handler.py

@@ -0,0 +1,26 @@
+from typing import Dict, List, Any
+from transformers import pipeline, WhisperProcessor, WhisperForConditionalGeneration
+import torch
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        device = 0 if torch.cuda.is_available() else "cpu"
+        self.pipe = pipeline(
+            task="automatic-speech-recognition",
+            model=path,
+            chunk_length_s=30,
+            device=device,
+        )
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            inputs (:obj: `str` | `PIL.Image` | `np.array`)
+            kwargs
+      Return:
+            A :obj:`list` | `dict`: will be serialized and returned
+        """
+        inputs = data.pop("inputs",data)
+        print("inputs", inputs)
+        prediction = self.pipe(inputs, return_timestamps=True)
+        return prediction

test.py

@@ -0,0 +1,10 @@
+from handler import EndpointHandler
+from datasets import load_dataset
+
+# init handler
+my_handler = EndpointHandler(path=".")
+
+# print(type(sample["data"]))
+prediction = my_handler({"inputs": "/Users/lesliejones-dove/Desktop/projects/Whisper/whisperx/whisperCustomHandler/Saffored_short.wav"})
+# /Users/lesliejones-dove/Desktop/projects/Whisper/whisperx/whisperCustomHandler
+print(prediction)