Create handler.py

handler.py

@@ -0,0 +1,33 @@
+from typing import  Dict
+from transformers.pipelines.audio_utils import ffmpeg_read
+import whisper
+import torch
+
+SAMPLE_RATE = 16000
+
+
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # load the model
+        self.model = whisper.load_model("large")
+
+
+    def __call__(self, data: Dict[str, bytes]) -> Dict[str, str]:
+        """
+        Args:
+            data (:obj:):
+                includes the deserialized audio file as bytes
+        Return:
+            A :obj:`dict`:. base64 encoded image
+        """
+        # process input
+        inputs = data.pop("inputs", data)
+        audio_nparray = ffmpeg_read(inputs, SAMPLE_RATE)
+        audio_tensor= torch.from_numpy(audio_nparray)
+        
+        # run inference pipeline
+        result = self.model.transcribe(audio_nparray)
+
+        # postprocess the prediction
+        return {"text": result["text"]}