Delete handler.py

handler.py

@@ -1,33 +0,0 @@
-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("medium")
-
-
-    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"]}