handler.py

from typing import  Dict
from transformers.pipelines.audio_utils import ffmpeg_read
import torch
import pyewts
from transformers import pipeline
converter = pyewts.pyewts()

SAMPLE_RATE = 16000



class EndpointHandler():
    def __init__(self, path=""):
        # load the model
        self.pipe = pipeline(model="TenzinGayche/whisper-small-3",device='cuda')


    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)
        text = pipe(audio_tensor)["text"]
        
        # run inference pipeline
        result = converter.toUnicode(text)

        # postprocess the prediction
        return {"text": result}