handler.py

from transformers import pipeline
from typing import Any

class EndpointHandler():
    def __init__(self, path=""):
        # create inference pipeline
        self.pipeline = pipeline("text-to-speech", model=path, device=0)

    def __call__(self, data: Any) -> Any:
        inputs = data.pop("inputs", data)
        parameters = data.pop("parameters", None)

        # pass inputs with all kwargs in data
        if parameters is not None:
            prediction = self.pipeline(inputs, **parameters)
        else:
            prediction = self.pipeline(inputs)
        
        # postprocess the prediction
        audio_array = prediction['audio']
        sampling_rate = prediction['sampling_rate']
        
        # If you need to return raw audio data
        return {
            "audio": audio_array,
            "sampling_rate": sampling_rate
        }