handler.py

from huggingface_hub import InferenceClient
from datasets import load_dataset
import soundfile as sf
from typing import Dict, List, Any

class EndpointHandler:
    def __init__(self, path=""):
        self.client = InferenceClient(repo_id="microsoft/speecht5_tts", task="text-to-speech")
        self.embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")

    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
        text = data.get("inputs", "")
        speaker_embedding = self.embeddings_dataset['xvector'][7306].unsqueeze(0).tolist()

        response = self.client(payload={"inputs": text, "forward_params": {"speaker_embeddings": speaker_embedding}}, options={"wait_for_model": True})

        # Write the response audio to a file
        sf.write("speech.wav", response.audio, response.sampling_rate)

        # Return the expected response format
        return {
            "statusCode": 200,
            "body": {
                "audio": response.audio,  # Consider encoding this to a suitable format
                "sampling_rate": response.sampling_rate
            }
        }

handler = EndpointHandler()