Update handler.py

handler.py

@@ -1,39 +1,28 @@
-import torch
-from transformers import pipeline
+from huggingface_hub import InferenceClient
 from datasets import load_dataset
 import soundfile as sf
-from huggingface_hub.inference_api import InferenceApi
 from typing import Dict, List, Any
 
-
 class EndpointHandler:
     def __init__(self, path=""):
-        self.api = InferenceApi(repo_id="microsoft/speecht5_tts", task="text-to-speech")
+        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):
-        
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
         text = data.get("inputs", "")
-        # Extract speaker_embedding using the index from your dataset, or replace with your own logic.
-        speaker_embedding = torch.tensor(self.embeddings_dataset[7306]["xvector"]).unsqueeze(0)
-        # Convert embedding to list to avoid serialization issues
-        speaker_embedding_list = speaker_embedding.tolist()
-
+        speaker_embedding = self.embeddings_dataset['xvector'][7306].unsqueeze(0).tolist()
 
-        # Use the API to run the model
-        #parameters={"forward_params": {"speaker_embeddings": speaker_embedding_list}}
-        response = self.api(inputs=text, options={"wait_for_model": True})
+        response = self.client(payload={"inputs": text, "forward_params": {"speaker_embeddings": speaker_embedding}}, options={"wait_for_model": True})
 
         # Write the response audio to a file
-        # Note: This might not be possible in all environments, ensure this is suitable for your deployment
-        sf.write("speech.wav", response["audio"], samplerate=response["sampling_rate"])
+        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"]
+                "audio": response.audio,  # Consider encoding this to a suitable format
+                "sampling_rate": response.sampling_rate
             }
         }