Create handler.py

handler.py

@@ -0,0 +1,34 @@
+import torch
+from parler_tts import ParlerTTSForConditionalGeneration
+from transformers import AutoTokenizer, set_seed
+import soundfile as sf
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+        self.model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-expresso").to(self.device)
+        self.tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-expresso")
+
+    def __call__(self, data: Any):
+        inputs = data["inputs"]
+        prompt = inputs["prompt"]
+        description = inputs["description"]
+        
+        input_ids = self.tokenizer(description, return_tensors="pt").input_ids.to(self.device)
+        prompt_input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(self.device)
+        
+        set_seed(42)
+        try:
+            generation = self.model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
+            audio_arr = generation.cpu().numpy().squeeze()
+            return audio_arr
+        
+        except Exception as e:
+            logger.error(str(e))
+            del inputs
+            gc.collect()
+            torch.cuda.empty_cache()
+
+            return {"error": str(e)}