add handler

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "openai/whisper-small",
+  "_name_or_path": "EricChang/openai-whisper-large-v2-Lora",
   "activation_dropout": 0.0,
   "activation_function": "gelu",
   "architectures": [

handler.py

@@ -0,0 +1,28 @@
+from typing import Dict, List, Any
+from transformers import WhisperForConditionalGeneration, pipeline
+from peft import LoraConfig, PeftModel, LoraModel, LoraConfig, get_peft_model, PeftConfig
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # Preload all the elements you are going to need at inference.
+        peft_config = PeftConfig.from_pretrained(path)
+        self.model= WhisperForConditionalGeneration.from_pretrained(
+            peft_config.base_model_name_or_path
+        )
+        self.model = PeftModel.from_pretrained(self.model, peft_model_id)
+        self.pipeline = pipeline(task= "automatic-speech-recognition", model=self.model)
+        self.pipeline.model.config.forced_decoder_ids = self.pipeline.tokenizer.get_decoder_prompt_ids(language="Chinese", task="transcribe")
+        self.pipeline.model.generation_config.forced_decoder_ids = self.pipeline.model.config.forced_decoder_ids
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            inputs (:obj: `str` | `PIL.Image` | `np.array`)
+            kwargs
+      Return:
+            A :obj:`list` | `dict`: will be serialized and returned
+        """
+
+        inputs = data.pop("inputs",data)
+        prediction = self.pipeline(inputs, return_timestamps=False)
+        return prediction