Update handler.py

handler.py

@@ -1,54 +1,21 @@
-from typing import  Dict, List, Any
-import torch
-from transformers.pipelines.audio_utils import ffmpeg_read
-from transformers import WhisperProcessor, AutoModelForSpeechSeq2Seq, AutoProcessor, AutoTokenizer, pipeline
+from typing import Dict, Any
+from transformers import AutoModelForSpeechSeq2Seq, AutoTokenizer, pipeline
 
 class EndpointHandler():
     def __init__(self, path=""):
-        device = "cuda:0" if torch.cuda.is_available() else "cpu"
-        torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
         model_id = "openai/whisper-large-v3"
-
-        model = AutoModelForSpeechSeq2Seq.from_pretrained(
-            model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-        )
-        model.to(device)
-
-        processor = AutoProcessor.from_pretrained(model_id)
-        #processor = WhisperProcessor.from_pretrained(model_id)
-        
-        self.pipeline = pipeline(
-            "automatic-speech-recognition",
-            model=model_id,
-            tokenizer=processor.tokenizer,
-            feature_extractor=processor.feature_extractor,
-            max_new_tokens=128,
-            chunk_length_s=30,
-            batch_size=16,
-            return_timestamps=True,
-            torch_dtype=torch_dtype,
-            device=device,
-        )
-        self.model = model
-
+        task = "automatic-speech-recognition"
+        self.model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id)
+        self.tokenizer = AutoTokenizer.from_pretrained(model_id)
+        self.pipeline = pipeline(task, model=self.model, tokenizer=self.tokenizer)
 
     def __call__(self, data: Dict[str, bytes]) -> Dict[str, str]:
-        """
-        Args:
-            data (:obj:):
-                includes the input data and the parameters for the inference.
-        Return:
-            A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
-                - "label": A string representing what the label/class is. There can be multiple labels.
-                - "score": A score between 0 and 1 describing how confident the model is for this label/class.
-        """
         inputs = data.pop("inputs", data)
         parameters = data.pop("parameters", None)
 
-        # pass inputs with all kwargs in data
         if parameters is not None:
-            result  = self.pipeline(inputs, return_timestamps=True, **parameters)
+            result = self.pipeline(inputs, return_timestamps=True, **parameters)
         else:
-            result  = self.pipeline(inputs, return_timestamps=True, generate_kwargs={"task": "translate"})
-        # postprocess the prediction
+            result = self.pipeline(inputs, return_timestamps=True)
+
         return {"chunks": result["chunks"]}