Create handler.py

handler.py

@@ -0,0 +1,51 @@
+import torch
+from transformers.pipelines.audio_utils import ffmpeg_read
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+from datasets import load_dataset
+
+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"
+        self.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)
+
+        self.pipeline = pipeline(
+            "automatic-speech-recognition",
+            model=model,
+            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,
+        )
+
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        """
+        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)
+        else:
+            result  = self.pipeline(inputs, return_timestamps=True, generate_kwargs={"task": "translate"})
+        # postprocess the prediction
+        return {"chunks": result["chunks"]}