Create handler.py

handler.py

@@ -0,0 +1,41 @@
+from typing import  Dict
+from transformers.pipelines.audio_utils import ffmpeg_read, WhisperProcessor, WhisperForConditionalGeneration
+from datasets import load_dataset
+
+import torch
+
+SAMPLE_RATE = 16000
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # load the model
+        #self.model = whisper.load_model("medium")
+        self.processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
+        self.model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2")
+        self.forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe")
+
+
+    def __call__(self, data: Dict[str, bytes]) -> Dict[str, str]:
+        """
+        Args:
+            data (:obj:):
+                includes the deserialized audio file as bytes
+        Return:
+            A :obj:`dict`:. base64 encoded image
+        """
+        # process input
+        inputs = data.pop("inputs", data)
+        audio_nparray = ffmpeg_read(inputs, SAMPLE_RATE)
+        audio_tensor= torch.from_numpy(audio_nparray)
+
+        #ds = load_dataset("common_voice", "fr", split="test", streaming=True)
+        #ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
+        #input_speech = next(iter(ds))["audio"]
+        #input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features
+
+        
+        # run inference pipeline
+        result = self.model.transcribe(audio_nparray)
+
+        # postprocess the prediction
+        return {"text": result["text"]}