Update handler.py

handler.py

@@ -1,8 +1,7 @@
-from typing import  Dict
+from typing import Dict
 from transformers import WhisperProcessor, WhisperForConditionalGeneration
-from transformers.pipelines.audio_utils import ffmpeg_read
-#from datasets import load_dataset
-
+from transformers.pipelines.audio import AudioClassificationPipeline
+from datasets import load_dataset
 import torch
 
 SAMPLE_RATE = 16000
@@ -10,11 +9,10 @@ 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")
-
+        self.classifier = AudioClassificationPipeline(model=self.model, processor=self.processor, device=0)
+        self.forced_decoder_ids = self.processor.get_decoder_prompt_ids(language="Danish", task="transcribe")
 
     def __call__(self, data: Dict[str, bytes]) -> Dict[str, str]:
         """
@@ -26,17 +24,11 @@ class EndpointHandler():
         """
         # process input
         inputs = data.pop("inputs", data)
-        audio_nparray = ffmpeg_read(inputs, SAMPLE_RATE)
+        audio_nparray = ffmpeg_read(inputs, sample_rate=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)
+        result = self.classifier(audio_nparray)
 
         # postprocess the prediction
-        return {"text": result["text"]}
+        return {"txt": result[0]["transcription"]}