Update ffmpeg_handler.py

ffmpeg_handler.py

@@ -1,40 +1,23 @@
 from typing import Dict, Any, List
-from transformers import WhisperForConditionalGeneration, AutoProcessor, WhisperTokenizer, WhisperProcessor, pipeline, WhisperFeatureExtractor
+from transformers import pipeline
 import torch
 from transformers.pipelines.audio_utils import ffmpeg_read
-#import io
 
-
-#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+#ffmpeg
 
 
 class EndpointHandler:
     def __init__(self, path=""):
-        #tokenizer = WhisperTokenizer.from_pretrained('openai/whisper-large', language="korean", task='transcribe')        
-        #model = WhisperForConditionalGeneration.from_pretrained(path)
-        #self.tokenizer = WhisperTokenizer.from_pretrained(path)
-        #self.processor = WhisperProcessor.from_pretrained(path, language="korean", task='transcribe')
-        #processor = AutoProcessor.from_pretrained(path)
-        #self.pipe = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.feature_extractor, feature_extractor=processor.feature_extractor)
-        #feature_extractor = WhisperFeatureExtractor.from_pretrained('openai/whisper-large')
         self.pipe = pipeline(task='automatic-speech-recognition', model=path)
         
-
-        
-        # Move model to device
-#        self.model.to(device)
-        
     def __call__(self, data: Any) -> List[Dict[str, str]]:
-        print('==========NEW PROCESS=========')
 
         inputs = data.pop("inputs", data)
         audio_nparray = ffmpeg_read(inputs, 16000)
         audio_tensor= torch.from_numpy(audio_nparray)
-
         
         transcribe = self.pipe
         transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="ko", task="transcribe")
-        result = transcribe(audio_tensor)
-        
+        result = transcribe(audio_tensor)  
 
         return result