Update handler.py

handler.py

@@ -1,20 +1,22 @@
 from typing import Dict, Any, List
 from transformers import WhisperForConditionalGeneration, AutoProcessor, WhisperTokenizer, WhisperProcessor, pipeline, WhisperFeatureExtractor
 import torch
-import soundfile as sf
-import io
+#import io
+
+
+#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
 class EndpointHandler:
     def __init__(self, path=""):
-        tokenizer = WhisperTokenizer.from_pretrained('openai/whisper-large', language="korean", task='transcribe')        
-        model = WhisperForConditionalGeneration.from_pretrained(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)
+        #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)
+        #feature_extractor = WhisperFeatureExtractor.from_pretrained('openai/whisper-large')
+        self.pipe = pipeline(task='automatic-speech-recognition', model=path, device=)
         
 
         
@@ -23,7 +25,7 @@ class EndpointHandler:
         
     def __call__(self, data: Any) -> List[Dict[str, str]]:
         print('==========NEW PROCESS=========')
-        transcribe = pipeline(task="automatic-speech-recognition", model="vasista22/whisper-kannada-tiny", chunk_length_s=30, device=device)
+        transcribe = pipeline(task="automatic-speech-recognition", model="vasista22/whisper-kannada-tiny")
         transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="ko", task="transcribe")
         result = transcribe(data['inputs'])