Update app.py

app.py

@@ -38,9 +38,9 @@ def convert_to_wav(audio_file):
 # Initialize device for torch
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Load Whisper model and tokenizer
+# Load Whisper model and processor directly using the transformers library
 whisper_processor = WhisperProcessor.from_pretrained("NbAiLab/nb-whisper-large")
-whisper_model = torch.hub.load('huggingface/pytorch-transformers', 'model', "NbAiLab/nb-whisper-large").to(device)
+whisper_model = WhisperForConditionalGeneration.from_pretrained("NbAiLab/nb-whisper-large").to(device)
 
 def transcribe_audio(audio_file):
     if audio_file.endswith(".m4a"):
@@ -62,7 +62,6 @@ def transcribe_audio(audio_file):
     result = f"Time taken: {output_time:.2f} seconds\nNumber of words: {len(text.split())}"
 
     return text, result
-
 # Clean and preprocess text for summarization
 def clean_text(text):
     text = re.sub(r'https?:\/\/.*[\r\n]*', '', text)