Update app.py

app.py

@@ -1,38 +1,31 @@
 import gradio as gr
 import torch
 from transformers import WhisperProcessor, WhisperForConditionalGeneration
+import soundfile as sf
 
 # Load Whisper model and processor from Hugging Face
 processor = WhisperProcessor.from_pretrained("openai/whisper-base")
 model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base").to("cuda" if torch.cuda.is_available() else "cpu")
 
-def transcribe(audio):
+def transcribe(audio_path):
     try:
-        # Load audio
-        audio_input = processor(audio, sampling_rate=16000, return_tensors="pt")
+        # Read audio file
+        audio, sampling_rate = sf.read(audio_path)
+        
+        # Process audio
+        inputs = processor(audio, sampling_rate=sampling_rate, return_tensors="pt").input_features
         
         # Move to appropriate device
-        audio_input = audio_input.input_features.to(model.device)
-
+        inputs = inputs.to(model.device)
+        
         # Generate transcription
-        predicted_ids = model.generate(audio_input)
+        predicted_ids = model.generate(inputs)
         transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
         
         return transcription
     except Exception as e:
         return f"Error: {str(e)}"
 
-def convert_to_wav(audio_file_path):
-    try:
-        wav_file_path = os.path.splitext(audio_file_path)[0] + '.wav'
-        audio = AudioSegment.from_file(audio_file_path)
-        audio.export(wav_file_path, format='wav')
-        logging.info(f'Converted {audio_file_path} to {wav_file_path}')
-        return wav_file_path
-    except Exception as e:
-        logging.error(f'Error converting file to WAV: {e}')
-        raise
-
 # Create a Gradio interface
 iface = gr.Interface(
     fn=transcribe,