Update app.py

app.py

@@ -1,21 +1,17 @@
 import gradio as gr
 from transformers import pipeline
 import torch
-import spaces
 
 # Load the Whisper model pipeline for speech recognition with optimizations
 model_name = "Vira21/Whisper-Base-KhmerV2"
 whisper_pipeline = pipeline(
     "automatic-speech-recognition", 
     model=model_name,
-    device="cuda" if torch.cuda.is_available() else "CPU"
+    device=0 if torch.cuda.is_available() else -1  # Use GPU if available, otherwise use CPU
 )
 
 def transcribe_audio(audio):
     try:
-        if audio is None:
-            return "No audio provided. Please upload an audio file or record your voice."
-        
         # Process and transcribe the audio
         result = whisper_pipeline(audio)["text"]
         return result
@@ -28,12 +24,13 @@ interface = gr.Interface(
     fn=transcribe_audio,
     inputs=gr.Audio(type="filepath"),  
     outputs="text",
-    title="OpenAI Whisper Small KHMER-ENGLISH Speech-to-Text",
-    description="Upload an audio file or record your voice to get the transcription.",
-    examples=[["Example Audio/126.wav"], ["Example Audio/tomholland28282.wav"]],
+    title="Whisper Base Khmer Speech-to-Text",
+    description="Upload an audio file or record your voice to get the transcription in Khmer.",
+    examples=[["Example Audio/126.wav"]. ["Example Audio/tomholland28282.wav"]],
     allow_flagging="never"  # Disables flagging to save resources
 )
 
 # Launch the app with queue enabled for better handling on free CPU
 if __name__ == "__main__":
-    interface.launch()
+    interface.queue()  # Enable asynchronous queuing for better performance
+    interface.launch()