Update app.py

app.py

@@ -1,32 +1,35 @@
 from fastapi import FastAPI, UploadFile, File
+from transformers import pipeline
+import torch
+import tempfile
 import os
 import time
-import tempfile
-import warnings
-import soundfile as sf
-import torch
-from transformers import pipeline
 
 # Define FastAPI app
 app = FastAPI()
 
+# Load the Whisper model once during startup
+device = 0 if torch.cuda.is_available() else -1  # Use GPU if available, otherwise CPU
+asr_pipeline = pipeline(model="openai/whisper-small", device=device)  # Initialize Whisper model
+
 # Basic GET endpoint
 @app.get("/")
 def read_root():
     return {"message": "Welcome to the FastAPI app on Hugging Face Spaces!"}
 
+# POST endpoint to transcribe audio
 @app.post("/transcribe/")
 async def transcribe_audio(file: UploadFile = File(...)):
     start_time = time.time()
 
     # Save the uploaded file using a temporary file manager
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio_file:
-        temp_file_path = temp_audio_file.name
         temp_audio_file.write(await file.read())
+        temp_file_path = temp_audio_file.name
 
     # Transcribe the audio
     transcription_start = time.time()
-    transcription = asr_pipeline(temp_file_path)
+    transcription = asr_pipeline(temp_file_path)  # Call the ASR pipeline
     transcription_end = time.time()
 
     # Clean up temporary file after use
@@ -38,7 +41,7 @@ async def transcribe_audio(file: UploadFile = File(...)):
     print(f"Total execution time: {end_time - start_time:.4f} seconds")
 
     return {"transcription": transcription['text']}
-    
+
 # If running as the main module, start Uvicorn
 if __name__ == "__main__":
     import uvicorn