use faster model if api token is provided

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 from stable_whisper import load_model
+from stable_whisper import load_hf_whisper
 import requests
 import os
 
@@ -20,6 +21,7 @@ def transcribe_to_subtitle(audio_bytes, model_name):
   """Transcribe audio to subtitle using OpenAI Whisper"""
   # Load model based on selection
   model = load_model(model_name)
+  speedmodel = load_hf_whisper(model_name)
   
   # Check file size for free tier
   if use_free_tier and len(audio_bytes) > 2 * 60 * 1024:
@@ -27,10 +29,16 @@ def transcribe_to_subtitle(audio_bytes, model_name):
       return
   
   #  Transcribe audio
-  try:
-    result = model.transcribe(audio_bytes, verbose=True)
-  except Exception as e:
-     return {"error": f"Error during transcription: {str(e)}"}
+  if use_free_tier and not api_token:
+    try:
+      result = model.transcribe(audio_bytes, verbose=True)
+    except Exception as e:
+       return {"error": f"Error during transcription: {str(e)}"}
+  else:
+    try:
+      result = speedmodel.transcribe(audio_bytes, verbose=True)
+    except Exception as e:
+       return {"error": f"Error during transcription: {str(e)}"}
   
   # Generate subtitle file
   subtitle_text = result.text