Update app.py

app.py

@@ -1,29 +1,14 @@
 
-
-import gradio as gr
 import whisper
-from accelerate import init_empty_weights, load_checkpoint_and_dispatch
-import torch
+import gradio as gr
 
-# Check if GPU is available and set up device map
-device_map = "auto"  # Automatically balance layers across available devices
-print(f"Using ZeRO-powered device map: {device_map}")
+# Force the model to run on CPU
+device = "cpu"
+print("Running on CPU")
 
-# Load the Whisper model using Accelerate with ZeRO
+# Load the Whisper model on CPU
 model_name = "tiny"  # Change to "base", "small", etc., as needed
-
-print(f"Loading the Whisper model: {model_name} with ZeRO optimization...")
-with init_empty_weights():
-    whisper_model = whisper.load_model(model_name)  # Load model structure without weights
-
-# Dispatch the model across devices using ZeRO
-whisper_model = load_checkpoint_and_dispatch(
-    whisper_model,
-    device_map=device_map,
-    dtype=torch.float16  # Use mixed precision for efficiency
-)
-
-print("Model successfully loaded with ZeRO optimization!")
+whisper_model = whisper.load_model(model_name, device=device)
 
 # Define the transcription function
 def transcribe(audio):
@@ -36,9 +21,10 @@ demo = gr.Interface(
     fn=transcribe,  # The function to be called for transcription
     inputs=gr.Audio(source="microphone", type="filepath", label="Speak into the microphone"),  # Input audio
     outputs=gr.Textbox(label="Transcription"),  # Output transcription
-    title="Whisper Speech-to-Text with ZeRO",  # Title of the interface
-    description="Record audio using your microphone and get a transcription using the Whisper model optimized with ZeRO."
+    title="Whisper Speech-to-Text",  # Title of the interface
+    description="Record audio using your microphone and get a transcription using the Whisper model."
 )
 
 # Launch the Gradio interface
 demo.launch()
+