Update app.py

app.py

@@ -1,7 +1,9 @@
+# app.py
 import gradio as gr
 from playdiffusion import PlayDiffusion, RVCInput
 import os
 import wget
+import torch # Import torch to check for CUDA availability
 
 # --- Model Downloading ---
 print("--- Checking and Downloading Model Assets ---")
@@ -21,10 +23,20 @@ for filename, url in MODEL_FILES.items():
     else:
         print(f"{filename} already exists. Skipping download.")
 
+
 # --- Gradio App ---
+# --- KEY CHANGE: Force the model to load on the CPU ---
+# Check if a GPU is available, otherwise default to CPU
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print(f"--- Device selected: {device.upper()} ---")
 print("Initializing PlayDiffusion... This will load the models into memory.")
-inpainter = PlayDiffusion()
+
+# Pass the selected device to the PlayDiffusion constructor
+inpainter = PlayDiffusion(device=device)
+
 print("PlayDiffusion initialized successfully.")
+# ----------------------------------------------------
+
 
 def speech_rvc(rvc_source_speech, rvc_target_voice):
     if rvc_source_speech is None or rvc_target_voice is None:
@@ -34,9 +46,13 @@ def speech_rvc(rvc_source_speech, rvc_target_voice):
     print("Voice conversion finished.")
     return converted_audio
 
+
 with gr.Blocks(theme=gr.themes.Soft(), title="PlayDiffusion Voice Conversion") as demo:
     gr.Markdown("# 🗣️ PlayDiffusion Voice Conversion")
+    gr.Markdown(f"### Running on: **{device.upper()}**")
     gr.Markdown("Upload a **Source Speech** audio and a **Target Voice** audio to convert the speech.")
+    if device == 'cpu':
+        gr.Warning("Running on CPU. The voice conversion process will be extremely slow and may time out.")
 
     with gr.Row():
         rvc_source_speech = gr.Audio(label="Source Speech", sources=["upload", "microphone"], type="filepath")
@@ -48,4 +64,5 @@ with gr.Blocks(theme=gr.themes.Soft(), title="PlayDiffusion Voice Conversion") a
 
     rvc_submit.click(fn=speech_rvc, inputs=[rvc_source_speech, rvc_target_voice], outputs=[rvc_output])
 
+
 demo.launch()