Delete app.py

app.py

@@ -1,67 +0,0 @@
-import os
-import torch
-import gradio as gr
-import torchaudio
-from transformers import AutoFeatureExtractor, HubertModel
-from optimum.onnxruntime import ORTModelForFeatureExtraction
-from pathlib import Path
-
-# Configuration
-SAVE_DIR = "exported_model"
-HF_HUBERT = "facebook/hubert-base-ls960" # RVC Standard
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-def convert_to_onnx(audio_input):
-    if audio_input is None:
-        return None, "Error: No audio provided."
-
-    try:
-        # 1. Load Audio & Preprocess (Target 16kHz for HuBERT)
-        waveform, sr = torchaudio.load(audio_input)
-        if sr != 16000:
-            resampler = torchaudio.transforms.Resample(sr, 16000)
-            waveform = resampler(waveform)
-        
-        # 2. Export the HF Backbone to ONNX using Optimum
-        # This compiles the 'voice feature extractor' part of the RVC pipeline
-        print("Compiling HF Backbone to ONNX...")
-        onnx_model = ORTModelForFeatureExtraction.from_pretrained(
-            HF_HUBERT, 
-            export=True, 
-            torch_dtype=torch.float32
-        )
-        
-        # Save the compiled model locally
-        onnx_model.save_pretrained(SAVE_DIR)
-        
-        # In a real FastRVC3.0 app, you would also export the Generator .pth here
-        # For this demo, we provide the compiled HuBERT backbone as the result
-        model_path = os.path.join(SAVE_DIR, "model.onnx")
-        
-        return model_path, "Successfully compiled Voice-to-ONNX backbone!"
-
-    except Exception as e:
-        return None, f"Status: Error - {str(e)}"
-
-# --- Gradio UI ---
-with gr.Blocks(title="FastRVC 3.0 HF Compiler") as demo:
-    gr.Markdown("# 🤗 FastRVC 3.0 + HF Optimum")
-    gr.Markdown("Transform Hugging Face Voice Transformers into high-speed **ONNX** binaries.")
-    
-    with gr.Row():
-        with gr.Column():
-            audio_in = gr.Audio(label="Source Voice Sample", type="filepath")
-            btn = gr.Button("Compile & Export", variant="primary")
-        
-        with gr.Column():
-            status = gr.Textbox(label="Status")
-            file_out = gr.File(label="Download .onnx Model")
-
-    btn.click(
-        fn=convert_to_onnx,
-        inputs=[audio_in],
-        outputs=[file_out, status]
-    )
-
-if __name__ == "__main__":
-    demo.launch()