Create app.py

app.py

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+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()