Delete app.py

app.py

@@ -1,63 +0,0 @@
-import os
-import torch
-import gradio as gr
-from pathlib import Path
-from transformers import AutoFeatureExtractor
-from optimum.onnxruntime import ORTModelForFeatureExtraction
-
-# Backend Safety Check
-try:
-    import torchcodec
-    HAS_CODEC = True
-except Exception:
-    os.environ["TORCHAUDIO_USE_TORCHCODEC"] = "0"
-    HAS_CODEC = False
-
-import torchaudio
-
-SAVE_DIR = "fastrvc_export"
-HF_MODEL = "facebook/hubert-base-ls960"
-
-def run_compilation(audio_path):
-    if not audio_path:
-        return None, "Please upload a voice sample."
-    
-    try:
-        # Step 1: Load Audio
-        waveform, sr = torchaudio.load(audio_path)
-        if sr != 16000:
-            waveform = torchaudio.transforms.Resample(sr, 16000)(waveform)
-        
-        # Step 2: Compile to ONNX
-        # Using Optimum to handle the complex HF -> ONNX mapping
-        model = ORTModelForFeatureExtraction.from_pretrained(
-            HF_MODEL, 
-            export=True, 
-            torch_dtype=torch.float32
-        )
-        
-        Path(SAVE_DIR).mkdir(parents=True, exist_ok=True)
-        model.save_pretrained(SAVE_DIR)
-        
-        onnx_file = os.path.join(SAVE_DIR, "model.onnx")
-        return onnx_file, "Success! Model compiled for FastRVC 3.0."
-        
-    except Exception as e:
-        return None, f"Error: {str(e)}"
-
-# Gradio 6.10.0 UI
-with gr.Blocks(title="FastRVC 3.0 Compiler") as demo:
-    gr.Markdown("# 🎙️ FastRVC 3.0 Voice-to-ONNX")
-    
-    with gr.Row():
-        with gr.Column():
-            audio_in = gr.Audio(label="Input Voice", type="filepath")
-            btn = gr.Button("Compile Now", variant="primary")
-        with gr.Column():
-            status = gr.Textbox(label="System Log")
-            file_out = gr.File(label="Download ONNX")
-
-    btn.click(run_compilation, inputs=audio_in, outputs=[file_out, status])
-
-if __name__ == "__main__":
-    demo.launch()