feat: move original plain HTML frontend to frontend-deprecated

lib/export_onnx.py

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+import os
+import sys
+import torch
+import argparse
+import traceback
+
+# Menambahkan direktori aktif ke path agar lib dapat diimpor
+sys.path.append(os.getcwd())
+
+from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
+
+def export_model_to_onnx(model_path, output_onnx_path):
+    print(f"Loading PyTorch checkpoint from: {model_path}")
+    try:
+        # Load checkpoint ke CPU
+        cpt = torch.load(model_path, map_location="cpu")
+    except Exception as e:
+        print(f"Error loading checkpoint: {e}")
+        return False
+        
+    # Ambil metadata model
+    tgt_sr = cpt["config"][-1]
+    
+    # Ambil jumlah spk dari bobot embedding
+    if "emb_g.weight" in cpt["weight"]:
+        n_spk = cpt["weight"]["emb_g.weight"].shape[0]
+    else:
+        n_spk = 1
+        
+    # Sesuaikan config spk_embed_dim
+    cpt["config"][-3] = n_spk
+    
+    version = cpt.get("version", "v1")
+    if_f0 = cpt.get("f0", 1)
+    
+    print(f"Model Version: {version}")
+    print(f"Pitch (F0) Enabled: {if_f0}")
+    print(f"Target Sample Rate: {tgt_sr} Hz")
+    print(f"Number of Speakers: {n_spk}")
+    
+    # Inisialisasi model khusus ONNX (SynthesizerTrnMsNSFsidM)
+    # is_half set ke False untuk ekspor dalam FP32 demi kompabilitas ONNX Runtime yang stabil
+    try:
+        net_g = SynthesizerTrnMsNSFsidM(*cpt["config"], version=version, is_half=False)
+        
+        # Hapus bagian encoder posterior yang tidak digunakan saat inferensi
+        if hasattr(net_g, "enc_q"):
+            del net_g.enc_q
+            
+        # Muat bobot model, biarkan strict=False agar mengabaikan enc_q yang dihapus
+        net_g.load_state_dict(cpt["weight"], strict=False)
+        net_g.eval()
+        
+        print("PyTorch model loaded successfully. Preparing dummy inputs...")
+    except Exception as e:
+        print(f"Failed to initialize RVC ONNX model class: {e}")
+        traceback.print_exc()
+        return False
+        
+    # Siapkan dummy inputs untuk tracing ekspor
+    test_len = 10  # Panjang sekuens dummy
+    feat_dim = 256 if version == "v1" else 768
+    
+    phone = torch.randn(1, test_len, feat_dim, dtype=torch.float32)
+    phone_lengths = torch.tensor([test_len], dtype=torch.int64)
+    pitch = torch.randint(1, 254, (1, test_len), dtype=torch.int64)
+    nsff0 = torch.randn(1, test_len, dtype=torch.float32)
+    g = torch.tensor([0], dtype=torch.int64)  # Speaker ID 0
+    rnd = torch.randn(1, 192, test_len, dtype=torch.float32)
+    
+    input_names = ["phone", "phone_lengths", "pitch", "nsff0", "g", "rnd"]
+    output_names = ["audio"]
+    
+    dynamic_axes = {
+        "phone": {1: "length"},
+        "pitch": {1: "length"},
+        "nsff0": {1: "length"},
+        "rnd": {2: "length"},
+        "audio": {1: "audio_length"}
+    }
+    
+    print(f"Exporting model to ONNX format at: {output_onnx_path}")
+    try:
+        torch.onnx.export(
+            net_g,
+            (phone, phone_lengths, pitch, nsff0, g, rnd),
+            output_onnx_path,
+            opset_version=17,
+            input_names=input_names,
+            output_names=output_names,
+            dynamic_axes=dynamic_axes,
+            verbose=False
+        )
+        print("ONNX model exported successfully!")
+        return True
+    except Exception as e:
+        print(f"Error during ONNX export: {e}")
+        traceback.print_exc()
+        return False
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Export RVC PyTorch .pth model to ONNX")
+    parser.add_argument("--model_name", type=str, required=True, help="Nama model di folder weights (nama sub-folder)")
+    parser.add_argument("--output", type=str, default="", help="Path output file ONNX (opsional)")
+    
+    args = parser.parse_args()
+    
+    model_root = "weights"
+    model_dir = os.path.join(model_root, args.model_name)
+    
+    if not os.path.isdir(model_dir):
+        print(f"Error: Folder '{model_dir}' tidak ditemukan!")
+        sys.exit(1)
+        
+    pth_files = [f for f in os.listdir(model_dir) if f.endswith(".pth")]
+    if not pth_files:
+        print(f"Error: Tidak ada berkas .pth di dalam folder '{model_dir}'!")
+        sys.exit(1)
+        
+    pth_path = os.path.join(model_dir, pth_files[0])
+    
+    if args.output:
+        onnx_path = args.output
+    else:
+        # Default simpan di dalam sub-folder weights yang sama
+        onnx_name = os.path.splitext(pth_files[0])[0] + ".onnx"
+        onnx_path = os.path.join(model_dir, onnx_name)
+        
+    success = export_model_to_onnx(pth_path, onnx_path)
+    if success:
+        print(f"\nSelesai! Model ONNX disimpan di: {onnx_path}")
+    else:
+        print("\nEkspor gagal!")
+        sys.exit(1)