add lora funetine webUI; optimize lora save and load logic

scripts/test_voxcpm_lora_infer.py

@@ -5,7 +5,6 @@ LoRA inference test script.
 Usage:
 
     python scripts/test_voxcpm_lora_infer.py \
-        --config_path conf/voxcpm/voxcpm_finetune_test.yaml \
         --lora_ckpt checkpoints/step_0002000 \
         --text "Hello, this is LoRA finetuned result." \
         --output lora_test.wav
@@ -13,37 +12,39 @@ Usage:
 With voice cloning:
 
     python scripts/test_voxcpm_lora_infer.py \
-        --config_path conf/voxcpm/voxcpm_finetune_test.yaml \
         --lora_ckpt checkpoints/step_0002000 \
         --text "This is voice cloning result." \
         --prompt_audio path/to/ref.wav \
         --prompt_text "Reference audio transcript" \
         --output lora_clone.wav
+
+Note: The script reads base_model path and lora_config from lora_config.json
+      in the checkpoint directory (saved automatically during training).
 """
 
 import argparse
+import json
 from pathlib import Path
 
 import soundfile as sf
 
 from voxcpm.core import VoxCPM
 from voxcpm.model.voxcpm import LoRAConfig
-from voxcpm.training.config import load_yaml_config
 
 
 def parse_args():
     parser = argparse.ArgumentParser("VoxCPM LoRA inference test")
-    parser.add_argument(
-        "--config_path",
-        type=str,
-        required=True,
-        help="Training YAML config path (contains pretrained_path and lora config)",
-    )
     parser.add_argument(
         "--lora_ckpt",
         type=str,
         required=True,
-        help="LoRA checkpoint directory (contains lora_weights.ckpt with lora_A/lora_B only)",
+        help="LoRA checkpoint directory (contains lora_weights.safetensors and lora_config.json)",
+    )
+    parser.add_argument(
+        "--base_model",
+        type=str,
+        default="",
+        help="Optional: override base model path (default: read from lora_config.json)",
     )
     parser.add_argument(
         "--text",
@@ -98,26 +99,44 @@ def parse_args():
 def main():
     args = parse_args()
 
-    # 1. Load YAML config
-    cfg = load_yaml_config(args.config_path)
-    pretrained_path = cfg["pretrained_path"]
-    lora_cfg_dict = cfg.get("lora", {}) or {}
-    lora_cfg = LoRAConfig(**lora_cfg_dict) if lora_cfg_dict else None
-
-    # 2. Check LoRA checkpoint
-    ckpt_dir = args.lora_ckpt
-    if not Path(ckpt_dir).exists():
+    # 1. Check LoRA checkpoint directory
+    ckpt_dir = Path(args.lora_ckpt)
+    if not ckpt_dir.exists():
         raise FileNotFoundError(f"LoRA checkpoint not found: {ckpt_dir}")
 
+    # 2. Load lora_config.json from checkpoint
+    lora_config_path = ckpt_dir / "lora_config.json"
+    if not lora_config_path.exists():
+        raise FileNotFoundError(
+            f"lora_config.json not found in {ckpt_dir}. "
+            "Make sure the checkpoint was saved with the updated training script."
+        )
+    
+    with open(lora_config_path, "r", encoding="utf-8") as f:
+        lora_info = json.load(f)
+    
+    # Get base model path (command line arg overrides config)
+    pretrained_path = args.base_model if args.base_model else lora_info.get("base_model")
+    if not pretrained_path:
+        raise ValueError("base_model not found in lora_config.json and --base_model not provided")
+    
+    # Get LoRA config
+    lora_cfg_dict = lora_info.get("lora_config", {})
+    lora_cfg = LoRAConfig(**lora_cfg_dict) if lora_cfg_dict else None
+    
+    print(f"Loaded config from: {lora_config_path}")
+    print(f"  Base model: {pretrained_path}")
+    print(f"  LoRA config: r={lora_cfg.r}, alpha={lora_cfg.alpha}" if lora_cfg else "  LoRA config: None")
+
     # 3. Load model with LoRA (no denoiser)
-    print(f"[1/2] Loading model with LoRA: {pretrained_path}")
+    print(f"\n[1/2] Loading model with LoRA: {pretrained_path}")
     print(f"      LoRA weights: {ckpt_dir}")
     model = VoxCPM.from_pretrained(
         hf_model_id=pretrained_path,
         load_denoiser=False,
         optimize=True,
         lora_config=lora_cfg,
-        lora_weights_path=ckpt_dir,
+        lora_weights_path=str(ckpt_dir),
     )
 
     # 4. Synthesize audio
@@ -197,7 +216,7 @@ def main():
 
     # === Test 5: Hot-reload LoRA (load_lora) ===
     print(f"\n  [Test 5] Hot-reload LoRA (load_lora)...")
-    loaded, skipped = model.load_lora(str(ckpt_dir))
+    loaded, skipped = model.load_lora(ckpt_dir)
     print(f"           Reloaded {len(loaded)} parameters")
     audio_np = model.generate(
         text=args.text,