add lora funetine webUI; optimize lora save and load logic

README.md

@@ -210,6 +210,8 @@ We're excited to see the VoxCPM community growing! Here are some amazing project
 - **[VoxCPM-NanoVLLM](https://github.com/a710128/nanovllm-voxcpm)** NanoVLLM integration for VoxCPM for faster, high-throughput inference on GPU.
 - **[VoxCPM-ONNX](https://github.com/bluryar/VoxCPM-ONNX)** ONNX export for VoxCPM supports faster CPU inference.
 - **[VoxCPMANE](https://github.com/0seba/VoxCPMANE)** VoxCPM TTS with Apple Neural Engine backend server.
+- **[PR: LoRA finetune web UI (by Ayin1412)](https://github.com/OpenBMB/VoxCPM/pull/100)**
+- **[voxcpm_rs](https://github.com/madushan1000/voxcpm_rs)** A re-implementation of VoxCPM-0.5B in Rust.
 
 *Note: The projects are not officially maintained by OpenBMB.*
 

app.py

@@ -267,7 +267,7 @@ def run_demo(server_name: str = "localhost", server_port: int = 7860, show_error
     demo = VoxCPMDemo()
     interface = create_demo_interface(demo)
     # Recommended to enable queue on Spaces for better throughput
-    interface.queue(max_size=10).launch(server_name=server_name, server_port=server_port, show_error=show_error)
+    interface.queue(max_size=10, default_concurrency_limit=1).launch(server_name=server_name, server_port=server_port, show_error=show_error)
 
 
 if __name__ == "__main__":

conf/voxcpm_v1.5/voxcpm_finetune_lora.yaml

@@ -19,6 +19,8 @@ tensorboard: /path/to/logs/finetune_lora
 lambdas:
   loss/diff: 1.0
   loss/stop: 1.0
+
+# LoRA configuration
 lora:
   enable_lm: true
   enable_dit: true
@@ -26,3 +28,9 @@ lora:
   r: 32               
   alpha: 16           
   dropout: 0.0
+
+# Distribution options (optional)
+# - If distribute=false (default): save pretrained_path as base_model in lora_config.json
+# - If distribute=true: save hf_model_id as base_model (hf_model_id is required)
+# hf_model_id: "openbmb/VoxCPM1.5"
+# distribute: true

conf/voxcpm_v1/voxcpm_finetune_lora.yaml

@@ -19,6 +19,8 @@ tensorboard: /path/to/logs/finetune_lora
 lambdas:
   loss/diff: 1.0
   loss/stop: 1.0
+
+# LoRA configuration
 lora:
   enable_lm: true
   enable_dit: true
@@ -26,3 +28,9 @@ lora:
   r: 32               
   alpha: 16           
   dropout: 0.0
+
+# Distribution options (optional)
+# - If distribute=false (default): save pretrained_path as base_model in lora_config.json
+# - If distribute=true: save hf_model_id as base_model (hf_model_id is required)
+# hf_model_id: "openbmb/VoxCPM-0.5B"
+# distribute: true       

docs/finetune.md

@@ -19,6 +19,7 @@ LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning method that:
 
 ## Table of Contents
 
+- [Quick Start: WebUI](#quick-start-webui)
 - [Data Preparation](#data-preparation)
 - [Full Fine-tuning](#full-fine-tuning)
 - [LoRA Fine-tuning](#lora-fine-tuning)
@@ -28,6 +29,31 @@ LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning method that:
 
 ---
 
+## Quick Start: WebUI
+
+For users who prefer a graphical interface, we provide `lora_ft_webui.py` - a comprehensive WebUI for training and inference:
+
+### Launch WebUI
+
+```bash
+python lora_ft_webui.py
+```
+
+Then open `http://localhost:7860` in your browser.
+
+### Features
+
+- **🚀 Training Tab**: Configure and start LoRA training with an intuitive interface
+  - Set training parameters (learning rate, batch size, LoRA rank, etc.)
+  - Monitor training progress in real-time
+  - Resume training from existing checkpoints
+
+- **🎵 Inference Tab**: Generate audio with trained models
+  - Automatic base model loading from LoRA checkpoint config
+  - Voice cloning with automatic ASR (reference text recognition)
+  - Hot-swap between multiple LoRA models
+  - Zero-shot TTS without reference audio
+
 ## Data Preparation
 
 Training data should be prepared as a JSONL manifest file, with one sample per line:
@@ -177,6 +203,10 @@ lora:
   # Target modules
   target_modules_lm: ["q_proj", "v_proj", "k_proj", "o_proj"]
   target_modules_dit: ["q_proj", "v_proj", "k_proj", "o_proj"]
+
+# Distribution options (optional)
+# hf_model_id: "openbmb/VoxCPM1.5"  # HuggingFace ID
+# distribute: true                   # If true, save hf_model_id in lora_config.json
 ```
 
 ### LoRA Parameters
@@ -189,6 +219,15 @@ lora:
 | `alpha` | Scaling factor, `scaling = alpha / r` | Usually `r/2` or `r` |
 | `target_modules_*` | Layer names to add LoRA | attention layers |
 
+### Distribution Options (Optional)
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `hf_model_id` | HuggingFace model ID (e.g., `openbmb/VoxCPM1.5`) | `""` |
+| `distribute` | If `true`, save `hf_model_id` as `base_model` in checkpoint; otherwise save local `pretrained_path` | `false` |
+
+> **Note**: If `distribute: true`, `hf_model_id` is required.
+
 ### Training
 
 ```bash
@@ -202,16 +241,37 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 \
 
 ### Checkpoint Structure
 
-LoRA training saves only LoRA parameters:
+LoRA training saves LoRA parameters and configuration:
 
 ```
 checkpoints/finetune_lora/
 └── step_0002000/
     ├── lora_weights.safetensors    # Only lora_A, lora_B parameters
+    ├── lora_config.json            # LoRA config + base model path
     ├── optimizer.pth
     └── scheduler.pth
 ```
 
+The `lora_config.json` contains:
+```json
+{
+  "base_model": "/path/to/VoxCPM1.5/",
+  "lora_config": {
+    "enable_lm": true,
+    "enable_dit": true,
+    "r": 32,
+    "alpha": 16,
+    ...
+  }
+}
+```
+
+The `base_model` field contains:
+- Local path (default): when `distribute: false` or not set
+- HuggingFace ID: when `distribute: true` (e.g., `"openbmb/VoxCPM1.5"`)
+
+This allows loading LoRA checkpoints without the original training config file.
+
 ---
 
 ## Inference
@@ -240,11 +300,10 @@ python scripts/test_voxcpm_ft_infer.py \
 
 ### LoRA Inference
 
-LoRA inference requires the training config (for LoRA structure) and LoRA checkpoint:
+LoRA inference only requires the checkpoint directory (base model path and LoRA config are read from `lora_config.json`):
 
 ```bash
 python scripts/test_voxcpm_lora_infer.py \
-    --config_path conf/voxcpm_v1.5/voxcpm_finetune_lora.yaml \
     --lora_ckpt /path/to/checkpoints/finetune_lora/step_0002000 \
     --text "Hello, this is LoRA fine-tuned result." \
     --output lora_output.wav
@@ -254,7 +313,6 @@ With voice cloning:
 
 ```bash
 python scripts/test_voxcpm_lora_infer.py \
-    --config_path conf/voxcpm_v1.5/voxcpm_finetune_lora.yaml \
     --lora_ckpt /path/to/checkpoints/finetune_lora/step_0002000 \
     --text "This is voice cloning with LoRA." \
     --prompt_audio /path/to/reference.wav \
@@ -262,6 +320,16 @@ python scripts/test_voxcpm_lora_infer.py \
     --output cloned_output.wav
 ```
 
+Override base model path (optional):
+
+```bash
+python scripts/test_voxcpm_lora_infer.py \
+    --lora_ckpt /path/to/checkpoints/finetune_lora/step_0002000 \
+    --base_model /path/to/another/VoxCPM1.5 \
+    --text "Use different base model." \
+    --output output.wav
+```
+
 ---
 
 ## LoRA Hot-swapping
@@ -315,20 +383,39 @@ print(f"Loaded {len(loaded)} params, skipped {len(skipped)}")
 lora_state = model.get_lora_state_dict()
 ```
 
-### Simplified Usage (Auto LoRA Config)
+### Simplified Usage (Load from lora_config.json)
 
-If you only have LoRA weights and don't need custom config, just provide the path:
+If your checkpoint contains `lora_config.json` (saved by the training script), you can load everything automatically:
 
 ```python
+import json
 from voxcpm.core import VoxCPM
+from voxcpm.model.voxcpm import LoRAConfig
 
-# Auto-create default LoRAConfig when only lora_weights_path is provided
+# Load config from checkpoint
+lora_ckpt_dir = "/path/to/checkpoints/finetune_lora/step_0002000"
+with open(f"{lora_ckpt_dir}/lora_config.json") as f:
+    lora_info = json.load(f)
+
+base_model = lora_info["base_model"]
+lora_cfg = LoRAConfig(**lora_info["lora_config"])
+
+# Load model with LoRA
 model = VoxCPM.from_pretrained(
-    hf_model_id="openbmb/VoxCPM1.5",
+    hf_model_id=base_model,
-    lora_weights_path="/path/to/lora_checkpoint",  # Will auto-create LoRAConfig
+    lora_config=lora_cfg,
+    lora_weights_path=lora_ckpt_dir,
 )
 ```
 
+Or use the test script directly:
+
+```bash
+python scripts/test_voxcpm_lora_infer.py \
+    --lora_ckpt /path/to/checkpoints/finetune_lora/step_0002000 \
+    --text "Hello world"
+```
+
 ### Method Reference
 
 | Method | Description | torch.compile Compatible |
@@ -354,7 +441,6 @@ model = VoxCPM.from_pretrained(
 
 - Increase `r` (LoRA rank)
 - Adjust `alpha` (try `alpha = r/2` or `alpha = r`)
-- Ensure `enable_dit: true` (required for voice cloning)
 - Increase training steps
 - Add more target modules
 
@@ -366,11 +452,13 @@ model = VoxCPM.from_pretrained(
 
 ### 4. LoRA Not Taking Effect at Inference
 
-- Ensure inference config matches training config LoRA parameters
+- Check that `lora_config.json` exists in the checkpoint directory
 - Check `load_lora()` return value - `skipped_keys` should be empty
 - Verify `set_lora_enabled(True)` is called
 
 ### 5. Checkpoint Loading Errors
 
 - Full fine-tuning: checkpoint directory should contain `model.safetensors` (or `pytorch_model.bin`), `config.json`, `audiovae.pth`
-- LoRA: checkpoint directory should contain `lora_weights.safetensors` (or `lora_weights.ckpt`)
+- LoRA: checkpoint directory should contain:
+  - `lora_weights.safetensors` (or `lora_weights.ckpt`) - LoRA weights
+  - `lora_config.json` - LoRA config and base model path

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
+    # 1. Check LoRA checkpoint directory
-    cfg = load_yaml_config(args.config_path)
+    ckpt_dir = Path(args.lora_ckpt)
-    pretrained_path = cfg["pretrained_path"]
+    if not ckpt_dir.exists():
-    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():
         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,

scripts/train_voxcpm_finetune.py

@@ -14,6 +14,8 @@ import torch
 from tensorboardX import SummaryWriter
 from torch.optim import AdamW
 from transformers import get_cosine_schedule_with_warmup
+import signal
+import os
 
 try:
     from safetensors.torch import save_file
@@ -56,8 +58,16 @@ def train(
     lambdas: Dict[str, float] = {"loss/diff": 1.0, "loss/stop": 1.0},
     lora: dict = None,
     config_path: str = "",
+    # Distribution options (for LoRA checkpoints)
+    hf_model_id: str = "",   # HuggingFace model ID (e.g., "openbmb/VoxCPM1.5")
+    distribute: bool = False, # If True, save hf_model_id as base_model; otherwise save pretrained_path
 ):
     _ = config_path
+    
+    # Validate distribution options
+    if lora is not None and distribute and not hf_model_id:
+        raise ValueError("hf_model_id is required when distribute=True")
+    
     accelerator = Accelerator(amp=True)
 
     save_dir = Path(save_path)
@@ -171,6 +181,39 @@ def train(
         num_training_steps=total_training_steps,
     )
 
+    # Try to load checkpoint and resume training
+    start_step = 0
+    if accelerator.rank == 0:
+        start_step = load_checkpoint(model, optimizer, scheduler, save_dir)
+    # Broadcast start_step to all processes
+    if hasattr(accelerator, 'all_reduce'):
+        start_step_tensor = torch.tensor(start_step, device=accelerator.device)
+        accelerator.all_reduce(start_step_tensor)
+        start_step = int(start_step_tensor.item())
+    
+    if start_step > 0 and accelerator.rank == 0:
+        tracker.print(f"Resuming training from step {start_step}")
+
+    # Resume tracker for signal handler to read current step
+    resume = {"step": start_step}
+
+    # Register signal handler to save checkpoint on termination (SIGTERM/SIGINT)
+    def _signal_handler(signum, frame, _model=model, _optim=optimizer, _sched=scheduler, _save_dir=save_dir, _pretrained=pretrained_path, _hf_id=hf_model_id, _dist=distribute, _resume=resume):
+        try:
+            cur_step = int(_resume.get("step", start_step))
+        except Exception:
+            cur_step = start_step
+        print(f"Signal {signum} received. Saving checkpoint at step {cur_step} ...")
+        try:
+            save_checkpoint(_model, _optim, _sched, _save_dir, cur_step, _pretrained, _hf_id, _dist)
+            print("Checkpoint saved. Exiting.")
+        except Exception as e:
+            print(f"Error saving checkpoint on signal: {e}")
+        os._exit(0)
+
+    signal.signal(signal.SIGTERM, _signal_handler)
+    signal.signal(signal.SIGINT, _signal_handler)
+
     # Manual epoch management instead of itertools.cycle to support DistributedSampler.set_epoch()
     grad_accum_steps = max(int(grad_accum_steps), 1)
     data_epoch = 0
@@ -191,7 +234,9 @@ def train(
             return next(train_iter)
 
     with tracker.live():
-        for step in range(num_iters):
+        for step in range(start_step, num_iters):
+            # update resume step so signal handler can save current progress
+            resume["step"] = step
             tracker.step = step
             optimizer.zero_grad(set_to_none=True)
 
@@ -255,10 +300,10 @@ def train(
                 validate(model, val_loader, batch_processor, accelerator, tracker, lambdas)
 
             if step % save_interval == 0 and accelerator.rank == 0:
-                save_checkpoint(model, optimizer, scheduler, save_dir, step, pretrained_path)
+                save_checkpoint(model, optimizer, scheduler, save_dir, step, pretrained_path, hf_model_id, distribute)
 
     if accelerator.rank == 0:
-        save_checkpoint(model, optimizer, scheduler, save_dir, num_iters, pretrained_path)
+        save_checkpoint(model, optimizer, scheduler, save_dir, num_iters, pretrained_path, hf_model_id, distribute)
     if writer:
         writer.close()
 
@@ -301,7 +346,77 @@ def validate(model, val_loader, batch_processor, accelerator, tracker, lambdas):
     model.train()
 
 
-def save_checkpoint(model, optimizer, scheduler, save_dir: Path, step: int, pretrained_path: str = None):
+def load_checkpoint(model, optimizer, scheduler, save_dir: Path):
+    """
+    Load the latest checkpoint if it exists.
+    Returns the step number to resume from, or 0 if no checkpoint found.
+    """
+    latest_folder = save_dir / "latest"
+    if not latest_folder.exists():
+        return 0
+    
+    unwrapped = model.module if hasattr(model, "module") else model
+    lora_cfg = unwrapped.lora_config
+    
+    # Load model weights
+    if lora_cfg is not None:
+        # LoRA: load lora_weights
+        lora_weights_path = latest_folder / "lora_weights.safetensors"
+        if not lora_weights_path.exists():
+            lora_weights_path = latest_folder / "lora_weights.ckpt"
+        
+        if lora_weights_path.exists():
+            if lora_weights_path.suffix == ".safetensors":
+                from safetensors.torch import load_file
+                state_dict = load_file(str(lora_weights_path))
+            else:
+                ckpt = torch.load(lora_weights_path, map_location="cpu")
+                state_dict = ckpt.get("state_dict", ckpt)
+            
+            # Load only lora weights
+            unwrapped.load_state_dict(state_dict, strict=False)
+            print(f"Loaded LoRA weights from {lora_weights_path}")
+    else:
+        # Full finetune: load model.safetensors or pytorch_model.bin
+        model_path = latest_folder / "model.safetensors"
+        if not model_path.exists():
+            model_path = latest_folder / "pytorch_model.bin"
+        
+        if model_path.exists():
+            if model_path.suffix == ".safetensors":
+                from safetensors.torch import load_file
+                state_dict = load_file(str(model_path))
+            else:
+                ckpt = torch.load(model_path, map_location="cpu")
+                state_dict = ckpt.get("state_dict", ckpt)
+            
+            unwrapped.load_state_dict(state_dict, strict=False)
+            print(f"Loaded model weights from {model_path}")
+    
+    # Load optimizer state
+    optimizer_path = latest_folder / "optimizer.pth"
+    if optimizer_path.exists():
+        optimizer.load_state_dict(torch.load(optimizer_path, map_location="cpu"))
+        print(f"Loaded optimizer state from {optimizer_path}")
+    
+    # Load scheduler state
+    scheduler_path = latest_folder / "scheduler.pth"
+    if scheduler_path.exists():
+        scheduler.load_state_dict(torch.load(scheduler_path, map_location="cpu"))
+        print(f"Loaded scheduler state from {scheduler_path}")
+    
+    # Try to infer step from checkpoint folders
+    step_folders = [d for d in save_dir.iterdir() if d.is_dir() and d.name.startswith("step_")]
+    if step_folders:
+        steps = [int(d.name.split("_")[1]) for d in step_folders]
+        resume_step = max(steps)
+        print(f"Resuming from step {resume_step}")
+        return resume_step
+    
+    return 0
+
+
+def save_checkpoint(model, optimizer, scheduler, save_dir: Path, step: int, pretrained_path: str = None, hf_model_id: str = "", distribute: bool = False):
     """
     Save checkpoint with different strategies for full finetune vs LoRA:
     - Full finetune: save non-vae weights to model.safetensors (or pytorch_model.bin if safetensors unavailable)
@@ -325,6 +440,17 @@ def save_checkpoint(model, optimizer, scheduler, save_dir: Path, step: int, pret
             save_file(state_dict, folder / "lora_weights.safetensors")
         else:
             torch.save({"state_dict": state_dict}, folder / "lora_weights.ckpt")
+        
+        # Save LoRA config and base model path to a separate JSON file
+        # If distribute=True, save hf_model_id; otherwise save local pretrained_path
+        import json
+        base_model_to_save = hf_model_id if distribute else (str(pretrained_path) if pretrained_path else None)
+        lora_info = {
+            "base_model": base_model_to_save,
+            "lora_config": lora_cfg.model_dump() if hasattr(lora_cfg, "model_dump") else vars(lora_cfg),
+        }
+        with open(folder / "lora_config.json", "w", encoding="utf-8") as f:
+            json.dump(lora_info, f, indent=2, ensure_ascii=False)
     else:
         # Full finetune: save non-vae weights to model.safetensors
         state_dict = {k: v for k, v in full_state.items() if not k.startswith("audio_vae.")}
@@ -345,6 +471,29 @@ def save_checkpoint(model, optimizer, scheduler, save_dir: Path, step: int, pret
     torch.save(optimizer.state_dict(), folder / "optimizer.pth")
     torch.save(scheduler.state_dict(), folder / "scheduler.pth")
 
+    # Update (or create) a `latest` symlink pointing to the most recent checkpoint folder
+    latest_link = save_dir / "latest"
+    try:
+        if latest_link.exists() or latest_link.is_symlink():
+            # remove existing link or directory
+            if latest_link.is_dir() and not latest_link.is_symlink():
+                shutil.rmtree(latest_link)
+            else:
+                latest_link.unlink()
+        # Create a symlink pointing to the new folder
+        os.symlink(str(folder), str(latest_link))
+    except Exception:
+        # If symlink creation fails (e.g., on Windows or permission issues), fall back to copying
+        try:
+            if latest_link.exists():
+                if latest_link.is_dir():
+                    shutil.rmtree(latest_link)
+                else:
+                    latest_link.unlink()
+            shutil.copytree(folder, latest_link)
+        except Exception:
+            print(f"Warning: failed to update latest checkpoint link at {latest_link}")
+
 
 if __name__ == "__main__":
     from voxcpm.training.config import load_yaml_config
@@ -359,4 +508,3 @@ if __name__ == "__main__":
         # Otherwise use command line args (parsed by argbind)
         with argbind.scope(args):
             train()
-

src/voxcpm/core.py

@@ -55,6 +55,7 @@ class VoxCPM:
             self.denoiser = ZipEnhancer(zipenhancer_model_path)
         else:
             self.denoiser = None
+        if optimize:
             print("Warm up VoxCPMModel...")
             self.tts_model.generate(
                 target_text="Hello, this is the first test sentence.",