update finetuning pipeline and runtime device handling

Support optional ref_audio samples in finetuning and make runtime device selection explicit while keeping auto fallback behavior consistent. Also ignore the local app override file to avoid accidental commits.

Made-with: Cursor

src/voxcpm/model/voxcpm.py

@@ -44,7 +44,7 @@ from ..modules.layers.lora import apply_lora_to_named_linear_modules
 from ..modules.locdit import CfmConfig, UnifiedCFM, VoxCPMLocDiT
 from ..modules.locenc import VoxCPMLocEnc
 from ..modules.minicpm4 import MiniCPM4Config, MiniCPMModel
-from .utils import get_dtype, mask_multichar_chinese_tokens
+from .utils import get_dtype, mask_multichar_chinese_tokens, resolve_runtime_device
 
 
 class VoxCPMEncoderConfig(BaseModel):
@@ -109,18 +109,15 @@ class VoxCPMModel(nn.Module):
         tokenizer: LlamaTokenizerFast,
         audio_vae: AudioVAE,
         lora_config: LoRAConfig = None,
+        device: str | None = None,
     ):
         super().__init__()
         self.config = config
         self.lora_config = lora_config
         self.feat_dim = config.feat_dim
         self.patch_size = config.patch_size
-        self.device = config.device
-        if not torch.cuda.is_available():
-            if torch.backends.mps.is_available():
-                self.device = "mps"
-            else:
-                self.device = "cpu"
+        self.device = resolve_runtime_device(device, config.device)
+        self.config.device = self.device
         print(f"Running on device: {self.device}, dtype: {self.config.dtype}", file=sys.stderr)
 
         # Text-Semantic LM
@@ -847,7 +844,14 @@ class VoxCPMModel(nn.Module):
             yield feat_pred, pred_feat_seq.squeeze(0).cpu()
 
     @classmethod
-    def from_local(cls, path: str, optimize: bool = True, training: bool = False, lora_config: LoRAConfig = None):
+    def from_local(
+        cls,
+        path: str,
+        optimize: bool = True,
+        training: bool = False,
+        device: str | None = None,
+        lora_config: LoRAConfig = None,
+    ):
         config = VoxCPMConfig.model_validate_json(open(os.path.join(path, "config.json")).read())
         tokenizer = LlamaTokenizerFast.from_pretrained(path)
         audio_vae_config = getattr(config, "audio_vae_config", None)
@@ -870,7 +874,7 @@ class VoxCPMModel(nn.Module):
             raise FileNotFoundError(
                 f"AudioVAE checkpoint not found. Expected either {audiovae_safetensors_path} or {audiovae_pth_path}"
             )
-        model = cls(config, tokenizer, audio_vae, lora_config)
+        model = cls(config, tokenizer, audio_vae, lora_config, device=device)
         if not training:
             lm_dtype = get_dtype(model.config.dtype)
             model = model.to(lm_dtype)