adjust ckpt_path param

config.json

@@ -4,7 +4,7 @@
     "AutoConfig": "model.INF5Config",
     "AutoModel": "model.INF5Model"
   },
-  "ckpt_path": "checkpoints/model_best.pt",
+  "ckpt_path": "",
   "model_type": "inf5",
   "remove_sil": true,
   "speed": 1.0,

model.py

@@ -24,7 +24,10 @@ import os
 class INF5Config(PretrainedConfig):
     model_type = "inf5"
 
-    def __init__(self, ckpt_path: str = "checkpoints/model_best.pt", vocab_path: str = "checkpoints/vocab.txt", 
+    def __init__(self, 
+                 ckpt_path: str = "",
+                #  ckpt_path: str = "checkpoints/model_best.pt",
+                 vocab_path: str = "checkpoints/vocab.txt", 
                  speed: float = 1.0, remove_sil: bool = True, device: str = "", **kwargs):
         super().__init__(**kwargs)
         self.ckpt_path = ckpt_path
@@ -54,6 +57,7 @@ class INF5Model(PreTrainedModel):
         self.ema_model = torch.compile(load_model(
                 DiT,
                 dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4),
+                config.ckpt_path,
                 mel_spec_type="vocos",
                 vocab_file=vocab_path,
                 device=self._device
@@ -148,12 +152,12 @@ if __name__ == '__main__':
 
     from huggingface_hub import HfApi
 
-    repo_id = "svp19/INF5"  # Change to your HF repo
+    repo_id = "hareeshbabu82/TeluguIndicF5"  # Change to your HF repo
 
     # Upload model directory to HF
     api = HfApi()
     api.upload_folder(
-        folder_path="INF5",
+        # folder_path="INF5",
         repo_id=repo_id,
         repo_type="model"
     )