fixes #299

whisperlivekit/whisper/__init__.py

@@ -108,7 +108,7 @@ def available_models() -> List[str]:
 def _infer_dims_from_config(path: str) -> Optional[ModelDimensions]:
     """
     attempt to infer ModelDimensions from a HF style config.json located
-    next to the given checkpoint, usefull for distilled models
+    next to the given checkpoint, usefull for distilled models/MLX models.
     """
     candidates = []
     if os.path.isdir(path):
@@ -122,6 +122,25 @@ def _infer_dims_from_config(path: str) -> Optional[ModelDimensions]:
         with open(candidate, "r", encoding="utf-8") as f:
             config = json.load(f)
 
+        # native Whisper format
+        native_keys = ["n_mels", "n_audio_ctx", "n_audio_state", "n_audio_head",
+                       "n_audio_layer", "n_vocab", "n_text_ctx", "n_text_state",
+                       "n_text_head", "n_text_layer"]
+        if all(k in config for k in native_keys):
+            return ModelDimensions(
+                n_mels=config["n_mels"],
+                n_audio_ctx=config["n_audio_ctx"],
+                n_audio_state=config["n_audio_state"],
+                n_audio_head=config["n_audio_head"],
+                n_audio_layer=config["n_audio_layer"],
+                n_vocab=config["n_vocab"],
+                n_text_ctx=config["n_text_ctx"],
+                n_text_state=config["n_text_state"],
+                n_text_head=config["n_text_head"],
+                n_text_layer=config["n_text_layer"],
+            )
+
+        # HuggingFace format
         try:
             return ModelDimensions(
                 n_mels=config["num_mel_bins"],
@@ -236,6 +255,24 @@ def _convert_hf_state_dict(state_dict: Dict[str, torch.Tensor]) -> Dict[str, tor
     return converted if converted else state_dict
 
 
+def _convert_mlx_state_dict(state_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+    """
+    Converts an mlx whisper checkpoint to a default openai whisper one
+    """
+    if not any("mlp1" in k or "mlp2" in k for k in state_dict):
+        return state_dict
+
+    converted = {}
+    for key, value in state_dict.items():
+        if key == "alignment_heads":
+            continue
+        
+        new_key = key.replace(".mlp1.", ".mlp.0.").replace(".mlp2.", ".mlp.2.")
+        converted[new_key] = value
+
+    return converted
+
+
 def _load_lora_state(lora_path: str):
     safe_path = os.path.join(lora_path, "adapter_model.safetensors")
     bin_path = os.path.join(lora_path, "adapter_model.bin")
@@ -520,7 +557,12 @@ def load_model(
         state_dict = checkpoint["model_state_dict"]
     else:
         state_dict = checkpoint
+    
+    if alignment_heads is None and "alignment_heads" in state_dict:
+        alignment_heads = state_dict["alignment_heads"]
+    
     state_dict = _convert_hf_state_dict(state_dict)
+    state_dict = _convert_mlx_state_dict(state_dict)
     _apply_lora_adapter(state_dict, lora_path)
 
     if dims_cfg is not None:
@@ -546,8 +588,13 @@ def load_model(
     model.load_state_dict(state_dict)
 
     if alignment_heads is not None:
-        model.set_alignment_heads(alignment_heads)
-
+        if isinstance(alignment_heads, bytes):
+            model.set_alignment_heads(alignment_heads)
+        elif isinstance(alignment_heads, torch.Tensor): #for mlx whisper
+            mask = torch.zeros(dims.n_text_layer, dims.n_text_head, dtype=torch.bool)
+            for layer, head in alignment_heads.tolist():
+                mask[layer, head] = True
+            model.register_buffer("alignment_heads", mask.to_sparse(), persistent=False)
     return model.to(device)