Fix whipser conversion for safetensors models (#935)

* fix whipser conversion for safetensor only. error in mlx lm for existing paths

* fix tests

whisper/convert.py

@@ -163,7 +163,12 @@ def load_torch_weights_and_config(
 
         name_or_path = snapshot_download(
             repo_id=name_or_path,
-            allow_patterns=["*.json", "pytorch_model.bin", "*.txt"],
+            allow_patterns=[
+                "*.json",
+                "pytorch_model.bin",
+                "model.safetensors",
+                "*.txt",
+            ],
         )
     else:
         raise RuntimeError(
@@ -176,10 +181,11 @@ def load_torch_weights_and_config(
         weights, config = checkpoint["model_state_dict"], checkpoint["dims"]
     else:
         name_or_path = Path(name_or_path)
-        weights = torch.load(
-            name_or_path / "pytorch_model.bin",
-            map_location="cpu",
-        )
+        pt_path = name_or_path / "pytorch_model.bin"
+        if pt_path.is_file():
+            weights = torch.load(pt_path, map_location="cpu")
+        else:
+            weights = mx.load(str(name_or_path / "model.safetensors"))
         with open(name_or_path / "config.json", "r") as fp:
             config = json.load(fp)
         weights, config = hf_to_pt(weights, config)
@@ -230,7 +236,9 @@ def convert(name_or_path: str, dtype: mx.Dtype = mx.float16):
         key = key.replace("mlp.2", "mlp2")
         if "conv" in key and value.ndim == 3:
             value = value.swapaxes(1, 2)
-        return key, mx.array(value.detach()).astype(dtype)
+        if isinstance(value, torch.Tensor):
+            value = mx.array(value.detach())
+        return key, value.astype(dtype)
 
     weights, config, alignment_heads = load_torch_weights_and_config(name_or_path)
     weights.pop("encoder.positional_embedding", None)
@@ -262,12 +270,16 @@ This model was converted to MLX format from [`{torch_name_or_path}`]().
 
 ## Use with mlx
 ```bash
-git clone https://github.com/ml-explore/mlx-examples.git
-cd mlx-examples/whisper/
-pip install -r requirements.txt
+pip install mlx-whisper
+```
 
->> import whisper
->> whisper.transcribe("FILE_NAME")
+```python
+import mlx_whisper
+
+result = mlx_whisper.transcribe(
+    "FILE_NAME",
+    path_or_hf_repo={repo_id},
+)
 ```
 """
     card = ModelCard(text)