Whisper: Add pip distribution configuration to support pip installations. (#739)

* Whisper: rename whisper to mlx_whisper

* Whisper: add setup.py config for publish

* Whisper: add assets data to setup config

* Whisper: pre-commit for setup.py

* Whisper: Update README.md

* Whisper: Update README.md

* nits

* fix package data

* nit in readme

---------

Co-authored-by: Awni Hannun <awni@apple.com>

whisper/mlx_whisper/load_models.py

@@ -0,0 +1,43 @@
+# Copyright © 2023 Apple Inc.
+
+import json
+from pathlib import Path
+
+import mlx.core as mx
+import mlx.nn as nn
+from huggingface_hub import snapshot_download
+from mlx.utils import tree_unflatten
+
+from . import whisper
+
+
+def load_model(
+    path_or_hf_repo: str,
+    dtype: mx.Dtype = mx.float32,
+) -> whisper.Whisper:
+    model_path = Path(path_or_hf_repo)
+    if not model_path.exists():
+        model_path = Path(snapshot_download(repo_id=path_or_hf_repo))
+
+    with open(str(model_path / "config.json"), "r") as f:
+        config = json.loads(f.read())
+        config.pop("model_type", None)
+        quantization = config.pop("quantization", None)
+
+    model_args = whisper.ModelDimensions(**config)
+
+    weights = mx.load(str(model_path / "weights.npz"))
+
+    model = whisper.Whisper(model_args, dtype)
+
+    if quantization is not None:
+        class_predicate = (
+            lambda p, m: isinstance(m, (nn.Linear, nn.Embedding))
+            and f"{p}.scales" in weights
+        )
+        nn.quantize(model, **quantization, class_predicate=class_predicate)
+
+    weights = tree_unflatten(list(weights.items()))
+    model.update(weights)
+    mx.eval(model.parameters())
+    return model