Avoid loading large models into host memory when in_memory is False.

whisper/__init__.py

@@ -3,7 +3,7 @@ import io
 import os
 import urllib
 import warnings
-from typing import List, Optional, Union
+from typing import List, Optional, Union, BinaryIO
 
 import torch
 from tqdm import tqdm
@@ -51,7 +51,25 @@ _ALIGNMENT_HEADS = {
 }
 
 
-def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]:
+# hashlib.file_digest() added in Python 3.11
+if not hasattr(hashlib, 'file_digest'):
+    def _file_digest(file: BinaryIO, algo: str):
+        d = hashlib.new(algo)
+
+        while True:
+            buf = file.read(65536)
+
+            if not buf:
+                break
+
+            d.update(buf)
+
+        return d
+
+    hashlib.file_digest = _file_digest
+
+
+def _download(url: str, root: str) -> str:
     os.makedirs(root, exist_ok=True)
 
     expected_sha256 = url.split("/")[-2]
@@ -62,10 +80,9 @@ def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]:
 
     if os.path.isfile(download_target):
         with open(download_target, "rb") as f:
-            model_bytes = f.read()
+            if hashlib.file_digest(f, "sha256").hexdigest() == expected_sha256:
-        if hashlib.sha256(model_bytes).hexdigest() == expected_sha256:
+                return download_target
-            return model_bytes if in_memory else download_target
+
-        else:
             warnings.warn(
                 f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"
             )
@@ -86,13 +103,13 @@ def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]:
                 output.write(buffer)
                 loop.update(len(buffer))
 
-    model_bytes = open(download_target, "rb").read()
+    with open(download_target, "rb") as f:
-    if hashlib.sha256(model_bytes).hexdigest() != expected_sha256:
+        if hashlib.file_digest(f, "sha256").hexdigest() != expected_sha256:
             raise RuntimeError(
                 "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."
             )
 
-    return model_bytes if in_memory else download_target
+    return download_target
 
 
 def available_models() -> List[str]:
@@ -134,22 +151,25 @@ def load_model(
         download_root = os.path.join(os.getenv("XDG_CACHE_HOME", default), "whisper")
 
     if name in _MODELS:
-        checkpoint_file = _download(_MODELS[name], download_root, in_memory)
+        checkpoint_file = _download(_MODELS[name], download_root)
         alignment_heads = _ALIGNMENT_HEADS[name]
     elif os.path.isfile(name):
-        checkpoint_file = open(name, "rb").read() if in_memory else name
+        checkpoint_file = name
         alignment_heads = None
     else:
         raise RuntimeError(
             f"Model {name} not found; available models = {available_models()}"
         )
 
-    with (
+    fp = open(checkpoint_file, "rb")
-        io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")
+
-    ) as fp:
+    if in_memory:
+        with fp:
+            fp = io.BytesIO(fp.read())
+
+    with fp:
         kwargs = {"weights_only": True} if torch.__version__ >= "1.13" else {}
         checkpoint = torch.load(fp, map_location=device, **kwargs)
-    del checkpoint_file
 
     dims = ModelDimensions(**checkpoint["dims"])
     model = Whisper(dims)