diff --git a/spleeter/commands/separate.py b/spleeter/commands/separate.py
index df88229..2fbbfee 100644
--- a/spleeter/commands/separate.py
+++ b/spleeter/commands/separate.py
@@ -11,20 +11,8 @@
         -i /path/to/audio1.wav /path/to/audio2.mp3
 """
 
-from multiprocessing import Pool
-from os.path import isabs, join, split, splitext
-from tempfile import gettempdir
-
-# pylint: disable=import-error
-import tensorflow as tf
-import numpy as np
-# pylint: enable=import-error
-
 from ..audio.adapter import get_audio_adapter
-from ..audio.convertor import to_n_channels
 from ..separator import Separator
-from ..utils.estimator import create_estimator
-from ..utils.tensor import set_tensor_shape
 
 __email__ = 'research@deezer.com'
 __author__ = 'Deezer Research'
diff --git a/spleeter/model/provider/github.py b/spleeter/model/provider/github.py
index 3afecd1..2f6d7a3 100644
--- a/spleeter/model/provider/github.py
+++ b/spleeter/model/provider/github.py
@@ -14,10 +14,10 @@
     >>> provider.download('2stems', '/path/to/local/storage')
 """
 
+import hashlib
 import tarfile
 
-from os import environ
-from tempfile import TemporaryFile
+from tempfile import NamedTemporaryFile
 
 import requests
 
@@ -34,6 +34,7 @@ class GithubModelProvider(ModelProvider):
 
     LATEST_RELEASE = 'v1.4.0'
     RELEASE_PATH = 'releases/download'
+    CHECKSUM_INDEX = 'checksum.json'
 
     def __init__(self, host, repository, release):
         """ Default constructor.
@@ -46,6 +47,33 @@ class GithubModelProvider(ModelProvider):
         self._repository = repository
         self._release = release
 
+    def checksum(self, name, path):
+        """ Computes given path file sha256 and compares it to reference index
+        from release. Raise an exception if not matching.
+
+        :param name: Name of the model to compute checksum for.
+        :param path: Path of the file to compare checksum with.
+        :raise ValueError: If the given model name is not indexed.
+        :raise IOerror: if checksum is not valid or index cannot be downloaded.
+        """
+        url = '{}/{}/{}/{}/{}'.format(
+            self._host,
+            self._repository,
+            self.RELEASE_PATH,
+            self._release,
+            self.CHECKSUM_INDEX)
+        response = requests.get(url)
+        response.raise_for_status()
+        index = response.json()
+        if name not in index:
+            raise ValueError('No checksum for model {}'.format(name))
+        sha256 = hashlib.sha256()
+        with open(path, 'rb') as stream:
+            for chunk in iter(lambda: stream.read(4096), b''):
+                sha256.update(chunk)
+        if sha256.hexdigest() != index[name]:
+            raise IOError('Downloaded file is corrupted, please retry')
+
     def download(self, name, path):
         """ Download model denoted by the given name to disk.
 
@@ -60,17 +88,17 @@ class GithubModelProvider(ModelProvider):
             name)
         get_logger().info('Downloading model archive %s', url)
         with requests.get(url, stream=True) as response:
-            # Note: check for error logging here or upstream ?
             response.raise_for_status()
-            with TemporaryFile() as stream:
+            archive = NamedTemporaryFile(delete=False)
+            with archive as stream:
                 # Note: check for chunk size parameters ?
                 for chunk in response.iter_content(chunk_size=8192):
                     if chunk:
                         stream.write(chunk)
-                get_logger().info('Extracting downloaded %s archive', name)
-                stream.seek(0)
-                tar = tarfile.open(fileobj=stream)
-                tar.extractall(path=path)
-                tar.close()
-            # TODO: perform checksum control
+            get_logger().info('Validating archive checksum')
+            self.checksum(name, archive.name)
+            get_logger().info('Extracting downloaded %s archive', name)
+            tar = tarfile.open(name=archive.name)
+            tar.extractall(path=path)
+            tar.close()
         get_logger().info('%s model file(s) extracted', name)