add fake classifier

bark/generation.py

@@ -3,7 +3,6 @@ import hashlib
 import os
 import re
 import requests
-import sys
 
 from encodec import EncodecModel
 import funcy
@@ -63,27 +62,43 @@ CUR_PATH = os.path.dirname(os.path.abspath(__file__))
 
 default_cache_dir = os.path.join(os.path.expanduser("~"), ".cache")
 CACHE_DIR = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")
-os.makedirs(CACHE_DIR, exist_ok=True)
 
 
 REMOTE_BASE_URL = "http://s3.amazonaws.com/suno-public/bark/models/v0/"
 REMOTE_MODEL_PATHS = {
-    "text": os.environ.get("SUNO_TEXT_MODEL_PATH", os.path.join(REMOTE_BASE_URL, "text_2.pt")),
+    "text": {
-    "coarse": os.environ.get(
+        "path": os.environ.get("SUNO_TEXT_MODEL_PATH", os.path.join(REMOTE_BASE_URL, "text_2.pt")),
+        "checksum": "54afa89d65e318d4f5f80e8e8799026a",
+    },
+    "coarse": {
+        "path": os.environ.get(
             "SUNO_COARSE_MODEL_PATH", os.path.join(REMOTE_BASE_URL, "coarse_2.pt")
         ),
-    "fine": os.environ.get("SUNO_FINE_MODEL_PATH", os.path.join(REMOTE_BASE_URL, "fine_2.pt")),
+        "checksum": "8a98094e5e3a255a5c9c0ab7efe8fd28",
+    },
+    "fine": {
+        "path": os.environ.get("SUNO_FINE_MODEL_PATH", os.path.join(REMOTE_BASE_URL, "fine_2.pt")),
+        "checksum": "59d184ed44e3650774a2f0503a48a97b",
+    },
 }
 
 
-def _compute_md5(s):
+def _string_md5(s):
     m = hashlib.md5()
     m.update(s.encode("utf-8"))
     return m.hexdigest()
 
 
+def _md5(fname):
+    hash_md5 = hashlib.md5()
+    with open(fname, "rb") as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            hash_md5.update(chunk)
+    return hash_md5.hexdigest()
+
+
 def _get_ckpt_path(model_type):
-    model_name = _compute_md5(REMOTE_MODEL_PATHS[model_type])
+    model_name = _string_md5(REMOTE_MODEL_PATHS[model_type]["path"])
     return os.path.join(CACHE_DIR, f"{model_name}.pt")
 
 
@@ -97,6 +112,7 @@ def _parse_s3_filepath(s3_filepath):
 
 
 def _download(from_s3_path, to_local_path):
+    os.makedirs(CACHE_DIR, exist_ok=True)
     response = requests.get(from_s3_path, stream=True)
     total_size_in_bytes = int(response.headers.get('content-length', 0))
     block_size = 1024  # 1 Kibibyte
@@ -164,9 +180,15 @@ def _load_model(ckpt_path, device, model_type="text"):
         ModelClass = FineGPT
     else:
         raise NotImplementedError()
+    if (
+        os.path.exists(ckpt_path) and
+        _md5(ckpt_path) != REMOTE_MODEL_PATHS[model_type]["checksum"]
+    ):
+        print(f"found outdated {model_type} model, removing...")
+        os.remove(ckpt_path)
     if not os.path.exists(ckpt_path):
         print(f"{model_type} model not found, downloading...")
-        _download(REMOTE_MODEL_PATHS[model_type], ckpt_path)
+        _download(REMOTE_MODEL_PATHS[model_type]["path"], ckpt_path)
     checkpoint = torch.load(ckpt_path, map_location=device)
     # this is a hack
     model_args = checkpoint["model_args"]

model-card.md

@@ -36,4 +36,5 @@ Straightforward improvements will allow models to run faster than realtime, rend
  
 While we hope that this release will enable users to express their creativity and build applications that are a force
 for good, we acknowledge that any text to audio model has the potential for dual use. While it is not straightforward
-to voice clone known people with Bark, they can still be used for nefarious purposes. 
+to voice clone known people with Bark, they can still be used for nefarious purposes. To further reduce the chances of unintended use of Bark, 
+we also release a simple classifier to detect Bark-generated audio with high accuracy (see notebooks section of the main repository).