diff --git a/bark/generation.py b/bark/generation.py
index 4ac165c..3b12757 100644
--- a/bark/generation.py
+++ b/bark/generation.py
@@ -14,6 +14,7 @@ import torch
 import torch.nn.functional as F
 import tqdm
 from transformers import BertTokenizer
+from huggingface_hub import hf_hub_download
 
 from .model import GPTConfig, GPT
 from .model_fine import FineGPT, FineGPTConfig
@@ -89,31 +90,64 @@ USE_SMALL_MODELS = os.environ.get("SUNO_USE_SMALL_MODELS", False)
 GLOBAL_ENABLE_MPS = os.environ.get("SUNO_ENABLE_MPS", False)
 OFFLOAD_CPU = os.environ.get("SUNO_OFFLOAD_CPU", False)
 
-REMOTE_BASE_URL = "https://dl.suno-models.io/bark/models/v0/"
+# REMOTE_BASE_URL = "https://dl.suno-models.io/bark/models/v0/"
+
+# REMOTE_MODEL_PATHS = {
+#     "text_small": {
+#         "path": os.path.join(REMOTE_BASE_URL, "text.pt"),
+#         "checksum": "b3e42bcbab23b688355cd44128c4cdd3",
+#     },
+#     "coarse_small": {
+#         "path": os.path.join(REMOTE_BASE_URL, "coarse.pt"),
+#         "checksum": "5fe964825e3b0321f9d5f3857b89194d",
+#     },
+#     "fine_small": {
+#         "path": os.path.join(REMOTE_BASE_URL, "fine.pt"),
+#         "checksum": "5428d1befe05be2ba32195496e58dc90",
+#     },
+#     "text": {
+#         "path": os.path.join(REMOTE_BASE_URL, "text_2.pt"),
+#         "checksum": "54afa89d65e318d4f5f80e8e8799026a",
+#     },
+#     "coarse": {
+#         "path": os.path.join(REMOTE_BASE_URL, "coarse_2.pt"),
+#         "checksum": "8a98094e5e3a255a5c9c0ab7efe8fd28",
+#     },
+#     "fine": {
+#         "path": os.path.join(REMOTE_BASE_URL, "fine_2.pt"),
+#         "checksum": "59d184ed44e3650774a2f0503a48a97b",
+#     },
+# }
 
 REMOTE_MODEL_PATHS = {
     "text_small": {
-        "path": os.path.join(REMOTE_BASE_URL, "text.pt"),
+        "repo_id": "reach-vb/bark-small",
+        "file_name": "text.pt",
         "checksum": "b3e42bcbab23b688355cd44128c4cdd3",
     },
     "coarse_small": {
-        "path": os.path.join(REMOTE_BASE_URL, "coarse.pt"),
+        "repo_id": "reach-vb/bark-small",
+        "file_name": "coarse.pt",
         "checksum": "5fe964825e3b0321f9d5f3857b89194d",
     },
     "fine_small": {
-        "path": os.path.join(REMOTE_BASE_URL, "fine.pt"),
+        "repo_id": "reach-vb/bark-small",
+        "file_name": "fine.pt",
         "checksum": "5428d1befe05be2ba32195496e58dc90",
     },
     "text": {
-        "path": os.path.join(REMOTE_BASE_URL, "text_2.pt"),
+        "repo_id": "reach-vb/bark",
+        "file_name": "text_2.pt",
         "checksum": "54afa89d65e318d4f5f80e8e8799026a",
     },
     "coarse": {
-        "path": os.path.join(REMOTE_BASE_URL, "coarse_2.pt"),
+        "repo_id": "reach-vb/bark",
+        "file_name": "coarse_2.pt",
         "checksum": "8a98094e5e3a255a5c9c0ab7efe8fd28",
     },
     "fine": {
-        "path": os.path.join(REMOTE_BASE_URL, "fine_2.pt"),
+        "repo_id": "reach-vb/bark-small",
+        "file_name": "fine_2.pt",
         "checksum": "59d184ed44e3650774a2f0503a48a97b",
     },
 }
@@ -165,21 +199,25 @@ def _parse_s3_filepath(s3_filepath):
     return bucket_name, rel_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
-    progress_bar = tqdm.tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True)
-    with open(to_local_path, "wb") as file:
-        for data in response.iter_content(block_size):
-            progress_bar.update(len(data))
-            file.write(data)
-    progress_bar.close()
-    if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
-        raise ValueError("ERROR, something went wrong")
+# 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
+#     progress_bar = tqdm.tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True)
+#     with open(to_local_path, "wb") as file:
+#         for data in response.iter_content(block_size):
+#             progress_bar.update(len(data))
+#             file.write(data)
+#     progress_bar.close()
+#     if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
+#         raise ValueError("ERROR, something went wrong")
 
 
+def _download(from_hf_path, file_name, to_local_path):
+    os.makedirs(CACHE_DIR, exist_ok=True)
+    hf_hub_download(repo_id=from_hf_path, filename=file_name, cache_dir=to_local_path)
+
 class InferenceContext:
     def __init__(self, benchmark=False):
         # we can't expect inputs to be the same length, so disable benchmarking by default
@@ -243,7 +281,7 @@ def _load_model(ckpt_path, device, use_small=False, model_type="text"):
         os.remove(ckpt_path)
     if not os.path.exists(ckpt_path):
         logger.info(f"{model_type} model not found, downloading into `{CACHE_DIR}`.")
-        _download(model_info["path"], ckpt_path)
+        _download(model_info["repo_id"], model_info["file_name"], ckpt_path)
     checkpoint = torch.load(ckpt_path, map_location=device)
     # this is a hack
     model_args = checkpoint["model_args"]