Merge pull request #62 from suno-ai/test_model_control

Simplify small model and gpu/cpu choice

README.md

@@ -21,14 +21,20 @@ Bark is a transformer-based text-to-audio model created by [Suno](https://suno.a
 ## 🤖 Usage
 
 ```python
-from bark import SAMPLE_RATE, generate_audio
+from bark import SAMPLE_RATE, generate_audio, preload_models
 from IPython.display import Audio
 
+# download and load all models
+preload_models()
+
+# generate audio from text
 text_prompt = """
      Hello, my name is Suno. And, uh — and I like pizza. [laughs] 
      But I also have other interests such as playing tic tac toe.
 """
 audio_array = generate_audio(text_prompt)
+
+# play text in notebook
 Audio(audio_array, rate=SAMPLE_RATE)
 ```
 

bark/generation.py

@@ -1,4 +1,5 @@
 import contextlib
+import gc
 import hashlib
 import os
 import re
@@ -84,36 +85,33 @@ CACHE_DIR = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno",
 USE_SMALL_MODELS = os.environ.get("SUNO_USE_SMALL_MODELS", False)
 
 REMOTE_BASE_URL = "https://dl.suno-models.io/bark/models/v0/"
-if USE_SMALL_MODELS:
+
-    REMOTE_MODEL_PATHS = {
+REMOTE_MODEL_PATHS = {
-        "text": {
+    "text_small": {
-            "path": os.path.join(REMOTE_BASE_URL, "text.pt"),
+        "path": os.path.join(REMOTE_BASE_URL, "text.pt"),
-            "checksum": "b3e42bcbab23b688355cd44128c4cdd3",
+        "checksum": "b3e42bcbab23b688355cd44128c4cdd3",
-        },
+    },
-        "coarse": {
+    "coarse_small": {
-            "path": os.path.join(REMOTE_BASE_URL, "coarse.pt"),
+        "path": os.path.join(REMOTE_BASE_URL, "coarse.pt"),
-            "checksum": "5fe964825e3b0321f9d5f3857b89194d",
+        "checksum": "5fe964825e3b0321f9d5f3857b89194d",
-        },
+    },
-        "fine": {
+    "fine_small": {
-            "path": os.path.join(REMOTE_BASE_URL, "fine.pt"),
+        "path": os.path.join(REMOTE_BASE_URL, "fine.pt"),
-            "checksum": "5428d1befe05be2ba32195496e58dc90",
+        "checksum": "5428d1befe05be2ba32195496e58dc90",
-        },
+    },
-    }
+    "text": {
-else:
+        "path": os.path.join(REMOTE_BASE_URL, "text_2.pt"),
-    REMOTE_MODEL_PATHS = {
+        "checksum": "54afa89d65e318d4f5f80e8e8799026a",
-        "text": {
+    },
-            "path": os.path.join(REMOTE_BASE_URL, "text_2.pt"),
+    "coarse": {
-            "checksum": "54afa89d65e318d4f5f80e8e8799026a",
+        "path": os.path.join(REMOTE_BASE_URL, "coarse_2.pt"),
-        },
+        "checksum": "8a98094e5e3a255a5c9c0ab7efe8fd28",
-        "coarse": {
+    },
-            "path": os.path.join(REMOTE_BASE_URL, "coarse_2.pt"),
+    "fine": {
-            "checksum": "8a98094e5e3a255a5c9c0ab7efe8fd28",
+        "path": os.path.join(REMOTE_BASE_URL, "fine_2.pt"),
-        },
+        "checksum": "59d184ed44e3650774a2f0503a48a97b",
-        "fine": {
+    },
-            "path": os.path.join(REMOTE_BASE_URL, "fine_2.pt"),
+}
-            "checksum": "59d184ed44e3650774a2f0503a48a97b",
-        },
-    }
 
 
 if not hasattr(torch.nn.functional, 'scaled_dot_product_attention'):
@@ -137,8 +135,9 @@ def _md5(fname):
     return hash_md5.hexdigest()
 
 
-def _get_ckpt_path(model_type):
+def _get_ckpt_path(model_type, use_small=False):
-    model_name = _string_md5(REMOTE_MODEL_PATHS[model_type]["path"])
+    model_key = f"{model_type}_small" if use_small or USE_SMALL_MODELS else model_type
+    model_name = _string_md5(REMOTE_MODEL_PATHS[model_key]["path"])
     return os.path.join(CACHE_DIR, f"{model_name}.pt")
 
 
@@ -204,9 +203,10 @@ def clean_models(model_key=None):
         if k in models:
             del models[k]
     _clear_cuda_cache()
+    gc.collect()
 
 
-def _load_model(ckpt_path, device, model_type="text"):
+def _load_model(ckpt_path, device, use_small=False, model_type="text"):
     if "cuda" not in device:
         logger.warning("No GPU being used. Careful, inference might be extremely slow!")
     if model_type == "text":
@@ -220,15 +220,17 @@ def _load_model(ckpt_path, device, model_type="text"):
         ModelClass = FineGPT
     else:
         raise NotImplementedError()
+    model_key = f"{model_type}_small" if use_small or USE_SMALL_MODELS else model_type
+    model_info = REMOTE_MODEL_PATHS[model_key]
     if (
         os.path.exists(ckpt_path) and
-        _md5(ckpt_path) != REMOTE_MODEL_PATHS[model_type]["checksum"]
+        _md5(ckpt_path) != model_info["checksum"]
     ):
         logger.warning(f"found outdated {model_type} model, removing.")
         os.remove(ckpt_path)
     if not os.path.exists(ckpt_path):
         logger.info(f"{model_type} model not found, downloading into `{CACHE_DIR}`.")
-        _download(REMOTE_MODEL_PATHS[model_type]["path"], ckpt_path)
+        _download(model_info["path"], ckpt_path)
     checkpoint = torch.load(ckpt_path, map_location=device)
     # this is a hack
     model_args = checkpoint["model_args"]
@@ -278,8 +280,8 @@ def _load_codec_model(device):
     return model
 
 
-def load_model(ckpt_path=None, use_gpu=True, force_reload=False, model_type="text"):
+def load_model(use_gpu=True, use_small=False, force_reload=False, model_type="text"):
-    _load_model_f = funcy.partial(_load_model, model_type=model_type)
+    _load_model_f = funcy.partial(_load_model, model_type=model_type, use_small=use_small)
     if model_type not in ("text", "coarse", "fine"):
         raise NotImplementedError()
     global models
@@ -289,8 +291,7 @@ def load_model(ckpt_path=None, use_gpu=True, force_reload=False, model_type="tex
         device = "cuda"
     model_key = str(device) + f"__{model_type}"
     if model_key not in models or force_reload:
-        if ckpt_path is None:
+        ckpt_path = _get_ckpt_path(model_type, use_small=use_small)
-            ckpt_path = _get_ckpt_path(model_type)
         clean_models(model_key=model_key)
         model = _load_model_f(ckpt_path, device)
         models[model_key] = model
@@ -311,17 +312,29 @@ def load_codec_model(use_gpu=True, force_reload=False):
     return models[model_key]
 
 
-def preload_models(text_ckpt_path=None, coarse_ckpt_path=None, fine_ckpt_path=None, use_gpu=True):
+def preload_models(
+    text_use_gpu=True,
+    text_use_small=False,
+    coarse_use_gpu=True,
+    coarse_use_small=False,
+    fine_use_gpu=True,
+    fine_use_small=False,
+    codec_use_gpu=True,
+    force_reload=False,
+):
     _ = load_model(
-        ckpt_path=text_ckpt_path, model_type="text", use_gpu=use_gpu, force_reload=True
+        model_type="text", use_gpu=text_use_gpu, use_small=text_use_small, force_reload=force_reload
     )
     _ = load_model(
-        ckpt_path=coarse_ckpt_path, model_type="coarse", use_gpu=use_gpu, force_reload=True
+        model_type="coarse",
+        use_gpu=coarse_use_gpu,
+        use_small=coarse_use_small,
+        force_reload=force_reload,
     )
     _ = load_model(
-        ckpt_path=fine_ckpt_path, model_type="fine", use_gpu=use_gpu, force_reload=True
+        model_type="fine", use_gpu=fine_use_gpu, use_small=fine_use_small, force_reload=force_reload
     )
-    _ = load_codec_model(use_gpu=use_gpu, force_reload=True)
+    _ = load_codec_model(use_gpu=codec_use_gpu, force_reload=force_reload)
 
 
 ####