Adding PyPI files

Author: zqevans

Diff for: README.md

@@ -1,2 +1,2 @@
-# harmonai
+# harmonai-tools
 Generative models for conditional audio generation

Diff for: harmonai_tools/inference/generation.py

@@ -6,13 +6,13 @@
 
 from einops import rearrange
 
-from ..models.diffusion import ConditionedDiffusionModelWrapper
+#from ..models.diffusion import ConditionedDiffusionModelWrapper
 from .sampling import sample, sample_k, variation_k
 from ..data.utils import PadCrop
 
 
 def generate_diffusion_cond(
-        model: ConditionedDiffusionModelWrapper,
+        model,
         steps: int = 250,
         cfg_scale=6,
         conditioning: dict = None,

Diff for: harmonai_tools/models/diffusion.py

@@ -11,6 +11,7 @@
 from .factory import create_pretransform_from_config
 from .conditioners import MultiConditioner, create_multi_conditioner_from_conditioning_config
 from .pretransforms import Pretransform
+from ..inference.generation import generate_diffusion_cond
 
 from audio_diffusion_pytorch.modules import UNetCFG1d
 
@@ -71,8 +72,8 @@ def forward(self,
                 cross_attn_masks: torch.Tensor = None,
                 input_concat_cond: torch.Tensor = None,
                 global_embed: torch.Tensor = None,
-                cfg_scale: float = 6.0,
-                cfg_dropout_prob: float = 0.1,
+                cfg_scale: float = 1.0,
+                cfg_dropout_prob: float = 0.0,
                 batch_cfg: bool = False,
                 scale_cfg: bool = False,
                 **kwargs):
@@ -113,12 +114,11 @@ def get_conditioning_inputs(self, cond: tp.Dict[str, tp.Any]):
         }
 
     def forward(self, x: torch.Tensor, t: torch.Tensor, cond: tp.Dict[str, tp.Any], **kwargs):
-        p = Profiler()
-        p.tick("start")
         outputs = self.model(x, t, **self.get_conditioning_inputs(cond), **kwargs)
-        p.tick("Model forward with conditioning")
-        #print(f"ConditionedDiffusionModelWrapper forward: {p}")
         return outputs
+    
+    def generate(self, *args, **kwargs):
+        return generate_diffusion_cond(self, *args, **kwargs)
 
 class UNetCFG1DWrapper(ConditionedDiffusionModel):
     def __init__(

Diff for: pyproject.toml

@@ -0,0 +1,3 @@
+[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"

Diff for: setup.py

@@ -2,9 +2,10 @@
 
 setup(
     name='harmonai-tools',
-    version='1.0.0',
+    version='0.0.1',
     url='https://github.com/Harmonai-org/harmonai-tools.git',
     author='Harmonai',
+    description='Training and inference tools for generative audio models from Harmonai',
     packages=find_packages(),  
     install_requires=[
         'aeiou',