Prepping for open source. (#80)

* Check if is cuda.

* Adding stuff.

* no more print statements

* Deleting outdated tools for release.

* Bringing back post.

* bringing back tests

* Adding preference test tools + tests

* Adding to tests.

* Adding again.

* Adding stuff from Hugo's PR.

* Fixing for tests, adding docs.

* Adding timeline notches + dividing line.

* Adjusting color.

* Make cookie name customizable.

* Version bump.

---------

Co-authored-by: pseeth <[email protected]>

audiotools/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.6.4"
+__version__ = "0.7.0"
 from .core import AudioSignal
 from .core import STFTParams
 from .core import Meter

audiotools/core/util.py

@@ -1,4 +1,5 @@
 import csv
+import glob
 import math
 import numbers
 import os
@@ -37,7 +38,12 @@ def info(audio_path: str):
     audio_path : str
         Path to audio file.
     """
-    info = torchaudio.info(str(audio_path))
+    # try default backend first, then fallback to soundfile
+    try:
+        info = torchaudio.info(str(audio_path))
+    except:  # pragma: no cover
+        info = torchaudio.backend.soundfile_backend.info(str(audio_path))
+
     if isinstance(info, tuple):  # pragma: no cover
         signal_info = info[0]
         info = Info(sample_rate=signal_info.rate, num_frames=signal_info.length)
@@ -232,7 +238,13 @@ def find_audio(folder: str, ext: List[str] = AUDIO_EXTENSIONS):
     # Take care of case where user has passed in an audio file directly
     # into one of the calling functions.
     if str(folder).endswith(tuple(ext)):
-        return [folder]
+        # if, however, there's a glob in the path, we need to
+        # return the glob, not the file.
+        if "*" in str(folder):
+            return glob.glob(str(folder), recursive=("**" in str(folder)))
+        else:
+            return [folder]
+
     files = []
     for x in ext:
         files += folder.glob(f"**/*{x}")

audiotools/ml/__init__.py

@@ -1,7 +1,5 @@
 from . import decorators
 from . import layers
-from . import tricks
 from .accelerator import Accelerator
 from .experiment import Experiment
 from .layers import BaseModel
-from .trainer import BaseTrainer

audiotools/ml/decorators.py

@@ -329,7 +329,7 @@ def decorated(*args, **kwargs):
                         v = torch.tensor([v])
                     if not torch.is_tensor(v):
                         continue
-                    if ddp_active:  # pragma: no cover
+                    if ddp_active and v.is_cuda:  # pragma: no cover
                         dist.all_reduce(v, op=op)
                     output[k] = v.detach()
                     if torch.numel(v) == 1: