Merge branch 'master' into working_branch

.codecov.yml

@@ -20,6 +20,7 @@ coverage:
     # https://codecov.readme.io/v1.0/docs/commit-status
     project:
       default:
+        informational: true
         target: 95% # specify the target coverage for each commit status
         threshold: 30% # allow this little decrease on project
         # https://github.com/codecov/support/wiki/Filtering-Branches
@@ -28,6 +29,7 @@ coverage:
     # https://github.com/codecov/support/wiki/Patch-Status
     patch:
       default:
+        informational: true
         threshold: 50% # allow this much decrease on patch
     changes: false
 

tests/unittests/audio/test_sdr.py

@@ -34,12 +34,12 @@
 Input = namedtuple("Input", ["preds", "target"])
 
 inputs_1spk = Input(
-    preds=torch.rand(4, 1, 1, 1000),
-    target=torch.rand(4, 1, 1, 1000),
+    preds=torch.rand(2, 1, 1, 500),
+    target=torch.rand(2, 1, 1, 500),
 )
 inputs_2spk = Input(
-    preds=torch.rand(4, 1, 2, 1000),
-    target=torch.rand(4, 1, 2, 1000),
+    preds=torch.rand(2, 1, 2, 500),
+    target=torch.rand(2, 1, 2, 500),
 )
 
 
@@ -52,7 +52,7 @@ def _sdr_original_batch(preds: Tensor, target: Tensor, compute_permutation: bool
     for b in range(preds.shape[0]):
         sdr_val_np, _, _, _ = bss_eval_sources(target[b], preds[b], compute_permutation)
         mss.append(sdr_val_np)
-    return torch.tensor(mss)
+    return torch.tensor(np.array(mss))
 
 
 def _average_metric(preds: Tensor, target: Tensor, metric_func: Callable) -> Tensor:

tests/unittests/audio/test_snr.py

@@ -22,20 +22,16 @@
 
 from torchmetrics.audio import SignalNoiseRatio
 from torchmetrics.functional.audio import signal_noise_ratio
-from unittests import NUM_BATCHES
 from unittests.helpers import seed_all
 from unittests.helpers.testers import MetricTester
 
 seed_all(42)
 
-TIME = 25
-
 Input = namedtuple("Input", ["preds", "target"])
 
-BATCH_SIZE = 2
 inputs = Input(
-    preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 1, TIME),
-    target=torch.rand(NUM_BATCHES, BATCH_SIZE, 1, TIME),
+    preds=torch.rand(2, 1, 1, 25),
+    target=torch.rand(2, 1, 1, 25),
 )