Update VAD docstring and check for input shape length (#1513)

* Update VAD docstring and check for input shape length

* Update docstring in forward for transform

* Address review feedback: merge tests, update wording

test/torchaudio_unittest/transforms/sox_compatibility_test.py

+import warnings
+
+import torch
 import torchaudio.transforms as T
 from parameterized import parameterized
 
@@ -61,3 +64,25 @@ class TestFunctionalFiltering(TempDirMixin, TorchaudioTestCase):
         data, sample_rate = load_wav(path)
         result = T.Vad(sample_rate)(data)
         self.assert_sox_effect(result, path, ['vad'])
+
+    def test_vad_warning(self):
+        """vad should throw a warning if input dimension is greater than 2"""
+        sample_rate = 41100
+
+        data = torch.rand(5, 5, sample_rate)
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            T.Vad(sample_rate)(data)
+        assert len(w) == 1
+
+        data = torch.rand(5, sample_rate)
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            T.Vad(sample_rate)(data)
+        assert len(w) == 0
+
+        data = torch.rand(sample_rate)
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            T.Vad(sample_rate)(data)
+        assert len(w) == 0

torchaudio/functional/filtering.py

 import math
+import warnings
 from typing import Optional
 
 import torch
@@ -1374,7 +1375,10 @@ def vad(
     so in order to trim from the back, the reverse effect must also be used.
 
     Args:
-        waveform (Tensor): Tensor of audio of dimension `(..., time)`
+        waveform (Tensor): Tensor of audio of dimension `(channels, time)` or `(time)`
+            Tensor of shape `(channels, time)` is treated as a multi-channel recording
+            of the same event and the resulting output will be trimmed to the earliest
+            voice activity in any channel.
         sample_rate (int): Sample rate of audio signal.
         trigger_level (float, optional): The measurement level used to trigger activity detection.
             This may need to be cahnged depending on the noise level, signal level,
@@ -1420,6 +1424,15 @@ def vad(
         http://sox.sourceforge.net/sox.html
     """
 
+    if waveform.ndim > 2:
+        warnings.warn(
+            "Expected input tensor dimension of 1 for single channel"
+            f" or 2 for multi-channel. Got {waveform.ndim} instead. "
+            "Batch semantics is not supported. "
+            "Please refer to https://github.com/pytorch/audio/issues/1348"
+            " and https://github.com/pytorch/audio/issues/1468."
+        )
+
     measure_duration: float = (
         2.0 / measure_freq if measure_duration is None else measure_duration
     )

torchaudio/transforms.py

@@ -1126,7 +1126,10 @@ class Vad(torch.nn.Module):
     def forward(self, waveform: Tensor) -> Tensor:
         r"""
         Args:
-            waveform (Tensor): Tensor of audio of dimension `(..., time)`
+            waveform (Tensor): Tensor of audio of dimension `(channels, time)` or `(time)`
+                Tensor of shape `(channels, time)` is treated as a multi-channel recording
+                of the same event and the resulting output will be trimmed to the earliest
+                voice activity in any channel.
         """
         return F.vad(
             waveform=waveform,