Multichannel Resample (#154)

test/compliance/test_kaldi.py

@@ -267,6 +267,22 @@ class Test_Kaldi(unittest.TestCase):
         for i in range(1, 20):
             self._test_resample_waveform_accuracy(up_scale_factor=1.0 + i / 20.0)
 
+    def test_resample_waveform_multi_channel(self):
+        num_channels = 3
+
+        sound, sample_rate = torchaudio.load_wav(self.test_8000_filepath)  # (1, 8000)
+        multi_sound = sound.repeat(num_channels, 1)  # (num_channels, 8000)
+
+        for i in range(num_channels):
+            multi_sound[i, :] *= (i + 1) * 1.5
+
+        multi_sound_sampled = kaldi.resample_waveform(multi_sound, sample_rate, sample_rate // 2)
+
+        # check that sampling is same whether using separately or in a tensor of size (c, n)
+        for i in range(num_channels):
+            single_channel = sound * (i + 1) * 1.5
+            single_channel_sampled = kaldi.resample_waveform(single_channel, sample_rate, sample_rate // 2)
+            self.assertTrue(torch.allclose(multi_sound_sampled[i, :], single_channel_sampled, rtol=1e-4))
 
 if __name__ == '__main__':
     unittest.main()

torchaudio/compliance/kaldi.py

@@ -711,7 +711,8 @@ def resample_waveform(wave, orig_freq, new_freq, lowpass_filter_width=6):
             wave_to_conv = torch.nn.functional.pad(wave_to_conv, (left_padding, right_padding))
 
         conv_wave = torch.nn.functional.conv1d(
-            wave_to_conv.unsqueeze(0), weights[i].view(1, 1, window_size), stride=conv_stride)
+            wave_to_conv.unsqueeze(0), weights[i].repeat(num_channels, 1, 1),
+            stride=conv_stride, groups=num_channels)
 
         # we want conv_wave[:, i] to be at output[:, i + n*conv_transpose_stride]
         dilated_conv_wave = torch.nn.functional.conv_transpose1d(