diff --git a/test/kaldi_compatibility_impl.py b/test/kaldi_compatibility_impl.py
index 9e0ecfe1ca..f4c7986269 100644
--- a/test/kaldi_compatibility_impl.py
+++ b/test/kaldi_compatibility_impl.py
@@ -3,6 +3,7 @@
 import shutil
 import unittest
 import subprocess
+import math
 
 import kaldi_io
 import torch
@@ -107,3 +108,89 @@ def test_mfcc(self, kwargs):
         command = ['compute-mfcc-feats'] + _convert_args(**kwargs) + ['scp:-', 'ark:-']
         kaldi_result = _run_kaldi(command, 'scp', wave_file)
         self.assert_equal(result, expected=kaldi_result, rtol=1e-4, atol=1e-8)
+
+    def test_mfcc_empty(self):
+        # Passing in an empty tensor should result in an error
+        input = torch.empty(0).to(dtype=self.dtype, device=self.device)
+        self.assertRaises(AssertionError, torchaudio.compliance.kaldi.mfcc, input)
+
+    def test_resample_waveform_upsample_size(self):
+        test_8000_filepath = common_utils.get_asset_path('kaldi_file_8000.wav')
+        sound, sample_rate = torchaudio.load_wav(test_8000_filepath)
+        sound = sound.to(dtype=self.dtype, device=self.device)
+        upsample_sound = torchaudio.compliance.kaldi.resample_waveform(sound, sample_rate, sample_rate * 2)
+        self.assertTrue(upsample_sound.size(-1) == sound.size(-1) * 2)
+
+    def test_resample_waveform_downsample_size(self):
+        test_8000_filepath = common_utils.get_asset_path('kaldi_file_8000.wav')
+        sound, sample_rate = torchaudio.load_wav(test_8000_filepath)
+        sound = sound.to(dtype=self.dtype, device=self.device)
+        downsample_sound = torchaudio.compliance.kaldi.resample_waveform(sound, sample_rate, sample_rate // 2)
+        self.assertTrue(downsample_sound.size(-1) == sound.size(-1) // 2)
+
+    def test_resample_waveform_identity_size(self):
+        test_8000_filepath = common_utils.get_asset_path('kaldi_file_8000.wav')
+        sound, sample_rate = torchaudio.load_wav(test_8000_filepath)
+        sound = sound.to(dtype=self.dtype, device=self.device)
+        downsample_sound = torchaudio.compliance.kaldi.resample_waveform(sound, sample_rate, sample_rate)
+        self.assertTrue(downsample_sound.size(-1) == sound.size(-1))
+
+    def _test_resample_waveform_accuracy(self, up_scale_factor=None, down_scale_factor=None,
+                                         atol=1e-1, rtol=1e-4):
+        # resample the signal and compare it to the ground truth
+        n_to_trim = 20
+        sample_rate = 1000
+        new_sample_rate = sample_rate
+
+        if up_scale_factor is not None:
+            new_sample_rate *= up_scale_factor
+
+        if down_scale_factor is not None:
+            new_sample_rate //= down_scale_factor
+
+        duration = 5  # seconds
+        original_timestamps = torch.arange(0, duration, 1.0 / sample_rate)
+        original_timestamps = original_timestamps.to(dtype=self.dtype, device=self.device)
+
+        sound = 123 * torch.cos(2 * math.pi * 3 * original_timestamps).unsqueeze(0)
+        estimate = torchaudio.compliance.kaldi.resample_waveform(sound, sample_rate, new_sample_rate).squeeze()
+
+        new_timestamps = torch.arange(0, duration, 1.0 / new_sample_rate)[:estimate.size(0)]
+        new_timestamps = new_timestamps.to(dtype=self.dtype, device=self.device)
+        ground_truth = 123 * torch.cos(2 * math.pi * 3 * new_timestamps)
+
+        # trim the first/last n samples as these points have boundary effects
+        ground_truth = ground_truth[..., n_to_trim:-n_to_trim]
+        estimate = estimate[..., n_to_trim:-n_to_trim]
+
+        self.assert_equal(estimate, expected=ground_truth, atol=atol, rtol=rtol)
+
+    def test_resample_waveform_downsample_accuracy(self):
+        for i in range(1, 20):
+            self._test_resample_waveform_accuracy(down_scale_factor=i * 2)
+
+    def test_resample_waveform_upsample_accuracy(self):
+        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
+
+        test_8000_filepath = common_utils.get_asset_path('kaldi_file_8000.wav')
+        # (1, 8000)
+        sound, sample_rate = torchaudio.load_wav(test_8000_filepath)
+        sound = sound.to(dtype=self.dtype, device=self.device)
+        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 = torchaudio.compliance.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 = torchaudio.compliance.kaldi.resample_waveform(single_channel,
+                                                                                   sample_rate,
+                                                                                   sample_rate // 2)
+            self.assert_equal(multi_sound_sampled[i, :], expected=single_channel_sampled[0], rtol=1e-4, atol=1e-8)