diff --git a/test/test_transforms.py b/test/test_transforms.py
index d78c5b6000..a438c4481c 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -313,6 +313,18 @@ def test_compute_deltas_twochannel(self):
         computed = transform(specgram)
         self.assertTrue(computed.shape == specgram.shape, (computed.shape, specgram.shape))
 
+    def test_batch_spectrogram(self):
+        waveform, sample_rate = torchaudio.load(self.test_filepath)
+
+        # Single then transform then batch
+        expected = transforms.Spectrogram()(waveform).repeat(3, 1, 1, 1)
+
+        # Batch then transform
+        computed = transforms.Spectrogram()(waveform.repeat(3, 1, 1))
+
+        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
+        self.assertTrue(torch.allclose(computed, expected))
+
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/torchaudio/functional.py b/torchaudio/functional.py
index 7539f371d4..4397ddd51b 100644
--- a/torchaudio/functional.py
+++ b/torchaudio/functional.py
@@ -96,8 +96,7 @@ def istft(
 
     Args:
         stft_matrix (torch.Tensor): Output of stft where each row of a channel is a frequency and each
-            column is a window. it has a size of either (channel, fft_size, n_frame, 2) or (
-            fft_size, n_frame, 2)
+            column is a window. it has a size of either (..., fft_size, n_frame, 2)
         n_fft (int): Size of Fourier transform
         hop_length (Optional[int]): The distance between neighboring sliding window frames.
             (Default: ``win_length // 4``)
@@ -218,14 +217,15 @@ def istft(
 def spectrogram(
     waveform, pad, window, n_fft, hop_length, win_length, power, normalized
 ):
-    # type: (Tensor, int, Tensor, int, int, int, int, bool) -> Tensor
+    # type: (Tensor, int, Tensor, int, int, int, Optional[int], bool) -> Tensor
     r"""
     spectrogram(waveform, pad, window, n_fft, hop_length, win_length, power, normalized)
 
-    Create a spectrogram from a raw audio signal.
+    Create a spectrogram or a batch of spectrograms from a raw audio signal.
+    The spectrogram can be either magnitude-only or complex.
 
     Args:
-        waveform (torch.Tensor): Tensor of audio of dimension (channel, time)
+        waveform (torch.Tensor): Tensor of audio of dimension (..., channel, time)
         pad (int): Two sided padding of signal
         window (torch.Tensor): Window tensor that is applied/multiplied to each frame/window
         n_fft (int): Size of FFT
@@ -233,27 +233,36 @@ def spectrogram(
         win_length (int): Window size
         power (int): Exponent for the magnitude spectrogram,
             (must be > 0) e.g., 1 for energy, 2 for power, etc.
+            If None, then the complex spectrum is returned instead.
         normalized (bool): Whether to normalize by magnitude after stft
 
     Returns:
-        torch.Tensor: Dimension (channel, freq, time), where channel
+        torch.Tensor: Dimension (..., channel, freq, time), where channel
         is unchanged, freq is ``n_fft // 2 + 1`` and ``n_fft`` is the number of
         Fourier bins, and time is the number of window hops (n_frame).
     """
-    assert waveform.dim() == 2
 
     if pad > 0:
         # TODO add "with torch.no_grad():" back when JIT supports it
         waveform = torch.nn.functional.pad(waveform, (pad, pad), "constant")
 
+    # pack batch
+    shape = waveform.size()
+    waveform = waveform.reshape(-1, shape[-1])
+
     # default values are consistent with librosa.core.spectrum._spectrogram
     spec_f = _stft(
         waveform, n_fft, hop_length, win_length, window, True, "reflect", False, True
     )
 
+    # unpack batch
+    spec_f = spec_f.reshape(shape[:-1] + spec_f.shape[-3:])
+
     if normalized:
         spec_f /= window.pow(2).sum().sqrt()
-    spec_f = spec_f.pow(power).sum(-1)  # get power of "complex" tensor
+    if power is not None:
+        spec_f = spec_f.pow(power).sum(-1)  # get power of "complex" tensor
+
     return spec_f
 
 
@@ -431,11 +440,11 @@ def complex_norm(complex_tensor, power=1.0):
     r"""Compute the norm of complex tensor input.
 
     Args:
-        complex_tensor (torch.Tensor): Tensor shape of `(*, complex=2)`
+        complex_tensor (torch.Tensor): Tensor shape of `(..., complex=2)`
         power (float): Power of the norm. (Default: `1.0`).
 
     Returns:
-        torch.Tensor: Power of the normed input tensor. Shape of `(*, )`
+        torch.Tensor: Power of the normed input tensor. Shape of `(..., )`
     """
     if power == 1.0:
         return torch.norm(complex_tensor, 2, -1)
@@ -448,10 +457,10 @@ def angle(complex_tensor):
     r"""Compute the angle of complex tensor input.
 
     Args:
-        complex_tensor (torch.Tensor): Tensor shape of `(*, complex=2)`
+        complex_tensor (torch.Tensor): Tensor shape of `(..., complex=2)`
 
     Return:
-        torch.Tensor: Angle of a complex tensor. Shape of `(*, )`
+        torch.Tensor: Angle of a complex tensor. Shape of `(..., )`
     """
     return torch.atan2(complex_tensor[..., 1], complex_tensor[..., 0])
 
@@ -459,10 +468,10 @@ def angle(complex_tensor):
 @torch.jit.script
 def magphase(complex_tensor, power=1.0):
     # type: (Tensor, float) -> Tuple[Tensor, Tensor]
-    r"""Separate a complex-valued spectrogram with shape `(*, 2)` into its magnitude and phase.
+    r"""Separate a complex-valued spectrogram with shape `(..., 2)` into its magnitude and phase.
 
     Args:
-        complex_tensor (torch.Tensor): Tensor shape of `(*, complex=2)`
+        complex_tensor (torch.Tensor): Tensor shape of `(..., complex=2)`
         power (float): Power of the norm. (Default: `1.0`)
 
     Returns: