diff --git a/src/torchaudio/functional/functional.py b/src/torchaudio/functional/functional.py
index 75bd6c57eb..295e915c9b 100644
--- a/src/torchaudio/functional/functional.py
+++ b/src/torchaudio/functional/functional.py
@@ -275,8 +275,10 @@ def griffinlim(
     and *Signal estimation from modified short-time Fourier transform* :cite:`1172092`.
 
     Args:
-        specgram (Tensor): A magnitude-only STFT spectrogram of dimension `(..., freq, frames)`
-            where freq is ``n_fft // 2 + 1``.
+        specgram (Tensor):
+            A STFT spectrogram of dimension `(..., freq, frames)` where freq is ``n_fft // 2 + 1``.
+            If magnitude-only (non-complex dtype), then either random init or zero init.
+            If magnitude and phase (complex dtype), then phase used as init.
         window (Tensor): Window tensor that is applied/multiplied to each frame/window
         n_fft (int): Size of FFT, creates ``n_fft // 2 + 1`` bins
         hop_length (int): Length of hop between STFT windows. (
@@ -306,10 +308,19 @@ def griffinlim(
     specgram = specgram.pow(1 / power)
 
     # initialize the phase
-    if rand_init:
-        angles = torch.rand(specgram.size(), dtype=_get_complex_dtype(specgram.dtype), device=specgram.device)
+    if torch.is_complex(specgram) and rand_init:
+        raise ValueError("Cannot choose between given phase init and random phase init. Either give complex spectrogram input or rand_init True with non-complex spectrogram.")
+    elif torch.is_complex(specgram) and not rand_init:
+        # Use current phase as init
+        angles = torch.angle(specgram)
+        angles = torch.polar(abs=torch.ones_like(angles), angle=angles) # angles should be complex dtype
+        specgram = torch.abs(specgram)
     else:
-        angles = torch.full(specgram.size(), 1, dtype=_get_complex_dtype(specgram.dtype), device=specgram.device)
+        # Either random phase init or zero phase init
+        if rand_init:
+            angles = torch.rand(specgram.size(), dtype=_get_complex_dtype(specgram.dtype), device=specgram.device)
+        else:
+            angles = torch.full(specgram.size(), 1, dtype=_get_complex_dtype(specgram.dtype), device=specgram.device)
 
     # And initialize the previous iterate to 0
     tprev = torch.tensor(0.0, dtype=specgram.dtype, device=specgram.device)
diff --git a/src/torchaudio/transforms/_transforms.py b/src/torchaudio/transforms/_transforms.py
index 36f7ecc1cc..c587fd3178 100644
--- a/src/torchaudio/transforms/_transforms.py
+++ b/src/torchaudio/transforms/_transforms.py
@@ -277,8 +277,9 @@ def forward(self, specgram: Tensor) -> Tensor:
         r"""
         Args:
             specgram (Tensor):
-                A magnitude-only STFT spectrogram of dimension (..., freq, frames)
-                where freq is ``n_fft // 2 + 1``.
+                A STFT spectrogram of dimension (..., freq, frames) where freq is ``n_fft // 2 + 1``.
+                If magnitude-only (non-complex dtype), then either random init or zero init.
+                If magnitude and phase (complex dtype), then phase used as init.
 
         Returns:
             Tensor: waveform of (..., time), where time equals the ``length`` parameter if given.