diff --git a/aten/src/ATen/native/SpectralOps.cpp b/aten/src/ATen/native/SpectralOps.cpp
index b612d37e123..c1784ef8b45 100644
--- a/aten/src/ATen/native/SpectralOps.cpp
+++ b/aten/src/ATen/native/SpectralOps.cpp
@@ -826,7 +826,7 @@ static Stream& write_opt(Stream& SS, const std::optional<T>& value) {
 Tensor stft(const Tensor& self, const int64_t n_fft, const std::optional<int64_t> hop_lengthOpt,
             const std::optional<int64_t> win_lengthOpt, const std::optional<Tensor>& window_opt,
             const bool center, std::string_view mode, const bool normalized,
-            const std::optional<bool> onesidedOpt, const std::optional<bool> return_complexOpt) {
+            const std::optional<bool> onesidedOpt, const std::optional<bool> return_complexOpt, const std::optional<bool> align_to_windowOpt) {
   // See [Note: hacky wrapper removal for optional tensor]
   c10::MaybeOwned<Tensor> window_maybe_owned = at::borrow_from_optional_tensor(window_opt);
   const Tensor& window = *window_maybe_owned;
@@ -853,11 +853,14 @@ Tensor stft(const Tensor& self, const int64_t n_fft, const std::optional<int64_t
     } \
     SS << ", normalized=" << normalized << ", onesided="; \
     write_opt(SS, onesidedOpt) << ", return_complex="; \
-    write_opt(SS, return_complexOpt) << ") "
+    write_opt(SS, return_complexOpt) << ", align_to_window="; \
+    write_opt(SS, align_to_windowOpt) << ") "
 
   TORCH_CHECK(!window.defined() || window.device() == self.device(),
               "stft input and window must be on the same device but got self on ",
               self.device(), " and window on ", window.device())
+  TORCH_CHECK(!center || !align_to_windowOpt.has_value(),
+          "stft align_to_window should only be set when center = false.")
 
   // default_init hop_length and win_length
   auto hop_length = hop_lengthOpt.value_or(n_fft >> 2);
@@ -869,7 +872,6 @@ Tensor stft(const Tensor& self, const int64_t n_fft, const std::optional<int64_t
         "stft requires the return_complex parameter be given for real inputs, "
         "and will further require that return_complex=True in a future PyTorch release.");
 
-
     TORCH_WARN_ONCE(
         "stft with return_complex=False is deprecated. In a future pytorch "
         "release, stft will return complex tensors for all inputs, and "
@@ -943,7 +945,17 @@ Tensor stft(const Tensor& self, const int64_t n_fft, const std::optional<int64_t
       window_.narrow(0, left, win_length).fill_(1);
     }
   }
-  int64_t n_frames = 1 + (len - n_fft) / hop_length;
+
+  const bool align_to_window = align_to_windowOpt.value_or(false);
+  int64_t n_frames;
+  if (!center && align_to_window) {
+    // Calculate n_frames based on window length, since we are aligning start of window with t = 0.
+    n_frames = 1 + (len - win_length) / hop_length;
+    // Window-based padding.
+    input = at::pad(input, {(n_fft - win_length) / 2, (n_fft - win_length) / 2}, mode);
+  } else {
+    n_frames = 1 + (len - n_fft) / hop_length;
+  }
   // time2col
   input = input.as_strided(
     {batch, n_frames, n_fft},
@@ -982,11 +994,12 @@ Tensor stft(
     const Tensor& self, const int64_t n_fft, const std::optional<int64_t> hop_lengthOpt,
     const std::optional<int64_t> win_lengthOpt, const std::optional<Tensor>& window_opt,
     const bool normalized,
-    const std::optional<bool> onesidedOpt, const std::optional<bool> return_complexOpt) {
+    const std::optional<bool> onesidedOpt, const std::optional<bool> return_complexOpt,
+    const std::optional<bool> align_to_windowOpt) {
   return at::stft(
       self, n_fft, hop_lengthOpt, win_lengthOpt, window_opt,
       /*center=*/false, /*mode=*/"constant", normalized, onesidedOpt,
-      return_complexOpt);
+      return_complexOpt, align_to_windowOpt);
 }
 
 // Create complex tensor from the old style of real tensor with size=(..., 2)
diff --git a/aten/src/ATen/native/native_functions.yaml b/aten/src/ATen/native/native_functions.yaml
index e412752a1dc..4eb313cf5bd 100644
--- a/aten/src/ATen/native/native_functions.yaml
+++ b/aten/src/ATen/native/native_functions.yaml
@@ -5758,11 +5758,11 @@
 - func: dstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
 
 # Overload without center & pad mode, needed for forward-compatibility
-- func: stft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor
+- func: stft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None, bool? align_to_window=None) -> Tensor
   variants: function, method
   cpp_no_default_args: ['hop_length', 'win_length', 'window', 'normalized']
 
-- func: stft.center(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, str pad_mode="reflect", bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor
+- func: stft.center(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, str pad_mode="reflect", bool normalized=False, bool? onesided=None, bool? return_complex=None, bool? align_to_window=None) -> Tensor
   variants: function, method
 
 - func: istft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, bool normalized=False, bool? onesided=None, int? length=None, bool return_complex=False) -> Tensor
diff --git a/test/test_spectral_ops.py b/test/test_spectral_ops.py
index 76f128f4369..154c36832f7 100644
--- a/test/test_spectral_ops.py
+++ b/test/test_spectral_ops.py
@@ -1226,6 +1226,14 @@ class TestFFT(TestCase):
         with self.assertRaisesRegex(RuntimeError, 'stft requires the return_complex parameter'):
             y = x.stft(10, pad_mode='constant')
 
+    @onlyNativeDeviceTypes
+    @skipCPUIfNoFFT
+    def test_stft_align_to_window_only_requires_non_center(self, device):
+        x = torch.rand(100)
+        for align_to_window in [True, False]:
+            with self.assertRaisesRegex(RuntimeError, 'stft align_to_window should only be set when center = false'):
+                y = x.stft(10, center=True, return_complex=True, align_to_window=align_to_window)
+
     # stft and istft are currently warning if a window is not provided
     @onlyNativeDeviceTypes
     @skipCPUIfNoFFT
diff --git a/torch/_refs/__init__.py b/torch/_refs/__init__.py
index 200039ce81a..3db1dad6c59 100644
--- a/torch/_refs/__init__.py
+++ b/torch/_refs/__init__.py
@@ -3362,6 +3362,7 @@ def stft(
     normalized: bool = False,
     onesided: Optional[bool] = None,
     return_complex: Optional[bool] = None,
+    align_to_window: Optional[bool] = None,
 ) -> Tensor:
     torch._check(
         window is None or window.device == input.device,
@@ -3370,6 +3371,10 @@ def stft(
             + f" and window on {window.device}"  # type: ignore[union-attr]
         ),
     )
+    torch._check(
+        not center or align_to_window is None,
+        "stft only supports align_to_window for center = False.",
+    )
 
     hop_length_ = hop_length if hop_length is not None else n_fft // 4
     win_length_ = win_length if win_length is not None else n_fft
@@ -3433,6 +3438,9 @@ def stft(
         window = aten.constant_pad_nd(window, [left, n_fft - win_length_ - left])
 
     input = input.unfold(dimension=-1, size=n_fft, step=hop_length_)
+    if not center and align_to_window:
+        input_pad_amount = (n_fft - win_length_) // 2
+        input = aten.pad(input, [input_pad_amount, input_pad_amount], pad_mode)
     if window is not None:
         input = input * window
 
diff --git a/torch/_tensor.py b/torch/_tensor.py
index f33951e0488..1af81357cd6 100644
--- a/torch/_tensor.py
+++ b/torch/_tensor.py
@@ -940,6 +940,7 @@ class Tensor(torch._C.TensorBase):
         normalized: bool = False,
         onesided: Optional[bool] = None,
         return_complex: Optional[bool] = None,
+        align_to_window: Optional[bool] = None,
     ):
         r"""See :func:`torch.stft`
 
@@ -961,6 +962,7 @@ class Tensor(torch._C.TensorBase):
                 normalized=normalized,
                 onesided=onesided,
                 return_complex=return_complex,
+                align_to_window=align_to_window,
             )
         return torch.stft(
             self,
@@ -973,6 +975,7 @@ class Tensor(torch._C.TensorBase):
             normalized,
             onesided,
             return_complex=return_complex,
+            align_to_window=align_to_window,
         )
 
     def istft(
diff --git a/torch/_tensor_docs.py b/torch/_tensor_docs.py
index 351a2886bd9..c5f7aa1983c 100644
--- a/torch/_tensor_docs.py
+++ b/torch/_tensor_docs.py
@@ -6398,7 +6398,8 @@ See :func:`torch.dsplit`
 add_docstr_all(
     "stft",
     r"""
-stft(frame_length, hop, fft_size=None, return_onesided=True, window=None, pad_end=0) -> Tensor
+stft(frame_length, hop, fft_size=None, return_onesided=True, window=None,
+ pad_end=0, align_to_window=None) -> Tensor
 
 See :func:`torch.stft`
 """,
diff --git a/torch/functional.py b/torch/functional.py
index 852661253a9..6201dc7b63c 100644
--- a/torch/functional.py
+++ b/torch/functional.py
@@ -551,6 +551,7 @@ def stft(
     normalized: bool = False,
     onesided: Optional[bool] = None,
     return_complex: Optional[bool] = None,
+    align_to_window: Optional[bool] = None,
 ) -> Tensor:
     r"""Short-time Fourier transform (STFT).
 
@@ -698,6 +699,11 @@ def stft(
             normalized=normalized,
             onesided=onesided,
             return_complex=return_complex,
+            align_to_window=align_to_window,
+        )
+    if center and align_to_window is not None:
+        raise RuntimeError(
+            "stft align_to_window should only be set when center = false"
         )
     # NOTE: Do not edit. This code will be removed once the forward-compatibility
     #       period is over for PR #73432
@@ -716,6 +722,7 @@ def stft(
         normalized,
         onesided,
         return_complex,
+        align_to_window,
     )
 
 
diff --git a/torch/onnx/symbolic_opset17.py b/torch/onnx/symbolic_opset17.py
index ed5cd93ab05..bcf80058fe2 100644
--- a/torch/onnx/symbolic_opset17.py
+++ b/torch/onnx/symbolic_opset17.py
@@ -98,7 +98,7 @@ def _compute_edge_sizes(n_fft, window_size):
 
 
 @_onnx_symbolic("aten::stft")
-@symbolic_helper.parse_args("v", "i", "i", "i", "v", "b", "b", "b")
+@symbolic_helper.parse_args("v", "i", "i", "i", "v", "b", "b", "b", "b")
 def stft(
     g: jit_utils.GraphContext,
     input: _C.Value,
@@ -109,6 +109,7 @@ def stft(
     normalized: bool = False,
     onesided: Optional[bool] = True,
     return_complex: Optional[bool] = False,
+    align_to_window: Optional[bool] = None,
 ) -> _C.Value:
     """Associates `torch.stft` with the `STFT` ONNX operator.
     Note that torch.stft calls _VF.stft, without centering or padding options.
@@ -137,6 +138,12 @@ def stft(
             msg="STFT does not currently support complex types", value=input
         )
 
+    if align_to_window is not None:
+        raise errors.SymbolicValueError(
+            msg="STFT does not currently support the align_to_window option",
+            value=input,
+        )  # TODO(#145944): add compatibility with align_to_window option.
+
     # Get STFT sizes
     frame_step_value = hop_length if hop_length is not None else n_fft // 4
     frame_step_const = g.op(
diff --git a/torch/overrides.py b/torch/overrides.py
index 56249324252..97fc86af920 100644
--- a/torch/overrides.py
+++ b/torch/overrides.py
@@ -1130,7 +1130,7 @@ def get_testing_overrides() -> dict[Callable, Callable]:
         torch.std: lambda input, dim=None: -1,
         torch.std_mean: lambda input, dim=None: -1,
         torch.stft: (
-            lambda input, n_fft, hop_length=None, win_length=None, window=None, center=True, pad_mode="reflect", normalized=False, onesided=True, return_complex=None: -1  # noqa: B950
+            lambda input, n_fft, hop_length=None, win_length=None, window=None, center=True, pad_mode="reflect", normalized=False, onesided=True, return_complex=None, align_to_window=None: -1  # noqa: B950
         ),
         torch.sub: lambda input, other, out=None: -1,
         torch.subtract: lambda input, other, out=None: -1,