[STFT][PT FE] Add support for normalize attr in STFT conversion (open…

…vinotoolkit#28005)

### Details:
- Add support for `normalize` attribute of torch `aten::stft` by post op
subgraph in PT Frontend

### Tickets:
 - 159159

src/frontends/pytorch/src/op/stft.cpp

@@ -10,6 +10,7 @@
 #include "openvino/op/convert_like.hpp"
 #include "openvino/op/divide.hpp"
 #include "openvino/op/shape_of.hpp"
+#include "openvino/op/sqrt.hpp"
 #include "openvino/op/unsqueeze.hpp"
 #include "utils.hpp"
 
@@ -66,8 +67,6 @@ OutputVector translate_stft(const NodeContext& context) {
     if (!context.input_is_none(5)) {
         normalized = context.const_input<bool>(5);
     }
-    PYTORCH_OP_CONVERSION_CHECK(!normalized,
-                                "aten::stft conversion is currently supported with normalized=False only.");
 
     bool onesided = true;
     if (!context.input_is_none(6)) {
@@ -85,7 +84,15 @@ OutputVector translate_stft(const NodeContext& context) {
     // Perform STFT
     constexpr bool transpose_frames = true;
     auto stft = context.mark_node(std::make_shared<v15::STFT>(input, window, n_fft, hop_length, transpose_frames));
-    return {stft};
+
+    if (normalized) {
+        const auto nfft_convert = context.mark_node(std::make_shared<v1::ConvertLike>(n_fft, stft));
+        const auto divisor = context.mark_node(std::make_shared<v0::Sqrt>(nfft_convert));
+        const auto norm_stft = context.mark_node(std::make_shared<v1::Divide>(stft, divisor));
+        return {norm_stft};
+    } else {
+        return {stft};
+    }
 };
 }  // namespace op
 }  // namespace pytorch

tests/layer_tests/pytorch_tests/test_stft.py

@@ -24,16 +24,17 @@ def _prepare_input(self, win_length, signal_shape, rand_data=False, out_dtype="f
 
         return (signal, window.astype(out_dtype))
 
-    def create_model(self, n_fft, hop_length, win_length):
+    def create_model(self, n_fft, hop_length, win_length, normalized):
         import torch
 
         class aten_stft(torch.nn.Module):
 
-            def __init__(self, n_fft, hop_length, win_length):
+            def __init__(self, n_fft, hop_length, win_length, normalized):
                 super(aten_stft, self).__init__()
                 self.n_fft = n_fft
                 self.hop_length = hop_length
                 self.win_length = win_length
+                self.normalized = normalized
 
             def forward(self, x, window):
                 return torch.stft(
@@ -44,14 +45,14 @@ def forward(self, x, window):
                     window=window,
                     center=False,
                     pad_mode="reflect",
-                    normalized=False,
+                    normalized=self.normalized,
                     onesided=True,
                     return_complex=False,
                 )
 
         ref_net = None
 
-        return aten_stft(n_fft, hop_length, win_length), ref_net, "aten::stft"
+        return aten_stft(n_fft, hop_length, win_length, normalized), ref_net, "aten::stft"
 
     @pytest.mark.nightly
     @pytest.mark.precommit
@@ -64,10 +65,11 @@ def forward(self, x, window):
         [24, 32, 20],
         [128, 128, 128],
     ])
-    def test_stft(self, n_fft, hop_length, window_size, signal_shape, ie_device, precision, ir_version, trace_model):
+    @pytest.mark.parametrize(("normalized"), [True, False])
+    def test_stft(self, n_fft, hop_length, window_size, signal_shape, normalized, ie_device, precision, ir_version, trace_model):
         if ie_device == "GPU":
             pytest.xfail(reason="STFT op is not supported on GPU yet")
-        self._test(*self.create_model(n_fft, hop_length, window_size), ie_device, precision,
+        self._test(*self.create_model(n_fft, hop_length, window_size, normalized), ie_device, precision,
                    ir_version, kwargs_to_prepare_input={"win_length": window_size, "signal_shape": signal_shape}, trace_model=trace_model)
 
 
@@ -125,8 +127,8 @@ def forward(self, x):
         [16, None, 16, False, "reflect", False, True, False],  # hop_length None
         [16, None, None, False, "reflect", False, True, False],  # hop & win length None
         [16, 4, None, False, "reflect", False, True, False],  # win_length None
-        # Unsupported cases:
         [16, 4, 16, False, "reflect", True, True, False],  # normalized True
+        # Unsupported cases:
         [16, 4, 16, False, "reflect", False, False, False],  # onesided False
         [16, 4, 16, False, "reflect", False, True, True],  # reutrn_complex True
     ])
@@ -138,10 +140,6 @@ def test_stft_not_supported_attrs(self, n_fft, hop_length, win_length, center, p
             pytest.xfail(
                 reason="torch stft uses list() for `center` subgrpah before aten::stft, that leads to error: No conversion rule found for operations: aten::list")
 
-        if normalized is True:
-            pytest.xfail(
-                reason="aten::stft conversion is currently supported with normalized=False only")
-
         if onesided is False:
             pytest.xfail(
                 reason="aten::stft conversion is currently supported with onesided=True only")