diff --git a/examples/audio/md/stft.md b/examples/audio/md/stft.md
index 6f898ae474..08c6d42001 100644
--- a/examples/audio/md/stft.md
+++ b/examples/audio/md/stft.md
@@ -605,7 +605,7 @@ plt.show()
 
 
     
-![png](https://github.com/mostafa-mahmoud/keras-io/blob/master/examples/audio/img/stft/raw_audio.png)
+![png](https://github.com/keras-team/keras-io/blob/master/examples/audio/img/stft/raw_audio.png)
     
 
 
@@ -618,7 +618,7 @@ plot_single_spectrogram(sample_wav_data)
 
 
     
-![png](https://github.com/mostafa-mahmoud/keras-io/blob/master/examples/audio/img/stft/spectrogram.png)
+![png](https://github.com/keras-team/keras-io/blob/master/examples/audio/img/stft/spectrogram.png)
     
 
 
@@ -631,7 +631,7 @@ plot_multi_bandwidth_spectrogram(sample_wav_data)
 
 
     
-![png](https://github.com/mostafa-mahmoud/keras-io/blob/master/examples/audio/img/stft/multiband_spectrogram.png)
+![png](https://github.com/keras-team/keras-io/blob/master/examples/audio/img/stft/multiband_spectrogram.png)
     
 
 
@@ -643,9 +643,7 @@ def read_dataset(df, folds):
     msk = df["fold"].isin(folds)
     filenames = df["filename"][msk]
     targets = df["target"][msk].values
-    waves = np.array(
-        [read_wav_file(fil) for fil in filenames], dtype=np.float32
-    )
+    waves = np.array([read_wav_file(fil) for fil in filenames], dtype=np.float32)
     return waves, targets
 ```
 
@@ -1790,7 +1788,7 @@ plt.show()
 
 
     
-![png](https://github.com/mostafa-mahmoud/keras-io/blob/master/examples/audio/img/stft/training.png)
+![png](https://github.com/keras-team/keras-io/blob/master/examples/audio/img/stft/training.png)
     
 
 
@@ -1802,9 +1800,7 @@ Running the models on the test set.
 
 ```python
 _, test_acc = model1d.evaluate(test_x, test_y)
-print(
-    f"1D model wit non-trainable STFT -> Test Accuracy: {test_acc * 100:.2f}%"
-)
+print(f"1D model wit non-trainable STFT -> Test Accuracy: {test_acc * 100:.2f}%")
 ```
 
     3/3 ━━━━━━━━━━━━━━━━━━━━ 3s 307ms/step - accuracy: 0.8148 - loss: 0.6244
diff --git a/templates/examples/audio/stft.md b/templates/examples/audio/stft.md
index 7fe77f719a..de73a0453d 100644
--- a/templates/examples/audio/stft.md
+++ b/templates/examples/audio/stft.md
@@ -6,6 +6,7 @@
 **Description:** Introducing the `STFTSpectrogram` layer to extract spectrograms for audio classification.
 
 
+<div class='example_version_banner keras_3'>ⓘ This example uses Keras 3</div>
 <img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/audio/ipynb/stft.ipynb)  <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/audio/stft.py)
 
 
@@ -52,7 +53,6 @@ import scipy.io.wavfile
 from keras import layers
 from scipy.signal import resample
 
-np.random.seed(41)
 keras.utils.set_random_seed(41)
 ```
 
@@ -606,7 +606,7 @@ plt.show()
 
 
     
-![png](https://github.com/mostafa-mahmoud/keras-io/blob/master/examples/audio/img/stft/raw_audio.png)
+![png](https://github.com/keras-team/keras-io/blob/master/examples/audio/img/stft/raw_audio.png)
     
 
 
@@ -619,7 +619,7 @@ plot_single_spectrogram(sample_wav_data)
 
 
     
-![png](https://github.com/mostafa-mahmoud/keras-io/blob/master/examples/audio/img/stft/spectrogram.png)
+![png](https://github.com/keras-team/keras-io/blob/master/examples/audio/img/stft/spectrogram.png)
     
 
 
@@ -632,7 +632,7 @@ plot_multi_bandwidth_spectrogram(sample_wav_data)
 
 
     
-![png](https://github.com/mostafa-mahmoud/keras-io/blob/master/examples/audio/img/stft/multiband_spectrogram.png)
+![png](https://github.com/keras-team/keras-io/blob/master/examples/audio/img/stft/multiband_spectrogram.png)
     
 
 
@@ -644,9 +644,7 @@ def read_dataset(df, folds):
     msk = df["fold"].isin(folds)
     filenames = df["filename"][msk]
     targets = df["target"][msk].values
-    waves = np.array(
-        [read_wav_file(fil) for fil in filenames], dtype=np.float32
-    )
+    waves = np.array([read_wav_file(fil) for fil in filenames], dtype=np.float32)
     return waves, targets
 ```
 
@@ -1791,7 +1789,7 @@ plt.show()
 
 
     
-![png](https://github.com/mostafa-mahmoud/keras-io/blob/master/examples/audio/img/stft/training.png)
+![png](https://github.com/keras-team/keras-io/blob/master/examples/audio/img/stft/training.png)
     
 
 
@@ -1803,9 +1801,7 @@ Running the models on the test set.
 
 ```python
 _, test_acc = model1d.evaluate(test_x, test_y)
-print(
-    f"1D model wit non-trainable STFT -> Test Accuracy: {test_acc * 100:.2f}%"
-)
+print(f"1D model wit non-trainable STFT -> Test Accuracy: {test_acc * 100:.2f}%")
 ```
 
     3/3 ━━━━━━━━━━━━━━━━━━━━ 3s 307ms/step - accuracy: 0.8148 - loss: 0.6244