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-# TensorFlow Spectrogram Example
-
-This example shows how you can load audio from a .wav file, convert it to a
-spectrogram, and then save it out as a PNG image. A spectrogram is a
-visualization of the frequencies in sound over time, and can be useful as a
-feature for neural network recognition on noise or speech.
-
-## Building
-
-To build it, run this command:
-
-```bash
-bazel build tensorflow/examples/wav_to_spectrogram/...
-```
-
-That should build a binary executable that you can then run like this:
-
-```bash
-bazel-bin/tensorflow/examples/wav_to_spectrogram/wav_to_spectrogram
-```
-
-This uses a default test audio file that's part of the TensorFlow source code,
-and writes out the image to the current directory as spectrogram.png.
-
-## Options
-
-To load your own audio, you need to supply a .wav file in LIN16 format, and use
-the `--input_audio` flag to pass in the path.
-
-To control how the spectrogram is created, you can specify the `--window_size`
-and `--stride` arguments, which control how wide the window used to estimate
-frequencies is, and how widely adjacent windows are spaced.
-
-The `--output_image` flag sets the path to save the image file to. This is
-always written out in PNG format, even if you specify a different file
-extension.
-
-If your result seems too dark, try using the `--brightness` flag to make the
-output image easier to see.
-
-Here's an example of how to use all of them together:
-
-```bash
-bazel-bin/tensorflow/examples/wav_to_spectrogram/wav_to_spectrogram \
---input_wav=/tmp/my_audio.wav \
---window=1024 \
---stride=512 \
---output_image=/tmp/my_spectrogram.png
-```