chore: import upstream snapshot with attribution

tensorflow/examples/wav_to_spectrogram/README.md

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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
+```