xref: /external/tensorflow/tensorflow/examples/wav_to_spectrogram

# 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
```