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diff --git a/tensorflow/examples/wav_to_spectrogram/README.md b/tensorflow/examples/wav_to_spectrogram/README.md new file mode 100644 index 0000000000..7f7eb43700 --- /dev/null +++ b/tensorflow/examples/wav_to_spectrogram/README.md @@ -0,0 +1,49 @@ +# 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 +``` |