blob: 7f7eb43700c187e0049f9b7e6911cd1ed8d05a5b (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
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
```
|
