Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/marl/pysox
Python wrapper around sox.
https://github.com/marl/pysox
Last synced: about 2 months ago
JSON representation
Python wrapper around sox.
- Host: GitHub
- URL: https://github.com/marl/pysox
- Owner: marl
- License: bsd-3-clause
- Created: 2016-05-01T18:06:38.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2024-04-21T16:29:55.000Z (5 months ago)
- Last Synced: 2024-05-22T03:08:21.465Z (4 months ago)
- Language: Python
- Size: 4.1 MB
- Stars: 504
- Watchers: 13
- Forks: 79
- Open Issues: 34
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-python-audio - SoX
README
# pysox
Python wrapper around sox. [Read the Docs here](http://pysox.readthedocs.org).
[](https://badge.fury.io/py/sox)
[](https://pysox.readthedocs.io/en/latest/?badge=latest)
[](https://raw.githubusercontent.com/rabitt/pysox/master/LICENSE.md)
[]()
[](https://travis-ci.org/rabitt/pysox)
[](https://coveralls.io/github/rabitt/pysox?branch=master)
This library was presented in the following paper:
[R. M. Bittner](https://github.com/rabitt), [E. J. Humphrey](https://github.com/ejhumphrey) and J. P. Bello, "[pysox: Leveraging the Audio Signal Processing Power of SoX in Python](https://wp.nyu.edu/ismir2016/wp-content/uploads/sites/2294/2016/08/bittner-pysox.pdf)", in Proceedings of the 17th International Society for Music Information Retrieval Conference Late Breaking and Demo Papers, New York City, USA, Aug. 2016.
# Install
This requires that [SoX](http://sox.sourceforge.net/) version 14.4.2 or higher is installed.
To install SoX on Mac with Homebrew:
```brew install sox```
If you want support for `mp3`, `flac`, or `ogg` files, add the following flags:
```brew install sox --with-lame --with-flac --with-libvorbis```
on Linux:
```apt-get install sox```
or install [from source](https://sourceforge.net/projects/sox/files/sox/).
To install the most up-to-date release of this module via PyPi:
```pip install sox```
To install the master branch:
```pip install git+https://github.com/rabitt/pysox.git```
or
```
git clone https://github.com/rabitt/pysox.git
cd pysox
python setup.py install
```
# Tests
If you have a different version of SoX installed, it's recommended that you run
the tests locally to make sure everything behaves as expected, by simply running:
```
pytest
```
# Examples
```python
import sox
# create transformer
tfm = sox.Transformer()
# trim the audio between 5 and 10.5 seconds.
tfm.trim(5, 10.5)
# apply compression
tfm.compand()
# apply a fade in and fade out
tfm.fade(fade_in_len=1.0, fade_out_len=0.5)
# create an output file.
tfm.build_file('path/to/input_audio.wav', 'path/to/output/audio.aiff')
# or equivalently using the legacy API
tfm.build('path/to/input_audio.wav', 'path/to/output/audio.aiff')
# get the output in-memory as a numpy array
# by default the sample rate will be the same as the input file
array_out = tfm.build_array(input_filepath='path/to/input_audio.wav')
# see the applied effects
tfm.effects_log
> ['trim', 'compand', 'fade']
```
Transform in-memory arrays:
```python
import numpy as np
import sox
# sample rate in Hz
sample_rate = 44100
# generate a 1-second sine tone at 440 Hz
y = np.sin(2 * np.pi * 440.0 * np.arange(sample_rate * 1.0) / sample_rate)
# create a transformer
tfm = sox.Transformer()
# shift the pitch up by 2 semitones
tfm.pitch(2)
# transform an in-memory array and return an array
y_out = tfm.build_array(input_array=y, sample_rate_in=sample_rate)
# instead, save output to a file
tfm.build_file(
input_array=y, sample_rate_in=sample_rate,
output_filepath='path/to/output.wav'
)
# create an output file with a different sample rate
tfm.set_output_format(rate=8000)
tfm.build_file(
input_array=y, sample_rate_in=sample_rate,
output_filepath='path/to/output_8k.wav'
)
```
Concatenate 3 audio files:
```python
import sox
# create combiner
cbn = sox.Combiner()
# pitch shift combined audio up 3 semitones
cbn.pitch(3.0)
# convert output to 8000 Hz stereo
cbn.convert(samplerate=8000, n_channels=2)
# create the output file
cbn.build(
['input1.wav', 'input2.wav', 'input3.wav'], 'output.wav', 'concatenate'
)
# the combiner does not currently support array input/output
```
Get file information:
```python
import sox
# get the sample rate
sample_rate = sox.file_info.sample_rate('path/to/file.mp3')
# get the number of samples
n_samples = sox.file_info.num_samples('path/to/file.wav')
# determine if a file is silent
is_silent = sox.file_info.silent('path/to/file.aiff')
# file info doesn't currently support array input
```