Quick start

A most basic operation, transcoding, is easily performed with two processors:

>>> import timeside
>>> from timeside.core.tools.test_samples import samples
>>> from timeside.core import get_processor
>>> decoder = get_processor('file_decoder')(samples["sweep.wav"])
>>> encoder = get_processor('vorbis_encoder')("sweep.ogg")
>>> pipe = decoder | encoder
>>> pipe.run()

As one can see in the above example, creating a processing pipe is performed with the binary OR operator.

Audio data visualisation can be performed using graphers, such as Waveform and Spectrogram. All graphers return an image:

>>> import timeside
>>> from timeside.core.tools.test_samples import samples
>>> from timeside.core import get_processor
>>> decoder =  get_processor('file_decoder')(samples["sweep.wav"])
>>> spectrogram = get_processor('spectrogram_lin')(width=400, height=150)
>>> (decoder | spectrogram).run()
>>> spectrogram.render('graph.png')

It is possible to create longer pipes, as well as subpipes, here for both analysis and encoding:

>>> import timeside
>>> from timeside.core.tools.test_samples import samples
>>> from timeside.core import get_processor
>>> decoder = get_processor('file_decoder')(samples["sweep.wav"])
>>> levels = get_processor('level')()
>>> encoders = get_processor('mp3_encoder')('sweep.mp3') | get_processor('flac_encoder')('sweep.flac')
>>> (decoder | levels | encoders).run()
>>> print levels.results