<!DOCTYPE html>
Keras TimeseriesGenerator¶
In this reading we'll be looking at the TimeseriesGenerator
, which is used for preprocessing and generating batches of temporal data.
Examples of sequential data are audio tracks, music, books and essays. Here, the order of the notes, words and sentences carry information about the meaning.
import tensorflow as tf
tf.__version__
The dataset¶
In this notebook, we'll be using the DSD100 dataset to demonstrate the use of the TimeseriesGenerator
to perform various preprocessing operations. The DSD100 dataset is a dataset of 100 different music tracks in different styles. Its intended use is signal separation, and it also includes the separate instrument tracks that add up to make the music tracks. The tracks are all stereophonic and sampled at 44.1kHz. A sample from the dataset is provided.
Run the cell below to load one of the sample songs and press the play button to listen to the song.
# Play a sample track from the DSD100 dataset
from IPython import display as ipd
ipd.display(ipd.Audio("data/055 - Angels In Amplifiers - I'm Alright/mixture.wav"))