Spoken language identification with deep learning

Read more in the following blog posts:

• About TopCoder contest and our CNN-based solution implemented in Caffe (October 2015)
• About combining CNN and RNN using Theano/Lasagne (June 2016)

Theano/Lasagne models are here. The basic steps to run them are:

• Download the dataset from here or use your own dataset.
• Create spectrograms for recording using create_spectrograms.py or augment_data.py. The latter will also augment the data by randomly perturbing the spectrograms and cropping a random interval of length 9s from the recording.
• Create listfiles for training set and validation set, where each row of the a listfile describes one example and has 2 values seperated by a comma. The first one is the name of the example, the second one is the label (counting starts from 0). A typical listfile will look like this.
• Change the png_folder and listfile paths in theano/main.py.
• Run theano/main.py.