This is an mxnet solution for the kaggle dataset "Spoken Language Identification". It uses a simple FFDNN to determine the language spoken in audio data containing human speech.
The included program & model support classification of German, English, and Spanish speech.
Training and inference can be performed using the included python program.
usage: splidnn.py [-h] {train,infer} ...
Spoken language identification DNN
positional arguments:
{train,infer} sub-command help
train Train the DNN model
infer Infer using trained model
optional arguments:
-h, --help show this help message and exit
Training works using the dataset from https://www.kaggle.com/toponowicz/spoken-language-identification -- simply download the archive containing the training/test data and pass it over as an argument. Unpacking the zip archive is not necessary.
The training procedure alternates between training and validation passes, saving a copy of the network (weights) and the optimizer state at the end of every epoch.
usage: splidnn.py train [-h] -e EPOCHS archive
positional arguments:
archive Dataset archive
optional arguments:
-h, --help show this help message and exit
-e EPOCHS, --epochs EPOCHS
Specify the model state and any audio file(s) to analyze. The inference procedure will split up the audio input into 10-second chunks and output the predicted language label per block. FLAC and WAV input formats are supported, audio IO is performed using PySoundFile
usage: splidnn.py infer [-h] -m MODEL audio [audio ...]
positional arguments:
audio Audio file(s) to process
optional arguments:
-h, --help show this help message and exit
-m MODEL, --model MODEL
The following example infers language labels for a single example audio file:
$ python3 splidnn.py infer --model model_epoch5 /tmp/de_example_file.flac
/tmp/de_example_file.flac DE,DE,DE,DE
The results of the sample training run are included in output.txt as well as the best model/trainer pair model_epoch5, trainer_epoch5. We stop training after 5 epochs to avoid overfitting to the input dataset.