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Spoken language identification DNN implemented in mxnet

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.

Usage

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

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

Inference

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

Results

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.

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