Implementation of AM-MobileNet1D with Oneflow.

The mobile computation requires applications with reduced storage size, non-processing and memory intensive and efficient energy-consuming. The deep learning approaches, in contrast, usually are energy expensive, demanding storage, processing power, and memory. Additive Margin MobileNet1D (AM-MobileNet1D) is a portable and effective model. It obtains equivalent or better performances on TIMIT and MIT datasets concerning the baseline methods. Additionally, it takes only 11.6 megabytes on disk storage against 91.2 from SincNet and AM-SincNet architectures, making the model seven times faster, with eight times fewer parameters.

• Python 3.8
• oneflow 0.5.0
• pysoundfile

We use the TIMIT dataset to train the model. However, the code can be easily adapted to any speech dataset.

utils/TIMIT_preparation.py contains scripts to process TIMIT dataset into features compatible with AM-MobileNet1D.

This file receives three parameters, $TIMIT_FOLDER、$DATA_PREPARED and $LABEL. $TIMIT_FOLDER is the folder of the original TIMIT corpus. $OUTPUT_FOLDER is the folder in which the preprocessed TIMIT will be stored. $LABEL is the list of the TIMIT files used for training/test the speaker id system.

bash train.sh

bash infer.sh

oneflow 0.7953 pytorch 0.7870