Author: Fredrik Cumlin Email: fcumlin@gmail.com
This is the official implementation of the model LaMOSNet from the paper "Latent-based Neural Net for Non-intrusive Speech Quality Assessment". LaMOSNet is a non-intrusive speech quality metric that given an input signal, predicts the overall quality.
First, download the VCC2018 data, which can for example be done from here. Then run train.py
, which depend on the following arguments:
--num_epochs
: The number of epochs during training.--log_valid
: The number of epochs between logging results on validation data.--log_epoch
: The number of epochs between logging training losses.--data_path
: Path to the data folder.--id_table
: Path to the id_table folder.--save_path
: Path for the best model to be saved.
This repository inherits from the unofficial MBNet implementation and the LDNet implementation .