Pytorch-MBNet

A pytorch implementation of MBNET: MOS PREDICTION FOR SYNTHESIZED SPEECH WITH MEAN-BIAS NETWORK

Training

To train a new model, please run train.py, the input arguments are:

• --data_path: The path of the directory containing all .wav files of VCC-2018 and the train/dev/test split files (the files in ./data).
• --save_dir: The path of the directory to save the trained models. Please create the directory before training.
• --total_steps: The total #training step in the training.
• --valid_steps: Do the validation every #(valid_steps) of training update.
• --log_steps: Log the tensorboard every #(log_steps) of training update.
• --update_freq: Gradient accumulation, the default value is 1 (no accumulation).

Testing

To test on VCC-2018, please run test.py, the input arguments are:

• --model_path: The path to the saved model.
• --idtable_path: The path to the "judge id-number" mapping table file used during training.
• --step: The time step for tensorboard log, which can be the same as the training steps.
• --split: The valid/test split of data to be used in the testing.

Inference

After training on the VCC data, the model can be utilized to inference on other data. The input arguments are --data_path, --model_path, --save_dir, which are similar to the above. Notice that the bias-net is not used since in this code the ground-truth judge ids are assumed to be unavailable.

The pre-trained model can be found in ./pre_trained.