MOSNet

Implementation of "MOSNet: Deep Learning based Objective Assessment for Voice Conversion" https://arxiv.org/abs/1904.08352

Dependency

• tensorflow-gpu==2.0.0-beta1 (cudnn=7.6.0)
• scipy
• pandas
• matplotlib
• librosa

Environment set-up

For example,

conda create -n mosnet python=3.5
conda activate mosnet
pip install -r requirements.txt

Usage

1. cd ./data and run bash download.sh to download the VCC2018 evaluation results and submitted speech. (downsample the submitted speech might take some times)
2. Run python mos_results_preprocess.py to prepare the evaluation results. (Run python bootsrap_estimation.py to do the bootstrap experiment for intrinsic MOS calculation)
3. Run python utils.py to extract .wav to .h5
4. Run python train.py --model CNN-BLSTM to train a CNN-BLSTM version of MOSNet. ('CNN', 'BLSTM' or 'CNN-BLSTM' are supported in model.py, as described in paper)
5. Run python test.py to test on the pre-trained weights with specified model and weight.

Evaluation Results

The model is trained on the large listening evaluation results released by the Voice Conversion Challenge 2018.

The listening test results can be downloaded from here

The databases and results (submitted speech) can be downloaded from here