Skip to content

bshall/ZeroSpeech

Repository files navigation

VQ-VAE for Acoustic Unit Discovery and Voice Conversion

Train and evaluate the VQ-VAE model for our submission to the ZeroSpeech 2020 challenge. Voice conversion samples can be found here. Pretrained weights for the 2019 English and Indonesian datasets can be found here.

VQ-VAE for Acoustic Unit Discovery
Fig 1: VQ-VAE model architecture.

Quick Start

Requirements

  1. Ensure you have Python 3 and PyTorch 1.4 or greater.

  2. Install NVIDIA/apex for mixed precision training.

  3. Install pip dependencies:

    pip install -r requirements.txt
    
  4. For evaluation install bootphon/zerospeech2020.

Data and Preprocessing

  1. Download and extract the ZeroSpeech2020 datasets.

  2. Download the train/test splits here and extract in the root directory of the repo.

  3. Preprocess audio and extract train/test log-Mel spectrograms:

    python preprocess.py in_dir=/path/to/dataset dataset=[2019/english or 2019/surprise]
    

    Note: in_dir must be the path to the 2019 folder. For dataset choose between 2019/english or 2019/surprise. Other datasets will be added in the future.

    e.g. python preprocess.py in_dir=../datasets/2020/2019 dataset=2019/english
    

Training

Train the models or download pretrained weights here:

python train.py checkpoint_dir=path/to/checkpoint_dir dataset=[2019/english or 2019/surprise]
e.g. python train.py checkpoint_dir=checkpoints/2019english dataset=2019/english

Evaluation

Voice conversion

python convert.py checkpoint=path/to/checkpoint in_dir=path/to/wavs out_dir=path/to/out_dir synthesis_list=path/to/synthesis_list dataset=[2019/english or 2019/surprise]

Note: the synthesis list is a json file:

[
    [
        "english/test/S002_0379088085",
        "V002",
        "V002_0379088085"
    ]
]

containing a list of items with a) the path (relative to in_dir) of the source wav files; b) the target speaker (see datasets/2019/english/speakers.json for a list of options); and c) the target file name.

e.g. python convert.py checkpoint=checkpoints/2019english/model.ckpt-500000.pt in_dir=../datasets/2020/2019 out_dir=submission/2019/english/test synthesis_list=datasets/2019/english/synthesis.json dataset=2019/english

Voice conversion samples can be found here.

ABX Score

  1. Encode test data for evaluation:

    python encode.py checkpoint=path/to/checkpoint out_dir=path/to/out_dir dataset=[2019/english or 2019/surprise]
    
    e.g. python encode.py checkpoint=checkpoints/2019english/model.ckpt-500000.pt out_dir=submission/2019/english/test dataset=2019/english
    
  2. Run ABX evaluation script (see bootphon/zerospeech2020).

The ABX score for the pretrained english model (available here) is:

{
    "2019": {
        "english": {
            "scores": {
                "abx": 14.043611615570672,
                "bitrate": 412.2387509949519
            },
            "details_bitrate": {
                "test": 412.2387509949519
            },
            "details_abx": {
                "test": {
                    "cosine": 14.043611615570672,
                    "KL": 50.0,
                    "levenshtein": 35.927825062038984
                }
            }
        }
    }
}

References

This work is based on:

  1. Chorowski, Jan, et al. "Unsupervised speech representation learning using wavenet autoencoders." IEEE/ACM transactions on audio, speech, and language processing 27.12 (2019): 2041-2053.

  2. Lorenzo-Trueba, Jaime, et al. "Towards achieving robust universal neural vocoding." INTERSPEECH. 2019.

  3. van den Oord, Aaron, and Oriol Vinyals. "Neural discrete representation learning." Advances in Neural Information Processing Systems. 2017.