Official implementation of SelfRemaster: Self-Supervised Speech Restoration with Analysis-by-Synthesis Approach Using Channel Modeling (to appear in INTERSPEECH 2022).

This repo contains an older version of the code, but is kept for compatibility.

The latest version is available here.

• Audio samples
• Audio effect transfer with Gradio + HuggingFace Spaces 🤗

1. Clone this repository: git clone https://github.com/Takaaki-Saeki/ssl_speech_restoration.git.
2. CD into this repository: cd ssl_speech_restoration.
3. Install python packages and download some pretrained models: ./setup.sh.

• If you use default Japanese corpora:
  • Download JSUT Basic5000 and JVS Corpus
  • Downsample them to 22.05 kHz and place them under data/ as jsut_22k and jvs_22k.
    • JSUT is a single-speaker dataset and requires the structure as jsut_22k/*.wav. Note that this is the ground-truth clean speech data which correspond to the simulated data and is not used for training. You may want to use jsut_22k only to compare the restored speech and ground-truth speech.
    • JVS parallel100 includes 100-speaker data and requires the structure as jvs_22k/${spkr_name}/*.wav. This is a clean speech dataset used for the backward learning of the dual-learning method.
  • Place simulated low-quality data under ./data as jsut_22k-low.
• Or you can use arbitrary datasets by modifying config files.

You can choose MelSpec or SourFilter models with --config_path option.
As shown in the paper, MelSpec model is of higher-quality.

Firstly you need to split the data to train/val/test and dump them by the following command.

python preprocess.py --config_path configs/train/${feature}/ssl_jsut.yaml

To perform self-supervised learning with dual learning, run the following command.

python train.py \
    --config_path configs/train/${feature}/ssl_jsut.yaml \
    --stage ssl-dual \
    --run_name ssl_melspec_dual

For other options, refer to train.py.

Note that you might need to tune some parameters for your own datasets.
In our experiences, learning_rate and beta are cruicial parameters.
For example, if the trianing is unstable, consider making beta smaller (e.b., beta: 0.001).

To perform speech restoration of the test data, run the following command.

python eval.py \
    --config_path configs/test/${feature}/ssl_jsut.yaml \
    --ckpt_path ${path to checkpoint} \
    --stage ssl-dual \
    --run_name ssl_melspec_dual

For other options, see eval.py.

You can run a simple audio effect transfer demo using a model pretrained with real data.
Run the following command.

python aet_demo.py

Or you can customize the dataset or model.
You need to edit audio_effect_transfer.yaml and run the following command.

python aet.py \
    --config_path configs/test/melspec/audio_effect_transfer.yaml \
    --stage ssl-dual \
    --run_name aet_melspec_dual

For other options, see aet.py.

See here.

You can generate simulated low-quality data as in the paper with the following command.

python simulated_data.py \
    --in_dir ${input_directory (e.g., path to jsut_22k)} \
    --output_dir ${output_directory (e.g., path to jsut_22k-low)} \
    --corpus_type ${single-speaker corpus or multi-speaker corpus} \
    --deg_type lowpass

Then download the pretrained model correspond to the deg_type and run the following command.

python eval.py \
    --config_path configs/train/${feature}/ssl_jsut.yaml \
    --ckpt_path ${path to checkpoint} \
    --stage ssl-dual \
    --run_name ssl_melspec_dual

@article{saeki22selfremaster,
  title={{SelfRemaster}: {S}elf-Supervised Speech Restoration with Analysis-by-Synthesis Approach Using Channel Modeling},
  author={T. Saeki and S. Takamichi and T. Nakamura and N. Tanji and H. Saruwatari},
  journal={arXiv preprint arXiv:2203.12937},
  year={2022}
}

• HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
• VoiceFixer: Toward General Speech Restoration with Neural Vocoder