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SelfRemaster: Self-Supervised Speech Restoration

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

Note

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

The latest version is available here.

Demo

Setup

  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.

Getting started

  • 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.

Training

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).

Speech restoration

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.

Audio effect transfer

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.

Pretrained models

See here.

Reproducing results

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

Citation

@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}
}

Reference