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a2o-vc-vcc2020

README.md

S3PRL-VC: open-source voice conversion framework with self-supervised speech representations

Development: Wen-Chin Huang @ Nagoya University (2021).
If you have any questions, please open an issue, or contact through email: [email protected]

Wen-Chin Huang has released a standalone and actively maintained official repository for S3PRL-VC. The standalone version contains much more recipes for various VC experiments. Please must have a try!

Note: This is the any-to-one recipe. For the any-to-any recipe, please go to the a2a-vc-vctk recipe.

We have a preprint paper describing this toolkit. If you find this recipe useful, please consider citing:

@inproceedings{huang2021s3prl,
  title={S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations},
  author={Huang, Wen-Chin and Yang, Shu-Wen and Hayashi, Tomoki and Lee, Hung-Yi and Watanabe, Shinji and Toda, Tomoki},
  booktitle={Proc. ICASSP},
  year={2022}
}

Table of contents

Task

In this downstream, we focus on training any-to-one (A2O) voice conversion (VC) models on the two tasks in voice conversion challenge 2020 (VCC2020) The first task is intra-lingual VC, and the second task is cross-lingual VC. For more details about the two tasks and the VCC2020 dataset, please refer to the original paper:

  • Yi, Z., Huang, W., Tian, X., Yamagishi, J., Das, R.K., Kinnunen, T., Ling, Z., Toda, T. (2020) Voice Conversion Challenge 2020 –- Intra-lingual semi-parallel and cross-lingual voice conversion –-. Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 80-98, DOI: 10.21437/VCC_BC.2020-14. [paper] [database]

Implementation

We implement three models: the simple model, simple-AR model and Taco2-AR model. The simple model and the Taco2-AR model resemble the top systems in VCC2018 and VCC2020, respectively. They are described in the following papers:

  • Liu, L., Ling, Z., Jiang, Y., Zhou, M., Dai, L. (2018) WaveNet Vocoder with Limited Training Data for Voice Conversion. Proc. Interspeech 2018, 1983-1987, DOI: 10.21437/Interspeech.2018-1190. [paper]
  • Liu, L., Chen, Y., Zhang, J., Jiang, Y., Hu, Y., Ling, Z., Dai, L. (2020) Non-Parallel Voice Conversion with Autoregressive Conversion Model and Duration Adjustment. Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 126-130, DOI: 10.21437/VCC_BC.2020-17. [paper]

We made several modifications.

  1. Input: instead of using the phonetic posteriorgrams (PPGs) / bottleneck features (BNFs) of a pretrained ASR model, we use the various upstreams provided in S3PRL.
  2. Output: instead of using acoustic features extracted using a high-quality vocoder, STRAIGHT, we use the log-melspectrograms.
  3. Data: we benchmark on the VCC2020 dataset.
  4. Training strategy: instead of pretraining on a multispeaker dataset first, we directly trained on the target speaker training set.
  5. Vocoder: instead of using the WaveNet vocoder, we offer non-AR neural vocoders including Parallel WaveGAN (PWG) and Hifi-GAN, implemented in the open source project developed by kan-bayashi.

Dependencies:

  • parallel-wavegan
  • fastdtw
  • pyworld
  • pysptk
  • jiwer
  • resemblyzer

You can install them via the requirements.txt file.

Usage

Preparation

# Download the VCC2020 dataset.
cd <root-to-s3prl>/s3prl/downstream/a2o-vc-vcc2020
cd data
./data_download.sh vcc2020/
cd ../

# Download the pretrained vocoders.
./vocoder_download.sh ./

Dry run / benchmarking an upstream

Training

The following command starts a dry run (testing run) given any <upstream>.

cd <root-to-s3prl>/s3prl
python run_downstream.py -m train -n test -u <upstream> -d a2o-vc-vcc2020

By default, the config.yaml is used, which has the exact same configuration as config_taco2_ar.yaml. Also, the default is to convert to target speaker TEF1. We can change the target speaker, the upstream, as well as the exp name to our desired setting. For example, we can train a model that converts to TEF2 using the wav2vec upstream using the following command:

python run_downstream.py -m train -n a2o_vc_vcc2020_taco2_ar_TEF2_wav2vec -u wav2vec -d a2o-vc-vcc2020 -o "config.downstream_expert.trgspk='TEF2'"

Along the training process, you may find converted speech samples generated using the Griffin-Lim algorithm automatically saved in <root-to-s3prl>/s3prl/result/downstream/a2o_vc_vcc2020_taco2_ar_TEF2_wav2vec/<step>/test/wav/.

Waveform synthesis (decoding) using a neural vocoder & objective evaluation

We provide a shell script to conveniently perform the followings: (1) waveform synthesis (or decoding) using not Griffin-Lim but a neural vocoder, and (2) objective evaluation of a model trained with a specific number of steps. Note that decoding is done in the s3prl directory!

cd <root-to-s3prl>/s3prl/
.downstream/a2o-vc-vcc2020/decode.sh <vocoder_dir> result/downstream/<expname>/<step> <trgspk>

For example:

./downstream/a2o-vc-vcc2020/decode.sh downstream/a2o-vc-vcc2020/hifigan_vctk result/downstream/a2o_vc_vcc2020_taco2_ar_TEF1_wav2vec/10000 TEF1

The generated speech samples will be saved in <root-to-s3prl>/s3prl/result/downstream/a2o_vc_vcc2020_taco2_ar_<trgspk>_<upstream>/<step>/test/<vocoder_name>_wav/. Also, the output of the evaluation will be shown directly:

Mean MCD, f0RMSE, f0CORR, DDUR, CER: 7.79 39.02 0.422 0.356 7.0 15.4

And detailed utterance-wise evaluation results can be found in <root-to-s3prl>/s3prl/result/downstream/a2o_vc_vcc2020_taco2_ar_<trgspk>_<upstream>/<step>/test/<vocoder_name>_wav/obj.log.

Advanced usage

This section describes advanced usage, targeted at potential VC researchers that wants to evaluate the VC performance using different models in a more efficient way.

Batch training

If your GPU memory is sufficient, we can train multiple models in one GPU to avoid executing repeated commands. We can also specify a different config file. In the following command, we train multiple models. Note that this is done in the s3prl directory!

cd <root-to-s3prl>/s3prl
./downstream/a2o-vc-vcc2020/batch_vc_train.sh <upstream> <config_file> <tag> <part>

For example, if we want to use the hubert upstream with the config_simple.yaml configuration to train 4 models w.r.t. the 4 target speakers in VCC2020 task 1:

./downstream/a2o-vc-vcc2020/batch_vc_train.sh hubert downstream/a2o-vc-vcc2020/config_simple.yaml simple task1_all

Notes:

  • In batch training mode, the training log are not output to stdout, but redirected to <root-to-s3prl>/s3prl/result/downstream/a2o_vc_vcc2020_simple_<trgspk>_hubert/train.log.
  • All exp names will have the format: a2o_vc_vcc2020_<tag>_<trgspk>_<upstream>. This can be useful to distinguish different exps if you change the configs.
  • We can change <part> to specify which target speakers to train. For example, passing fin to the script starts two training processes for the two Finnish target speakers. If the GPU memory is insufficient, we can also specify different parts. Please refer to batch_vc_train.sh for different specifications.

Batch decoding & objective evaluation

After you train models for all target speakers for each task (which can be done by batch training), we can use batch decoding to evaluate all models at once.

./downstream/a2o-vc-vcc2020/batch_vc_decode.sh <upstream> <task> <tag> <vocoder_dir>

Using the example above, we can run:

./downstream/a2o-vc-vcc2020/batch_vc_decode.sh hubert task1 simple downstream/a2o-vc-vcc2020/hifigan_vctk

The best result will then be automatically shown.

Custom decoding

Since we are training in the A2O setting, the model accepts source speech from arbitrary speakers.

Preparation

Prepare a text file, which each line corresponding to an absolute path to a source speech file. Here's an example:

/mrnas02/internal/wenchin-h/Experiments/s3prl-merge/s3prl/downstream/a2a-vc-vctk/data/wenchin_recording/wenchin_001.wav
/mrnas02/internal/wenchin-h/Experiments/s3prl-merge/s3prl/downstream/a2a-vc-vctk/data/wenchin_recording/wenchin_002.wav
/mrnas02/internal/wenchin-h/Experiments/s3prl-merge/s3prl/downstream/a2a-vc-vctk/data/wenchin_recording/wenchin_003.wav
/mrnas02/internal/wenchin-h/Experiments/s3prl-merge/s3prl/downstream/a2a-vc-vctk/data/wenchin_recording/wenchin_004.wav
/mrnas02/internal/wenchin-h/Experiments/s3prl-merge/s3prl/downstream/a2a-vc-vctk/data/wenchin_recording/wenchin_005.wav

Decoding

After model training finishes (by following either the Dry run or the Advanced usage sections), use the following script to perform custom decoding:

cd <root-to-s3prl>/s3prl
./downstream/a2o-vc-vcc2020/custom_decode.sh <upstream> <trgspk> <tag> <ep> <vocoder_dir> <list_path>

For example:

./downstream/a2o-vc-vcc2020/custom_decode.sh vq_wav2vec TEF1 ar_taco2 10000 downstream/a2o-vc-vcc2020/hifigan_vctk downstream/a2o-vc-vcc2020/data/lists/custom_eval.yaml

After the decoding process ends, you should be able to find the generated files in result/downstream/a2o_vc_vcc2020_<tag>_<trgspk>_<upstream>/custom_test/.