Tacotron2

This is an implementation of Tacotron and Tacotron2.

Requirements

• Python >= 3.6
• tensorflow >= 1.11
• pyspark >= 2.3.0
• librosa >= 0.6.1
• scipy >= 1.1.1
• matplotlib >= 2.2.2
• hypothesis >= 3.59.1

Preprocessing

preprocess.py <dataset> </input/dataset/dir> </output/dataset/dir>

Currently available dataset are,

• ljspeech
• blizzard2012

Training

For training Tacotron itself, run the following command.

train.py --dataset=<dataset> --data-root=</output/dataset/dir> --checkpoint-dir=</path/to/model/dir> --hparams=<parmas>

For training Post-net of Tacotron (Mel to linear spectrogram conversion), run the following command.

train_postnet.py --dataset=<dataset> --data-root=</output/dataset/dir> --checkpoint-dir=</path/to/postnet/model/dir> --hparams=<parmas>

See Preprocessing for available dataset.

Synthesis

synthesize.py  --dataset=<dataset> --data-root=</output/dataset/dir> --checkpoint-dir=</path/to/model/dir> --postnet-checkpoint-dir=</path/to/postnet/model/dir> --hparams=<parmas>

How to use as an external library

This implementation supports Bazel build. You can add this repository as a external dependency in your Bazel project.

Add following lines to a WORKSPACE file of your project. These lines configure how to get Tacotron2 codes and what version you use.

git_repository(
    name = "tacotron2",
    remote = "git@github.com:nii-yamagishilab/tacotron2.git",
    commit = "138c7934e3c6d99238f8b6b84d6b0a30f4ea8b2e",
)

Then add a dependency of Tacotron2 to your BUILD file. For example, adding following lines enables your training script to use Tacotron2 codes.

py_binary(
    name = "train",
    srcs = [
        "train.py",
    ],
    srcs_version = "PY3ONLY",
    default_python_version = "PY3",
    deps = [
        "@tacotron2//:tacotron2",
    ],
)

Now you can import tacotron2 package in your training script.

ToDo

• Add Tacotron2 model
• Implement L2 regularization
• More easy to use runnable example

Authors

• Yusuke Yasuda (National Institute of Informatics, Japan) @TanUkkii007

References and resources

This is an implementation of the following papers.

• Yuxuan Wang, R.J. Skerry-Ryan, Daisy Stanton, Yonghui Wu, Ron J. Weiss, Navdeep Jaitly, Zongheng Yang, Ying Xiao, Zhifeng Chen, Samy Bengio, Quoc Le, Yannis Agiomyrgiannakis, Rob Clark, and Rif A. Saurous, “Tacotron: Towards end-to-end speech synthesis,” in Proc. Interspeech , 2017, pp. 4006–4010
• Jonathan Shen, Ruoming Pang, Ron J. Weiss, Mike Schuster, Navdeep Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang, Yuxuan Wang, RJ-Skerrv Ryan, Rif A. Saurous, Yannis Agiomyrgiannakis, and Yonghui Wu, “Natural TTS synthesis by conditioning WaveNet on Mel spectrogram predictions,” in Proc. ICASSP , 2018, pp. 4779–4783

This implementation is inspired from the following pioneers.

• https://github.com/keithito/tacotron
• https://github.com/Rayhane-mamah/Tacotron-2

Thank for outstanding papers and implementations.

Licence

BSD 3-Clause License

Copyright (c) 2018, Yamagishi Laboratory, National Institute of Informatics All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

• Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

• Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

• Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.