Percival: CNN-WGAN acoustic model for text-to-speech synthesis

Based on Python/TensorFlow/Keras, using Wasserstein GAN and training regularization to optimise 2D convolutional layers.

It uses festival and Merlin scripts to generate the input text labels. The PML vocoder is used for the waveform synthesis.

Inspired by

Wasserstein GAN article

https://github.com/eriklindernoren/Keras-GAN/blob/master/wgan_gp/wgan_gp.py
https://github.com/keras-team/keras-contrib/blob/master/examples/improved_wgan.py
https://gist.github.com/f0k/f3190ebba6c53887d598d03119ca2066
https://github.com/martinarjovsky/WassersteinGAN
https://github.com/fairytale0011/Conditional-WassersteinGAN
http://blog.richardweiss.org/2017/07/21/conditional-wasserstein-gan.html

Improved training for Wasserstein GAN article

https://github.com/tjwei/GANotebooks/blob/master/wgan2-lasagne.ipynb
https://github.com/ririw/ririw.github.io/blob/master/assets/conditional-wasserstein-gans/Improved.ipynb

Least Square mixing article

PixelCNN article

https://arxiv.org/pdf/1704.03809.pdf
http://www.dtic.upf.edu/~mblaauw/MdM_NIPS_seminar/

Legal

Copyright(C) 2017 Engineering Department, University of Cambridge, UK.

The code in this repository is released under the Apache License, Version 2.0. Please see LICENSE.md file for more details.

All source files of any kind (code source and any ressources), except the content of the 'external' directory, are under the same license. Please refer to the content of the 'external' directory for the legal issues related to those code source.

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 655764.

Disclaimer

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 OWNER 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. ALSO, THE COPYRIGHT HOLDERS AND CONTRIBUTORS DO NOT TAKE ANY LEGAL RESPONSIBILITY REGARDING THE IMPLEMENTATIONS OF THE PROCESSING TECHNIQUES OR ALGORITHMS (E.G. CONSEQUENCES OF BUGS OR ERRONEOUS IMPLEMENTATIONS).

Dependencies and working versions

Percival is not a standalone pipeline for TTS. It only trains an acoustic model. Technically, it is dependent on a text-to-audio alignment system, which usually provides context input labels (e.g. in HTS format; as label_state_align directory in Merlin).

Dealing with the numerous dependencies between the libraries and tools can also be a nightmare. We strongly suggest to use a package manager conda or miniconda on top of the OS package manager. The following conda commands have been found to give a working environment for PercivalTTS:

conda install tensorflow-gpu cython ipython matplotlib
conda install pip
pip install --upgrade pip
pip install py-cpuinfo defusedxml bandmat

This has been tested with Nvidia Drivers 390.77 with cudatoolkit=9.0 Depending in your driver, you might need to downgrade to a compatible CUDA toolkit. E.g.:

conda install cudatoolkit=9.0

To apply post-processing for formant enhancement, you need to have mcep command line from SPTK By default, un-post-processed and post-processed samples are generated in the out directory.

Install and run the demo

First clone the git repository:

$ git clone https://gitlab.com/gillesdegottex/percivaltts.git

Then, go into the repository directory and compile the various tools neccessary to run Percival:

$ cd percivaltts
$ make build

Download the demo data:

$ make tests/slt_arctic_merlin_full

Go into the percivaltts python module directory:

$ cd percivaltts

Edit setenv.sh according to your TensorFlow/Keras installation (see above).

And finally run the demo!

$ make run

A web page with the expected demonstration samples can be found here

Preparing a new voice

Like in Merlin, you basically need the three following elements from any corpus:

• label_state_align

  The same directory that is imported in Merlin. It is produced by an alignement tool, as in HTS. Merlin provides the necessary scripts for generating the text labels from text inputs and align these text labels on some given waveform.

  Phone aligned labels can also be used. In this case you have to pass subphone_feats='coarse_coding' to HTSLabelNormalisation constructor and pass label_type='phone_align' to label_normaliser.perform_normalisation

• wav

  The waveforms directory aligned with the labels above

• file_id_list.scp

  The same file that you find in Merlin, which contains the basenames of each file in label_state_align and wav.

Put this somehwere in a common directory and point the cp variable in the run.py file to this directory.

Formats

The are a few assumptions across the code about data formats.

First, floating point precision values are always saved on disc using the float32 format.

Data set

The basenames of the corpus files are listed in a file (e.g. file_id_list.scp). This list is then split into [traning; validation; test] sets, always in this order. The validation start starts at id_valid_start and contains id_valid_nb files. The test set directly follows the validation set and contains id_test_nb.

Because the size of the training set is always an interger multiple of the batch size, the training set might have less than id_valid_start files. The id_valid_start-1 last files right before id_valid_start might thus be completely ignored by the training process.

A last set exists, the demo set, which is a subset of the test set. This is convenient for generating and listening quickly to a few known sentences after a training. By default it is the first 10 sentences of the test set. id_test_demostart can be used to select the starting index (relative to the test set) in order to chose where the demo set starts within the test set.

During training, percival uses batches that have a small time window (2sec by default). Thus, when a sentences is picked for training, only this time window is used. There is two main advantage of this data formating: i) the memory size on the GPU is dependent on the duration of this time window and not on the random selection of the sentences; ii) each batch is full, in the sense that it doesn't need any zero padding at the end of short sentences, which means that masks are neither necessary. However, an epoch is not a full epoch in the sense that it does not see all of the training data. This explains why the number of "epoch" is quite huge (300) by default in order to compensate for the unseen data.

File access and shapes

To represent multiple files in a directory, file paths are usually defined with a wildcard (e.g. waveforms/*.wav). Because input and output data of the network (lab and cmp files) are saved in raw float32 format, without header, it is not possible to know the actual dimensions of the data inside each file. In Percival, the trick is to specify the shape of the data as a suffix of the file path, e.g. spectrum/*.spec:(-1,129). this suffix will be used, as is, to reshape the numpy array using np.reshape(.).

Batches

A batch has a shape: [size, length, features_dim], that represent the number of samples in the batches, the number of time frames in the batch and the feature dimensionality, respectively. Because TensorFlow/Keras needs a "channel" dimension (as in pictures), batches' shape often become temporarily [size, 1, length, features_dim] so that the last two dimensions define a picture of size [length, features_dim].

Features order

The implementation of the models assume the following features order: f0, amplitude spectrum (spec), noise (e.g. aperiodicity noise mask), vuv.

Training optimisation

Even though WGAN is more robust than the original GAN, there are still a few elements to verify to be sure a training went well.

1. The discri_training has to be higher than zero. If it goes negative, it surely means the either the critic or the generator converges too fast.
2. model_training_wgan_lse_ratio is the ratio between the WGAN loss and the LSE loss. If it goes very close to zero, it means the LSE loss is drowning the WGAN loss. In such a case, you might want to decrease cfg.train_LScoef closer to zero.
3. if discri_validation doesn't decrease overall over the end of your training, it might be that you should run your training longer.

Running experiments / Code cloning

Managing multiple experiments in parallel, fixing bugs and try new ideas while keeping track of all the changes this can imply can become quite problematic. In Percival, there is a simple script clone.sh that allows you to clone the whole code source and place it in an experiment directory:

$ ./clone.sh /path/to/experiment1

or clone and run the run.py script file directly:

$ ./clone.sh /path/to/experiment1 bash ../percivaltts/setenv.sh python ../percivaltts/run.py

when a script is runned using clone.sh, the working directory is /path/to/experiment1/out so that all of the experiment results are in this out sub-directory and all the code is in a separate sub-directory percivaltts. You can also replace the bash command by a script for submitting the job to a Sun Grid Engine (SGE). The Makefile has also all the corresponding commands (clone, run, clone_run_grid, etc.)

Author/Contributor

Gilles Degottex [email protected]

Contact

To raise bug reports, questions, suggestions, etc. please use the issue managment only.