VCTK multi-speaker tacotron for ICASSP 2020
This is an implementation of our paper to appear at ICASSP 2020:
"Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings," by Erica Cooper, Cheng-I Lai, Yusuke Yasuda, Fuming Fang, Xin Wang, Nanxin Chen, and Junichi Yamagishi.
Please cite this paper if you use this code.
Audio samples can be found here: https://nii-yamagishilab.github.io/samples-multi-speaker-tacotron/
is20and please also update your copies of
self-attention-tacotronrepositories as these contain some necessary changes.
It is recommended to set up a miniconda environment for using Tacotron. https://repo.anaconda.com
conda create -n taco python=3.6.8 conda activate taco conda install tensorflow-gpu scipy matplotlib docopt hypothesis pyspark unidecode conda install -c conda-forge librosa pip install inflect pysptk
Install this repository
git clone https://github.com/nii-yamagishilab/multi-speaker-tacotron.git external/multi_speaker_tacotron
Install Tacotron dependencies if you don't have them already:
mkdir external git clone https://github.com/nii-yamagishilab/tacotron2.git external/tacotron2 git clone https://github.com/nii-yamagishilab/self-attention-tacotron.git external/self_attention_tacotronNote the renaming of hyphens to underscores; this is necessary because “-” is an invalid character in Python.
Next, download project data and models, from the dropbox folder here: https://www.dropbox.com/sh/rq4lebus0n8tmso/AACldbmKDPRN9YiXrRROjtTSa?dl=0 * Preprocessed VCTK data: in the
datadirectory * VCTK Tacotron models: in the
tacotron-modelsdirectory * VCTK Wavenet models: in the
wavenet-modelsdirectory * Nancy model for parameter initialization: TBA
Training from scratch using the VCTK data only is possible using the script
train_from_scratch.sh; this does not require the Nancy pre-trained model.
To use our pre-trained WaveNet models, you will also need our WaveNet implementation which can be found here: https://github.com/nii-yamagishilab/project-CURRENNT-scripts
To obtain embeddings for new samples, you will need the neural speaker embedding code which can be found here: https://github.com/jefflai108/pytorch-kaldi-neural-speaker-embeddings
See the scripts
warmup.sh(warm start training),
train_from_scratch.sh(train on VCTK data only), and
predictmel.sh(prediction). The scripts assume a SLURM-type computing environment. You will need to change the paths to match your environments and point to your data. Here are the parameters relevant to multi-speaker TTS: *
target-data-root: path to your source and target preprocessed data *
selected-list-dir: train/eval/test set definitions *
batch_size: if you get OOM errors, try reducing the batch size *
use_external_speaker_embedding=True: use speaker embeddings that you provide from a file (see the files in the
embedding_file: path to the file containing your speaker embeddings *
speaker_embedding_dim: dimension should match the dimension in your embedding file <!-- TODO: deprecate this --> *
speaker_embedding_projection_out_dim=64: We found experimentally that projecting the speaker embedding to a lower dimension helped to reduce overfitting. You can try different values, but to use our pretrained multi-speaker models you will have to use 64. *
speaker_embedding_offset: must match the ID of your first speaker. <!-- TODO: deprecate this -->
The scripts are set up using
embedding_file="vctk-x-vector.txt",speaker_embedding_dim='200'which is default x-vectors. Please change it to
embedding_file="vctk-lde-3.txt",speaker_embedding_dim='512'to use LDE embeddings from our best system.
This work was partially supported by a JST CREST Grant (JPMJCR18A6, VoicePersonae project), Japan, and by MEXT KAKENHI Grants (16H06302, 17H04687, 18H04120, 18H04112, 18KT0051, 19K24372), Japan. The numerical calculations were carried out on the TSUBAME 3.0 supercomputer at the Tokyo Institute of Technology.
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