PyTorch implementation of Generative adversarial Networks (GAN) based text-to-speech (TTS) and voice conversion (VC).
- Saito, Yuki, Shinnosuke Takamichi, and Hiroshi Saruwatari. "Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks." IEEE/ACM Transactions on Audio, Speech, and Language Processing (2017).
- Shan Yang, Lei Xie, Xiao Chen, Xiaoyan Lou, Xuan Zhu, Dongyan Huang, Haizhou Li, " Statistical Parametric Speech Synthesis Using Generative Adversarial Networks Under A Multi-task Learning Framework", arXiv:1707.01670, Jul 2017.
Audio samples are available in the Jupyter notebooks at the link below:
- Voice conversion (en, MLP)
- Voice conversion (en, RNN)
- Text-to-speech synthesis (en, MLP)
- Text-to-speech synthesis (ja, MLP)
adversarial_streams, which represents streams (mgc, lf0, vuv, bap) to be used to compute adversarial loss, is a very speech quality sensitive parameter. Computing adversarial loss on mgc features (except for first few dimensions) seems to be working good.- If
mask_nth_mgc_for_adv_loss> 0, firstmask_nth_mgc_for_adv_lossdimension for mgc will be ignored for computing adversarial loss. As described in saito2017asja, I confirmed that using 0-th (and 1-th) mgc for computing adversarial loss affects speech quality. From my experience,mask_nth_mgc_for_adv_loss= 1 for mgc order 25,mask_nth_mgc_for_adv_loss= 2 for mgc order 59 are working to me. - F0 extracted by WORLD will be spline interpolated. Set
f0_interpolation_kindto "slinear" if you want frist-order spline interpolation, which is same as Merlin's default. - Set
use_harvestto True if you want to use Harvest F0 estimation algorithm. If False, Dio and StoneMask are used to estimate/refine F0. - If you see
cuda runtime error (2) : out of memory, try smaller batch size. #3
Though I haven't got improvements over Saito's approach [1] yet, but the GAN-based models described in [2] should be achieved by the following configurations:
- Set
generator_add_noiseto True. This will enable generator to use Gaussian noise as input. Linguistic features are concatenated with the noise vector. - Set
discriminator_linguistic_conditionto True. The discriminator uses linguistic features as condition.
- PyTorch >= v0.2.0
- TensorFlow (just for
tf.contrib.training.HParams) - nnmnkwii
- PyWorld
- https://github.com/taolei87/sru (if you want to try SRU-based models)
- Python
Please install PyTorch, TensorFlow and SRU (if needed) first. Once you have those, then
git clone --recursive https://github.com/r9y9/gantts && cd gantts
pip install -e ".[train]"
should install all other dependencies.
- gantts/: Network definitions, utilities for working on sequence-loss optimization.
- prepare_features_vc.py: Acoustic feature extraction script for voice conversion.
- prepare_features_tts.py: Linguistic/duration/acoustic feature extraction script for TTS.
- train.py: GAN-based training script. This is written to be generic so that can be used for training voice conversion models as well as text-to-speech models (duration/acoustic).
- train_gan.sh: Adversarial training wrapper script for
train.py. - hparams.py: Hyper parameters for VC and TTS experiments.
- evaluation_vc.py: Evaluation script for VC.
- evaluation_tts.py: Evaluation script for TTS.
Feature extraction scripts are written for CMU ARCTIC dataset, but can be easily adapted for other datasets.
vc_demo.sh is a clb to clt voice conversion demo script. Before running the script, please download wav files for clb and slt from CMU ARCTIC and check that you have all data in a directory as follows:
> tree ~/data/cmu_arctic/ -d -L 1
/home/ryuichi/data/cmu_arctic/
├── cmu_us_awb_arctic
├── cmu_us_bdl_arctic
├── cmu_us_clb_arctic
├── cmu_us_jmk_arctic
├── cmu_us_ksp_arctic
├── cmu_us_rms_arctic
└── cmu_us_slt_arctic
Once you have downloaded datasets, then:
./vc_demo.sh ${experimental_id} ${your_cmu_arctic_data_root}
e.g.,
./vc_demo.sh vc_gan_test ~/data/cmu_arctic/
Model checkpoints will be saved at ./checkpoints/${experimental_id} and audio samples
are saved at ./generated/${experimental_id}.
tts_demo.sh is a self-contained TTS demo script. The usage is:
./tts_demo.sh ${experimental_id}
This will download slt_arctic_full_data used in Merlin's demo, perform feature extraction, train models and synthesize audio samples for eval/test set. ${experimenta_id} can be arbitrary string, for example,
./tts_demo.sh tts_test
Model checkpoints will be saved at ./checkpoints/${experimental_id} and audio samples
are saved at ./generated/${experimental_id}.
See hparams.py.
tensorboard --logdir=log
- Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari, "Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks", arXiv:1709.08041 [cs.SD], Sep. 2017
- Yuki Saito, Shinnosuke Takamichi, and Hiroshi Saruwatari, "Training algorithm to deceive anti-spoofing verification for DNN-based text-to-speech synthesis," IPSJ SIG Technical Report, 2017-SLP-115, no. 1, pp. 1-6, Feb., 2017. (in Japanese)
- Yuki Saito, Shinnosuke Takamichi, and Hiroshi Saruwatari, "Voice conversion using input-to-output highway networks," IEICE Transactions on Information and Systems, Vol.E100-D, No.8, pp.1925--1928, Aug. 2017
- https://www.slideshare.net/ShinnosukeTakamichi/dnnantispoofing
- https://www.slideshare.net/YukiSaito8/Saito2017icassp
The repository doesn't try to reproduce same results reported in their papers because 1) data is not publically available and 2). hyper parameters are highly depends on data. Instead, I tried same ideas on different data with different hyper parameters.
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