nnmnkwii (nanamin kawaii) documentation

Library to build speech synthesis systems designed for easy and fast prototyping.

• Github: https://github.com/r9y9/nnmnkwii

• Tutorial notebooks: https://github.com/r9y9/nnmnkwii_gallery

For advanced applications using the library, see External links.

Notes

• General design documentation
• 設計ドキュメント (Japanese)
• A quick start guide

Tutorials

• DNN text-to-speech synthesis (en)
• Bidirectional-LSTM based RNNs for text-to-speech synthesis (en)
• Bidirectional-LSTM based RNNs for text-to-speech synthesis with OpenJTalk (ja)
• GMM-based voice conversion (en)

Package references

• Autograd
• Baseline
• Datasets
• Frontend
• Functions
• IO
• Evaluation metrics
• Parameter generation
• Post-filters
• Pre-processing
• Utilities

Meta information

• Change log

External links

• wavenet_vocoder: WaveNet vocoder 6 7

• deepvoice3_pytorch: PyTorch implementation of convolutional networks-based text-to-speech synthesis models. 4 5

• tacotron_pytorch: PyTorch implementation of Tacotron speech synthesis model. 3

• gantts: PyTorch implementation of GAN-based text-to-speech synthesis and voice conversion (VC). 1 2

• icassp2020-espnet-tts-merlin-baseline: ICASSP 2020 ESPnet-TTS: Merlin baseline system 8

1

Saito, Yuki, Shinnosuke Takamichi, and Hiroshi Saruwatari. “Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks.” IEEE/ACM Transactions on Audio, Speech, and Language Processing 26.1 (2018): 84-96.

2

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.

3

Yuxuan Wang, RJ Skerry-Ryan, Daisy Stanton et al, “Tacotron: Towards End-to-End Speech Synthesis”, arXiv:1703.10135, Mar 2017.

4

Wei Ping, Kainan Peng, Andrew Gibiansky, et al, “Deep Voice 3: 2000-Speaker Neural Text-to-Speech”, arXiv:1710.07654, Oct. 2017.

5

Hideyuki Tachibana, Katsuya Uenoyama, Shunsuke Aihara, “Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention”. arXiv:1710.08969, Oct 2017.

6

Aaron van den Oord, Sander Dieleman, Heiga Zen, et al, “WaveNet: A Generative Model for Raw Audio”, arXiv:1609.03499, Sep 2016.

7

Tamamori Akira, Tomoki Hayashi, Kazuhiro Kobayashi, et al. “Speaker-dependent WaveNet vocoder.” Proceedings of Interspeech. 2017.

8

‘T. Hayashi, R. Yamamoto, K. Inoue, T. Yoshimura, S. Watanabe,T. Toda, K. Takeda, Y. Zhang, and X. Tan, ESPnet-TTS: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit,” arXiv:1910.10909, 2019.

Indices and tables

• Index

• Module Index

• Search Page