license: cc-by-nc-4.0
language:
- ru
library_name: nemo
tags:
- text-to-speech
- tts
How to use
See example of inference pipeline for Russian TTS (G2P + FastPitch + HifiGAN) in this notebook. Or use this bash-script.
Input
This model is indended to be used in a G2P + FastPitch + HifiGAN pipeline (see above). If run independently, it expects text converted to IPA-like transcriptions. See this g2p model for conversion of plain Russian words to phonemes, or this new IPA-compatible G2P tool that can handle ambiguitity on sentence level. If you feed plain text directly, this FastPitch model will work, but quality will be low.
Output
This model generates mel spectrograms.
Training
The NeMo toolkit [1] was used for training the model for 1000+ epochs. Full training script is here
Datasets
This model is trained on RUSLAN [2] corpus (single speaker, male voice) sampled at 22050Hz.
References
- [1] NVIDIA NeMo Toolkit
- [2] Gabdrakhmanov L., Garaev R., Razinkov E. (2019) RUSLAN: Russian Spoken Language Corpus for Speech Synthesis. In: Salah A., Karpov A., Potapova R. (eds) Speech and Computer. SPECOM 2019. Lecture Notes in Computer Science, vol 11658. Springer, Cham