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---
license: cc-by-4.0
language:
- ru
library_name: nemo
---
### How to use
See example in this [notebook](https://github.com/bene-ges/nemo_compatible/blob/main/notebooks/Russian_TTS_with_IPA_G2P_FastPitch_and_HifiGAN.ipynb). It contains the inference pipeline for G2P + FastPitch + Hifigan.
### Input
This model expects text converted to IPA-like transcriptions. See this [g2p model](https://huggingface.co/bene-ges/ru_g2p_ipa_bert_large) for conversion of plain Russian text to phonemes.
If you feed plain text directly, it 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.
### Datasets
This model is trained on [RUSLAN](https://ruslan-corpus.github.io/) [2] corpus (single speaker, male voice) sampled at 22050Hz.
## References
- [1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
- [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 |