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license: cc-by-4.0 |
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language: |
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- ru |
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library_name: nemo |
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--- |
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### How to use |
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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. |
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### Input |
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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. |
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If you feed plain text directly, it will work, but quality will be low. |
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### Output |
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This model generates mel spectrograms. |
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## Training |
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The NeMo toolkit [1] was used for training the model for 1000+ epochs. |
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### Datasets |
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This model is trained on [RUSLAN](https://ruslan-corpus.github.io/) [2] corpus (single speaker, male voice) sampled at 22050Hz. |
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## References |
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- [1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) |
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- [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 |
