
### How to use

See example of inference pipeline for Russian TTS (G2P + FastPitch + HifiGAN) in this [notebook](https://github.com/bene-ges/nemo_compatible/blob/main/notebooks/Russian_TTS_with_IPA_G2P_FastPitch_and_HifiGAN.ipynb).
Or use this [bash-script](https://github.com/bene-ges/nemo_compatible/blob/main/scripts/tts/ru_ipa_fastpitch_hifigan/test.sh).

### 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](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.
Full training script is [here](https://github.com/bene-ges/nemo_compatible/blob/main/scripts/tts/ru_ipa_fastpitch_hifigan/train.sh)

### 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
{"language": ["ru"], "license": "cc-by-nc-4.0", "library_name": "nemo", "tags": ["text-to-speech", "tts"]}
nemo text-to-speech tts ru license:cc-by-nc-4.0 region:us
TAGS
#nemo #text-to-speech #tts #ru #license-cc-by-nc-4.0 #region-us 


### 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 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.
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
### 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 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.
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
TAGS
#nemo #text-to-speech #tts #ru #license-cc-by-nc-4.0 #region-us 
 ### 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 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.
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
TAGS
#nemo #text-to-speech #tts #ru #license-cc-by-nc-4.0 #region-us 
### 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 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.
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