
### 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 accepts batches of mel spectrograms.

### Output

This model outputs audio at 22050Hz.

## Training

The NeMo toolkit [1] was used for training the model for several 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": ["tts", "text-to-speech", "Vocoder"]}
nemo tts text-to-speech Vocoder ru license:cc-by-nc-4.0 region:us
TAGS
#nemo #tts #text-to-speech #Vocoder #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 accepts batches of mel spectrograms.

### Output

This model outputs audio at 22050Hz.

## Training

The NeMo toolkit [1] was used for training the model for several 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 accepts batches of mel spectrograms. ### Output

This model outputs audio at 22050Hz. ## Training

The NeMo toolkit [1] was used for training the model for several 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 #tts #text-to-speech #Vocoder #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 accepts batches of mel spectrograms. ### Output

This model outputs audio at 22050Hz. ## Training

The NeMo toolkit [1] was used for training the model for several 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 #tts #text-to-speech #Vocoder #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 accepts batches of mel spectrograms.### Output

This model outputs audio at 22050Hz.## Training

The NeMo toolkit [1] was used for training the model for several 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