---
license: apache-2.0
language:
- de
library_name: nemo
---

This FastPitch[1] model was trained on the HUI-Audio-Corpus-German[2] clean dataset using the Nemo Toolkit[3]. 
We selected 5 speakers who have the 5-largest amount of data and balanced training data across speakers (around 20 hours per speaker).



This a retrained model of:
https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_de_fastpitch_multispeaker_5


# How to Use:
Use with Nemo Toolkit
```python
  # Load spectrogram generator
  from nemo.collections.tts.models import FastPitchModel
  spec_generator = FastPitchModel.restore_from("path/to/model.nemo")
  
  # Load Vocoder
  from nemo.collections.tts.models import HifiGanModel
  model = HifiGanModel.from_pretrained(model_name="tts_de_hui_hifigan_ft_fastpitch_multispeaker_5")
  
  # Generate audio
  import soundfile as sf
  parsed = spec_generator.parse("")
  speaker_id = 0
  spectrogram = spec_generator.generate_spectrogram(tokens=parsed, speaker=10)
  audio = model.convert_spectrogram_to_audio(spec=spectrogram)
  
  # Save the audio to disk in a file called speech.wav
  sf.write("speech.wav", audio.to('cpu').numpy(), 44100)
```



[1] FastPitch: Parallel Text-to-speech with Pitch Prediction: https://arxiv.org/abs/2006.06873
[2] HUI-Audio-Corpus-German Dataset: https://opendata.iisys.de/datasets.html
[3] NVIDIA NeMo Toolkit: https://github.com/NVIDIA/NeMo