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---
license: apache-2.0
language:
- de
library_name: nemo
tags:
- tts
- pytorch
- FastPitch
- speech
pipeline_tag: text-to-speech
---
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 version 1.14.0
```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 torchaudio
parsed = spec_generator.parse("")
speaker_id = 0
spectrogram = spec_generator.generate_spectrogram(tokens=parsed, speaker=speaker_id)
audio = model.convert_spectrogram_to_audio(spec=spectrogram)
# Save the audio to disk in a file called speech.wav
torchaudio.save('german_speech.wav', audio.cpu(), 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 |