Update README.md

README.md

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 license: apache-2.0
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+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