Model Overview
Model Architecture
UnivNet is a generative adversarial network (GAN) model that generates audio from mel spectrograms. The generator uses transposed convolutions to upsample mel-spectrograms to audio.
Training
Dataset
This model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent.
Performance
No performance information available at this time.
How to Use this Model
This model can be automatically loaded from NGC.
NOTE: In order to generate audio, you also need a spectrogram generator from NeMo. This example uses the FastPitch model.
# Load PastPitch
from nemo.collections.tts.models import FastPitchModel
spec_generator = FastPitchModel.from_pretrained("tts_en_fastpitch")
# Load UnivNet
from nemo.collections.tts.models import UnivNetModel
model = UnivNetModel.from_pretrained(model_name="tts_en_lj_univnet")
# Generate audio
import soundfile as sf
parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.")
spectrogram = spec_generator.generate_spectrogram(tokens=parsed)
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(), 22050)
Input
This model accepts batches of mel spectrograms.
Output
This model outputs audio at 22050Hz.
Limitations
There are no known limitations at this time.
Versions
1.7.0: Add model (tts_en_lj_univnet.nemo) which was released with NeMo 1.7.0.
References
UnivNet paper: https://arxiv.org/abs/2106.07889
Licence
License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.