TTS En LibriTTS UnivNet

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 all LibriTTS training data (train-clean-100, train-clean-360, and train-other-500) sampled at 22050Hz, and has been tested on generating English voices.

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_libritts_multispeaker_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_libritts_multispeaker_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.