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.
This model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent.
No performance information available at this time.
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)
This model accepts batches of mel spectrograms.
This model outputs audio at 22050Hz.
There are no known limitations at this time.
1.7.0: Add model (tts_en_lj_univnet.nemo) which was released with NeMo 1.7.0.
UnivNet paper: https://arxiv.org/abs/2106.07889