import torch
waveglow = torch.hub.load('nvidia/DeepLearningExamples:torchhub', 'nvidia_waveglow')

will load the WaveGlow model pre-trained on LJ Speech dataset

Model Description

The Tacotron 2 and WaveGlow model form a text-to-speech system that enables user to synthesise a natural sounding speech from raw transcripts without any additional prosody information. The Tacotron 2 model (also available via torch.hub) produces mel spectrograms from input text using encoder-decoder architecture. WaveGlow is a flow-based model that consumes the mel spectrograms to generate speech.


In the example below:

  • pretrained Tacotron2 and Waveglow models are loaded from torch.hub
  • Tacotron2 generates mel spectrogram given tensor represantation of an input text (“Hello world, I missed you”)
  • Waveglow generates sound given the mel spectrogram
  • the output sound is saved in an ‘audio.wav’ file

To run the example you need some extra python packages installed. These are needed for preprocessing the text and audio, as well as for display and input / output.

pip install numpy scipy librosa unidecode inflect librosa
import numpy as np
from import write

Prepare the waveglow model for inference

waveglow = waveglow.remove_weightnorm(waveglow)
waveglow ='cuda')

Load tacotron2 from PyTorch Hub

tacotron2 = torch.hub.load('nvidia/DeepLearningExamples:torchhub', 'nvidia_tacotron2')
tacotron2 ='cuda')

Now, let’s make the model say “hello world, I missed you”

text = "hello world, I missed you"

Now chain pre-processing -> tacotron2 -> waveglow

# preprocessing
sequence = np.array(tacotron2.text_to_sequence(text, ['english_cleaners']))[None, :]
sequence = torch.from_numpy(sequence).to(device='cuda', dtype=torch.int64)

# run the models
with torch.no_grad():
    _, mel, _, _ = tacotron2.infer(sequence)
    audio = waveglow.infer(mel)
audio_numpy = audio[0].data.cpu().numpy()
rate = 22050

You can write it to a file and listen to it

write("audio.wav", rate, audio_numpy)

Alternatively, play it right away in a notebook with IPython widgets

from IPython.display import Audio
Audio(audio_numpy, rate=rate)


For detailed information on model input and output, training recipies, inference and performance visit: github and/or NGC