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WaveGlow
WaveGlow model for generating speech from mel spectrograms (generated by Tacotron2)
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NVIDIA
audio
NVIDIA/DeepLearningExamples
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cuda
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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.

Example

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 so much")
  • 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
apt-get update
apt-get install -y libsndfile1

Load the WaveGlow model pre-trained on LJ Speech dataset

import torch
waveglow = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_waveglow', model_math='fp32')

Prepare the WaveGlow model for inference

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

Load a pretrained Tacotron2 model

tacotron2 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_tacotron2', model_math='fp32')
tacotron2 = tacotron2.to('cuda')
tacotron2.eval()

Now, let's make the model say:

text = "hello world, I missed you so much"

Format the input using utility methods

utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_tts_utils')
sequences, lengths = utils.prepare_input_sequence([text])

Run the chained models

with torch.no_grad():
    mel, _, _ = tacotron2.infer(sequences, lengths)
    audio = waveglow.infer(mel)
audio_numpy = audio[0].data.cpu().numpy()
rate = 22050

You can write it to a file and listen to it

from scipy.io.wavfile import write
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)

Details

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

References