Speech Synthesis: WaveGlow Model Card

Model overview

WaveGlow is a neural vocoder model for text-to-speech applications. It is intended as the second part of a two-stage speech synthesis\npipeline, with a mel-spectrogram generator such as Tacotron2 as the first stage.

Model architecture

WaveGlow is a Glow-based (alternatively flow-based) model that generates audio conditioned on mel spectrograms. WaveGlow\nis a reversible neural network. It can be run in two modes: the first mode takes audio and transforms it to samples\ndrawn from a normal distribution, and the second mode takes samples from a normal distribution and transforms it to\naudio. Both modes are conditioned on a mel spectrogram.

Training

This model can be used to generate most voices in most languages without\nretraining. We have observed this trained WaveGlow to generate English audio\nand Mandarin audio.

Dataset

This model is trained on the LJSpeech dataset sampled at 22050 Hz.

How to use this model

WaveGlow is intended to be used as the second part of a two stage speech synthesis pipeline. WaveGlow takes a mel\nspectrogram and returns audio.

Input

Mel spectrogram of shape (batch x mel_channels x time)

Output

Audio of shape (batch x time)

Limitations

N/A

References

WaveGlow paper: https://arxiv.org/abs/1811.00002

License

By downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the terms of the Riva license

Ethical AI

NVIDIA’s platforms and application frameworks enable developers to build a wide\narray of AI applications. Consider potential algorithmic bias when choosing or\ncreating the models being deployed. Work with the model’s developer to ensure\nthat it meets the requirements for the relevant industry and use case; that the\nnecessary instruction and documentation are provided to understand error rates,\nconfidence intervals, and results; and that the model is being used under the\nconditions and in the manner intended.

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It is intended as the second part of a two-stage speech synthesis\npipeline, with a mel-spectrogram generator such as Tacotron2 as the first stage.\n\nModel architecture\n------------------\nWaveGlow is a Glow-based (alternatively flow-based) model that generates audio conditioned on mel spectrograms. WaveGlow\nis a reversible neural network. It can be run in two modes: the first mode takes audio and transforms it to samples\ndrawn from a normal distribution, and the second mode takes samples from a normal distribution and transforms it to\naudio. Both modes are conditioned on a mel spectrogram.\n\nTraining\n--------\nThis model can be used to generate most voices in most languages without\nretraining. We have observed this trained WaveGlow to generate English audio\nand Mandarin audio.\n\n### Dataset\nThis model is trained on the LJSpeech dataset sampled at 22050 Hz.\n\nHow to use this model\n---------------------\nWaveGlow is intended to be used as the second part of a two stage speech synthesis pipeline. WaveGlow takes a mel\nspectrogram and returns audio.\n\n### Input\nMel spectrogram of shape (batch x mel_channels x time)\n\n### Output\nAudio of shape (batch x time)\n\nLimitations\n-----------\nN/A\n\nReferences\n----------\nWaveGlow paper: https://arxiv.org/abs/1811.00002\n\nLicense\n-------\nBy downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/ga/license)\n\nEthical AI\n----------\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide\narray of AI applications. 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