Model Overview

GlowTTS is a Glow-based (alternatively flow-based) model that generates mel spectrograms from text.

Model Architecture

For more information about the model architecture, see the GlowTTS paper [1].

Training

This model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent.

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 22050Hz vocoder from NeMo. This example uses the HiFi-GAN model.

# Load GlowTTS\nfrom nemo.collections.tts.models import GlowTTSModel\nspec_generator = GlowTTSModel.from_pretrained(\"tts_en_glowtts\")\n\n# Load vocoder\nfrom nemo.collections.tts.models import Vocoder\nmodel = Vocoder.from_pretrained(model_name=\"tts_hifigan\")\n\n# Generate audio\nimport soundfile as sf\nparsed = spec_generator.parse(\"You can type your sentence here to get nemo to produce speech.\")\nspectrogram = spec_generator.generate_spectrogram(tokens=parsed)\naudio = model.convert_spectrogram_to_audio(spec=spectrogram)\n\n# Save the audio to disk in a file called speech.wav\nsf.write(\"speech.wav\", audio.to('cpu').numpy(), 22050)\n

Input

This model accepts batches of text.

Output

This model generates mel spectrograms.

Limitations

This checkpoint only works well with vocoders that were trained on 22050Hz data. Otherwise, the generated audio may be scratchy or choppy-sounding.

Versions

1.0.0rc1 (current): The original version released with NeMo 1.0.0rc1.

References

GlowTTS paper: https://arxiv.org/abs/2005.11129

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.

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This example uses the HiFi-GAN model.\n\n```python\n# Load GlowTTS\nfrom nemo.collections.tts.models import GlowTTSModel\nspec_generator = GlowTTSModel.from_pretrained(\"tts_en_glowtts\")\n\n# Load vocoder\nfrom nemo.collections.tts.models import Vocoder\nmodel = Vocoder.from_pretrained(model_name=\"tts_hifigan\")\n\n# Generate audio\nimport soundfile as sf\nparsed = spec_generator.parse(\"You can type your sentence here to get nemo to produce speech.\")\nspectrogram = spec_generator.generate_spectrogram(tokens=parsed)\naudio = model.convert_spectrogram_to_audio(spec=spectrogram)\n\n# Save the audio to disk in a file called speech.wav\nsf.write(\"speech.wav\", audio.to('cpu').numpy(), 22050)\n```\n\n### Input\nThis model accepts batches of text.\n\n### Output\nThis model generates mel spectrograms.\n\n## Limitations\nThis checkpoint only works well with vocoders that were trained on 22050Hz data. Otherwise, the generated audio may be scratchy or choppy-sounding.\n\n## Versions\n1.0.0rc1 (current): The original version released with NeMo 1.0.0rc1.\n\n## References\nGlowTTS paper: https://arxiv.org/abs/2005.11129\n\n## Licence\nLicense to use this model is covered by the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified. 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Key	Value
ARCHITECTURE	GLOW TTS
INPUTS	TEXT IN ENGLISH
OUTPUTS	MEL SPECTROGRAM