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Model Overview

VITS is an flow-based parallel end-to-end speech synthesis model. It consists of 2 encoders: TextEncoder and PosteriorEncoder (for spectrograms), normalizing flow, duration predictor and HiFiGAN vocoder.

During training, TextEncoder produces representation of text tokens and PosteriorEncoder produces representation of spectrograms are then fed to normalizing flow. This two representatons are then trained to match with KL divergence as in VAE. Also spectrogram representation are fed to MAS block to produce labels for Duration Predictor training.

During inference, Duration Predictor generates alignments from noise with conditioning on TextEncoder representations. Then they are passed through inversed flow to Vocoder block, which generates audios.

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

# Load VITS\nfrom nemo.collections.tts.models import VitsModel\naudio_generator = VitsModel.from_pretrained(\"tts_en_lj_vits\")\n\n# Generate audio\nimport soundfile as sf\nimport torch\nwith torch.no_grad():\n    parsed = audio_generator.parse(\"You can type your sentence here to get nemo to produce speech.\")\n    audio = audio_generator.convert_text_to_waveform(tokens=parsed)\n\n# Save the audio to disk in a file called speech.wav\nif isinstance(audio, torch.Tensor):\n    audio = audio.to('cpu').numpy()\nsf.write(\"speech.wav\", audio.T, 22050, format=\"WAV\")\n

Input

This model accepts batches of text.

Output

This model outputs audio at 22050Hz.

Limitations

There are no known limitations at this time.

Versions

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

References

VITS paper: https://arxiv.org/abs/2106.06103
\ngithub: https://github.com/jaywalnut310/vits

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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It consists of 2 encoders: TextEncoder and PosteriorEncoder (for spectrograms), normalizing flow, duration predictor and HiFiGAN vocoder.\n \nDuring training, TextEncoder produces representation of text tokens and PosteriorEncoder produces representation of spectrograms are then fed to normalizing flow. This two representatons are then trained to match with KL divergence as in VAE. Also spectrogram representation are fed to MAS block to produce labels for Duration Predictor training. \n\nDuring inference, Duration Predictor generates alignments from noise with conditioning on TextEncoder representations. Then they are passed through inversed flow to Vocoder block, which generates audios.\n\n## Training\n\nThis model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent.\n\n## Performance\n\nNo performance information available at this time.\n\n## How to Use this Model\n\n```python\n# Load VITS\nfrom nemo.collections.tts.models import VitsModel\naudio_generator = VitsModel.from_pretrained(\"tts_en_lj_vits\")\n\n# Generate audio\nimport soundfile as sf\nimport torch\nwith torch.no_grad():\n parsed = audio_generator.parse(\"You can type your sentence here to get nemo to produce speech.\")\n audio = audio_generator.convert_text_to_waveform(tokens=parsed)\n\n# Save the audio to disk in a file called speech.wav\nif isinstance(audio, torch.Tensor):\n audio = audio.to('cpu').numpy()\nsf.write(\"speech.wav\", audio.T, 22050, format=\"WAV\")\n```\n### Input\n\nThis model accepts batches of text.\n\n\n### Output\n\nThis model outputs audio at 22050Hz.\n\n## Limitations\n\nThere are no known limitations at this time.\n\n## Versions\n\n1.0.0 (current): The original version released with NeMo 1.0.0.\n\n## References\n\nVITS paper: [https://arxiv.org/abs/2106.06103](https://arxiv.org/abs/2106.06103) \ngithub: [https://github.com/jaywalnut310/vits](https://github.com/jaywalnut310/vits) \n## Licence\n\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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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578],[525,526,527,528,529],{"_364":439,"_206":439,"_168":210,"_337":549},{"_364":413,"_206":413,"_168":210,"_337":544},{"_364":539,"_206":539,"_168":210,"_337":540},{"_364":534,"_206":534,"_168":210,"_337":535},{"_364":530,"_206":530,"_168":210,"_337":531},"nvidia.partners",[532],{"_79":533,"_370":530,"_371":31,"_168":407},"edge","app.hushly.com",[536],{"_79":537,"_370":534,"_371":31,"_168":538},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[541],{"_79":542,"_370":539,"_371":31,"_168":543},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. 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