Model Overview

MelGAN is a generative adversarial network (GAN) model that generates audio from mel spectrograms. This checkpoint implements the full-band MelGAN as described in the Multi-band MelGAN paper [1].

Model Architecture

The MelGAN generator uses transposed convolutions to upscale by the mel spectrogram to audio.

Training

This model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent.\nAll NeMo models are trained in accordance with the model yaml. In particular, this model was trained on 8 16GB V100 gpus for 3000 epochs with a batch size of 64.

Performance

No performance information available at this time.

How to Use this Model

This model can be automatically loaded from NGC.\nNOTE: In order to generate audio, you also need a spectrogram generator from NeMo. This example uses the FastPitch model.

# Load spectrogram generator\nfrom nemo.collections.tts.models import FastPitchModel\nspec_generator = FastPitchModel.from_pretrained(\"tts_en_fastpitch\")\n\n# Load Melgan\nfrom nemo.collections.tts.models import MelGanModel\nmodel = MelGanModel.from_pretrained(model_name=\"tts_melgan\")\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 mel spectrograms.

Output

This model outputs audio at 22050Hz.

Limitations

There are no known limitations at this time.

Versions

1.0.0 (current): An updated version of melgan that standardizes mel spectrogram generation across NeMo models.

1.0.0rc1: The original version that was released with NeMo 1.0.0.rc1

References

MelGAN paper: https://arxiv.org/abs/1910.06711

Multi-band MelGAN paper: https://arxiv.org/abs/2005.05106

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 checkpoint implements the full-band MelGAN as described in the Multi-band MelGAN paper [1].\n\n## Model Architecture\n\nThe MelGAN generator uses transposed convolutions to upscale by the mel spectrogram to audio.\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.\nAll NeMo models are trained in accordance with the model yaml. In particular, this model was trained on 8 16GB V100 gpus for 3000 epochs with a batch size of 64.\n\n## Performance\nNo performance information available at this time.\n\n## How to Use this Model\n\nThis model can be automatically loaded from NGC.\nNOTE: In order to generate audio, you also need a spectrogram generator from NeMo. This example uses the FastPitch model.\n```python\n# Load spectrogram generator\nfrom nemo.collections.tts.models import FastPitchModel\nspec_generator = FastPitchModel.from_pretrained(\"tts_en_fastpitch\")\n\n# Load Melgan\nfrom nemo.collections.tts.models import MelGanModel\nmodel = MelGanModel.from_pretrained(model_name=\"tts_melgan\")\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 mel spectrograms.\n\n### Output\nThis model outputs audio at 22050Hz.\n\n## Limitations\nThere are no known limitations at this time.\n\n## Versions\n1.0.0 (current): An updated version of melgan that standardizes mel spectrogram generation across NeMo models.\n\n1.0.0rc1: The original version that was released with NeMo 1.0.0.rc1\n\n## References\nMelGAN paper: https://arxiv.org/abs/1910.06711\n\nMulti-band MelGAN paper: https://arxiv.org/abs/2005.05106\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. By downloading the public and release version of the model, you accept the terms and conditions of the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms).","TTS Vocoder Melgan",["D",1680638546404],"/api/image/nvidia/nemo/models/tts_melgan","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","bias","explainability","privacy","safetyAndSecurity",[],[288,289,290,291,292],[],[],[],{"_90":302,"_130":302,"_131":303,"_133":134},{"_90":299,"_130":299,"_131":300,"_133":134},{"_90":297,"_130":297,"_131":298,"_133":134},{"_90":295,"_130":295,"_131":296,"_133":134},{"_90":293,"_130":293,"_131":294,"_133":134},"Vocoder",{"_135":134,"_130":293},"TTS",{"_135":134,"_130":295},"PytorchLightning",{"_135":134,"_130":297},"NeMo",{"_135":301,"_130":299},"framework","Melgan",{"_135":134,"_130":302},{"_178":1473,"_180":1474},"ngcApps",[1426],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1407":1408,"_1409":1410,"_1411":1412,"_1413":1414,"_1415":315,"_210":339,"_1416":1417,"_1418":51,"_1419":157,"_1420":157,"_1421":1422,"_1423":157,"_1424":1425},"amplitude",{"_1404":1405,"_1010":-7,"_1406":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1398":1399,"_1400":1401,"_1402":1403},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1285":238,"_1286":1287,"_1288":1289,"_1290":1291},"announcements",[],"consentState",{"_376":1275,"_1276":157,"_1277":26,"_1278":26,"_1279":1280},"consentConfig",{"_358":359,"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377},"showContactAdmin",1784011332885,[],[],[353,354,355,356,357],{"_40":55,"_42":65,"_44":173,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":173,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":173,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":173,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":173,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","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. 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