Model Overview

Mixer-TTS-X is non-autoregressive model for mel-spectrogram generation. The model is based on the MLP-Mixer architecture adapted for speech synthesis. It contains pitch and duration predictors, with the latter being trained with an unsupervised TTS alignment framework, and additionally uses token embeddings from a pre-trained language model ALBERT.

Model Architecture

For more information about the model architecture, see the Mixer-TTS paper which describes Mixer-TTS-X.

Training

Dataset

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 which was additionally fine-tuned on Mixer-TTS-X outputs.

# Load Mixer-TTS-X\nfrom nemo.collections.tts.models import MixerTTSModel\nspec_generator = MixerTTSModel.from_pretrained(\"tts_en_lj_mixerttsx\")\n\n# Load vocoder\nfrom nemo.collections.tts.models import Vocoder\nmodel = Vocoder.from_pretrained(model_name=\"tts_en_lj_hifigan_ft_mixerttsx\")\n\n# Generate audio\nimport soundfile as sf\nraw_text = \"You can type your sentence here to get nemo to produce speech.\"\nparsed = spec_generator.parse(raw_text)\nspectrogram = spec_generator.generate_spectrogram(tokens=parsed, raw_texts=[raw_text])\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

1.6.0: The original version of Mixer-TTS-X model which was released with NeMo 1.6.0.

References

Mixer-TTS paper which describes Mixer-TTS-X: https://arxiv.org/pdf/2110.03584.pdf

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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The model is based on the MLP-Mixer architecture adapted for speech synthesis. It contains pitch and duration predictors, with the latter being trained with an unsupervised TTS alignment framework, and additionally uses token embeddings from a pre-trained language model ALBERT.\n\n## Model Architecture\n\nFor more information about the model architecture, see the Mixer-TTS [paper](https://arxiv.org/pdf/2110.03584.pdf) which describes Mixer-TTS-X.\n\n## Training\n### Dataset\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\nNo performance information available at this time.\n\n## How to Use this Model\n\nThis model can be automatically loaded from NGC.\n\nNOTE: In order to generate audio, you also need a 22050Hz vocoder from NeMo. This example uses the HiFi-GAN model which was additionally fine-tuned on Mixer-TTS-X outputs.\n\n```python\n# Load Mixer-TTS-X\nfrom nemo.collections.tts.models import MixerTTSModel\nspec_generator = MixerTTSModel.from_pretrained(\"tts_en_lj_mixerttsx\")\n\n# Load vocoder\nfrom nemo.collections.tts.models import Vocoder\nmodel = Vocoder.from_pretrained(model_name=\"tts_en_lj_hifigan_ft_mixerttsx\")\n\n# Generate audio\nimport soundfile as sf\nraw_text = \"You can type your sentence here to get nemo to produce speech.\"\nparsed = spec_generator.parse(raw_text)\nspectrogram = spec_generator.generate_spectrogram(tokens=parsed, raw_texts=[raw_text])\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\n\n1.6.0: The original version of Mixer-TTS-X model which was released with NeMo 1.6.0.\n\n## References\nMixer-TTS paper which describes Mixer-TTS-X: [https://arxiv.org/pdf/2110.03584.pdf](https://arxiv.org/pdf/2110.03584.pdf)\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).","displayName","TTS En LJ Mixer-TTS-X",["D",1680637002917],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/tts_en_lj_mixerttsx","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[240,241,242,243,244,245,246],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_209":265,"_248":265,"_249":266,"_251":252},{"_209":263,"_248":263,"_249":264,"_251":252},{"_209":261,"_248":261,"_249":262,"_251":252},{"_209":258,"_248":258,"_249":259,"_251":252},{"_209":256,"_248":256,"_249":257,"_251":252},{"_209":254,"_248":254,"_249":255,"_251":252},{"_209":247,"_248":247,"_249":250,"_251":252},"TTS","value","filter",{"_253":252,"_248":247},"category","label","field","SpectrogramGenerator",{"_253":252,"_248":254},"PytorchLightning",{"_253":252,"_248":256},"NeMo",{"_253":260,"_248":258},"framework","Mixer-TTS-X",{"_253":252,"_248":261},"LJSpeech",{"_253":252,"_248":263},"En",{"_253":252,"_248":265},{"_118":1440,"_120":1441},"ngcApps",[1393],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1374":1375,"_1376":1377,"_1378":1379,"_1380":1381,"_1382":278,"_149":302,"_1383":1384,"_1385":59,"_1386":98,"_1387":98,"_1388":1389,"_1390":98,"_1391":1392},"amplitude",{"_1371":1372,"_974":-7,"_1373":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1365":1366,"_1367":1368,"_1369":1370},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1249":181,"_1250":1251,"_1252":1253,"_1254":1255},"announcements",[],"consentState",{"_339":1239,"_1240":98,"_1241":34,"_1242":34,"_1243":1244},"consentConfig",{"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340},"showContactAdmin",1783953902773,[],[],[316,317,318,319,320],{"_48":62,"_50":73,"_52":68,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":68,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":68,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":57,"_58":59},{"_48":49,"_50":51,"_52":68,"_54":-5,"_56":-5,"_58":59},"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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