Model Overview

This collection contains Tacotron2 Text to Speech Model for Marathi language with Female Voice trained on IndicTTS dataset. This model is a mel-spectrogram generator and can be used along with HifiGAN as the vocoder to produce speech.

Model Training Details

Tacotron2 is an encoder-attention-decoder. The encoder is made of three parts in sequence: 1) a word embedding, 2) a convolutional network, and 3) a bi-directional LSTM. The encoded represented is connected to the decoder via a Location Sensitive Attention module. The decoder is comprised of a 2 layer LSTM network, a convolutional postnet, and a fully connected prenet.

During training, the ground frame is fed through the prenet and passed as input to the decoder LSTM layers. During inference, the model's predictions at the previous time step is used. In addition, an attention context is computed by the attention layer at each step and concatenated with the prenet output. The output of the LSTM network concatenated with the attention is sent through two projection layers. The first projects the information to a spectrogram while the other projects it to a stop token. The spectrogram is then sent through the convolutional postnet to compute a residual to add to the generated spectrogram.

Trained or fine-tuned NeMo models (with the file extenstion .nemo) can be converted to Riva models and then deployed. Here is a pre-trained Tacotron2 Speech Synthesis Riva model. Note that the Tacotron2 model at that link is not contained in a .riva file. Rather, it is used directly in the Riva build phase as a .nemo file.

Dataset Details

The model is trained using the IndicTTS dataset which is provided by consortrium of the following institutes.\nIIIT Hyderabad\nIIT Kharagpur\nIISc, Bangalore\nCDAC, Mumbai\nCDAC, Thiruvananthapuram\nIIT, Guwahati\nCDAC, Kolkata\nSSNCE, Chennai\nDA-IICT, Gujarat\nIIT, Mandi\nPESIT, Bangalore

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 Tacotron2 and HifiGAN\nimport IPython.display as ipd\nfrom nemo.collections.tts.models import HifiGanModel\nfrom nemo.collections.tts.models import Tacotron2Model\n\nspec_generator = Tacotron2Model.from_pretrained(\"tts_mr_female_tacotron2\")\n\nvocoder = HifiGanModel.from_pretrained(model_name=\"tts_hifigan\")\n\n# Generate audio\nparsed = spec_generator.parse(\"तू कसा आहेस\")\nspectrogram = spec_generator.generate_spectrogram(tokens=parsed)\naudio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)\nipd.Audio(audio[0].cpu().detach().numpy(), rate=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.

References

Tacotron2 paper: https://arxiv.org/abs/1712.05884\nLocation Sensitive Attention paper: https://arxiv.org/abs/1506.07503

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 model is a mel-spectrogram generator and can be used along with HifiGAN as the vocoder to produce speech.\n\n## Model Training Details\n\nTacotron2 is an encoder-attention-decoder. The encoder is made of three parts in sequence: 1) a word embedding, 2) a convolutional network, and 3) a bi-directional LSTM. The encoded represented is connected to the decoder via a Location Sensitive Attention module. The decoder is comprised of a 2 layer LSTM network, a convolutional postnet, and a fully connected prenet.\n\nDuring training, the ground frame is fed through the prenet and passed as input to the decoder LSTM layers. During inference, the model's predictions at the previous time step is used. In addition, an attention context is computed by the attention layer at each step and concatenated with the prenet output. The output of the LSTM network concatenated with the attention is sent through two projection layers. The first projects the information to a spectrogram while the other projects it to a stop token. The spectrogram is then sent through the convolutional postnet to compute a residual to add to the generated spectrogram.\n\nTrained or fine-tuned NeMo models (with the file extenstion .nemo) can be converted to Riva models and then deployed. Here is a pre-trained Tacotron2 Speech Synthesis Riva model. Note that the Tacotron2 model at that link is not contained in a .riva file. Rather, it is used directly in the Riva build phase as a .nemo file.\n\n## Dataset Details\nThe model is trained using the IndicTTS dataset which is provided by consortrium of the following institutes.\nIIIT Hyderabad\nIIT Kharagpur\nIISc, Bangalore\nCDAC, Mumbai\nCDAC, Thiruvananthapuram\nIIT, Guwahati\nCDAC, Kolkata\nSSNCE, Chennai\nDA-IICT, Gujarat\nIIT, Mandi\nPESIT, Bangalore\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.\n\n```python\n# Load Tacotron2 and HifiGAN\nimport IPython.display as ipd\nfrom nemo.collections.tts.models import HifiGanModel\nfrom nemo.collections.tts.models import Tacotron2Model\n\nspec_generator = Tacotron2Model.from_pretrained(\"tts_mr_female_tacotron2\")\n\nvocoder = HifiGanModel.from_pretrained(model_name=\"tts_hifigan\")\n\n# Generate audio\nparsed = spec_generator.parse(\"तू कसा आहेस\")\nspectrogram = spec_generator.generate_spectrogram(tokens=parsed)\naudio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)\nipd.Audio(audio[0].cpu().detach().numpy(), rate=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\n## References\nTacotron2 paper: https://arxiv.org/abs/1712.05884\nLocation Sensitive Attention paper: https://arxiv.org/abs/1506.07503\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 Mr Female Tacotron2",["D",1680635377153],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/tts_mr_female_tacotron2","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],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_209":260,"_246":261,"_247":262,"_249":250},{"_209":256,"_246":257,"_247":258,"_249":250},{"_209":254,"_246":254,"_247":255,"_249":250},{"_209":252,"_246":252,"_247":253,"_249":250},{"_209":245,"_246":245,"_247":248,"_249":250},"TTS","value","filter",{"_251":250,"_246":245},"category","label","field","Tacotron2",{"_251":250,"_246":252},"Mr",{"_251":250,"_246":254},"DL","soln_dl",{"_251":259,"_246":257},"technology","AI","soln_ai",{"_251":259,"_246":261},{"_120":1241,"_122":1242},"ngcApps",[1194],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1175":1176,"_1177":1178,"_1179":1180,"_1181":1182,"_1183":274,"_151":300,"_1184":1185,"_1186":59,"_1187":99,"_1188":99,"_1189":1190,"_1191":99,"_1192":1193},"amplitude",{"_1172":1173,"_814":-7,"_1174":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1166":1167,"_1168":1169,"_1170":1171},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787837846095,"1.12.0","defaultSearchSuggestions",{"_1054":381,"_1055":1056,"_1057":1058,"_1059":1060},"announcements",[],"consentState",{"_338":1044,"_1045":99,"_1046":34,"_1047":34,"_1048":1049},"consentConfig",{"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339},"showContactAdmin",1787837846096,[],[],[314,315,316,317,318,319],{"_48":63,"_50":74,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":68,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":62,"_58":59},{"_48":49,"_50":51,"_52":115,"_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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