Model Overview

The RAD-TTS Aligner is a model that aligns speech and text inputs. It generates both a soft and hard alignment, the latter of which can be used to calculate token durations in mel frames.

Model Architecture

The RAD-TTS Aligner is non-autoregressive, and uses 1D convolution layers to separately encode text and mel spectrogram inputs. The model calculates the pairwise L2 distances between tokens and mel frames, then uses the Viterbi algorithm to determine a hard alignment between the two. During inference, the model produces a soft alignment (L2 distance matrix) as well as the hard alignment of text tokens to audio frames.

For more details, please refer to the paper.

Training

During training, forward sum loss is used with bin loss to make up an unsupervised alignment learning objective.

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.

)\naudio = torch.tensor(audio_data, dtype=torch.float, device=device).unsqueeze(0)\naudio_len = torch.tensor(audio_len).unsqueeze(0)\nspec, spec_len = aligner.preprocessor(input_signal=audio, length=audio_len)\n\n# Process text\ntext_raw = ""\ntext_normalized = aligner.normalizer.normalize(text_raw, punct_post_process=True)\ntext_tokens = aligner.tokenizer(text_normalized)\ntext = torch.tensor(text_tokens, device=device).unsqueeze(0).long()\ntext_len = torch.tensor(len(text_tokens), device=device).unsqueeze(0).long()\n\n# Run the Aligner\nattn_soft_tensor, attn_logprob_tensor = aligner(spec=spec, spec_len=spec_len, text=text, text_len=text_len)\n\">

# Load the RAD-TTS Aligner\nfrom nemo.collections.tts.models import AlignerModel\naligner = AlignerModel.from_pretrained(\"tts_en_radtts_aligner\") # or \"tts_en_radtts_aligner_ipa\" for the IPA version\n\n# Load audio and text\naudio_data, orig_sr = sf.read(<audio_path>)\naudio = torch.tensor(audio_data, dtype=torch.float, device=device).unsqueeze(0)\naudio_len = torch.tensor(audio_len).unsqueeze(0)\nspec, spec_len = aligner.preprocessor(input_signal=audio, length=audio_len)\n\n# Process text\ntext_raw = \"<your_text_here>\"\ntext_normalized = aligner.normalizer.normalize(text_raw, punct_post_process=True)\ntext_tokens = aligner.tokenizer(text_normalized)\ntext = torch.tensor(text_tokens, device=device).unsqueeze(0).long()\ntext_len = torch.tensor(len(text_tokens), device=device).unsqueeze(0).long()\n\n# Run the Aligner\nattn_soft_tensor, attn_logprob_tensor = aligner(spec=spec, spec_len=spec_len, text=text, text_len=text_len)\n

See the Aligner Inference Tutorial for more in-depth examples and use-cases.

The model can also be used to disambiguate heteronyms for G2P, as in this example script.

Input

This model accepts batches of text and their corresponding audio.

Output

This model predicts soft and hard alignments between the pairs of inputs.

Limitations

This checkpoint will work best at aligning samples spoken by the LJSpeech speaker, at 22050Hz. It will be less reliable for other speakers.

Versions

ARPABET_1.11.0: The original version released with NeMo 1.11.0.

IPA_1.13.0: An additional version that uses IPA rather than ARPABET.

References

RAD-TTS Aligner paper: https://arxiv.org/abs/2108.10447

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 generates both a soft and hard alignment, the latter of which can be used to calculate token durations in mel frames.\n\n## Model Architecture\n\nThe RAD-TTS Aligner is non-autoregressive, and uses 1D convolution layers to separately encode text and mel spectrogram inputs. The model calculates the pairwise L2 distances between tokens and mel frames, then uses the Viterbi algorithm to determine a hard alignment between the two. During inference, the model produces a soft alignment (L2 distance matrix) as well as the hard alignment of text tokens to audio frames.\n\nFor more details, please refer to [the paper](https://arxiv.org/abs/2108.10447).\n\n## Training\n\nDuring training, forward sum loss is used with bin loss to make up an unsupervised alignment learning objective.\n\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\n```python\n# Load the RAD-TTS Aligner\nfrom nemo.collections.tts.models import AlignerModel\naligner = AlignerModel.from_pretrained(\"tts_en_radtts_aligner\") # or \"tts_en_radtts_aligner_ipa\" for the IPA version\n\n# Load audio and text\naudio_data, orig_sr = sf.read()\naudio = torch.tensor(audio_data, dtype=torch.float, device=device).unsqueeze(0)\naudio_len = torch.tensor(audio_len).unsqueeze(0)\nspec, spec_len = aligner.preprocessor(input_signal=audio, length=audio_len)\n\n# Process text\ntext_raw = \"\"\ntext_normalized = aligner.normalizer.normalize(text_raw, punct_post_process=True)\ntext_tokens = aligner.tokenizer(text_normalized)\ntext = torch.tensor(text_tokens, device=device).unsqueeze(0).long()\ntext_len = torch.tensor(len(text_tokens), device=device).unsqueeze(0).long()\n\n# Run the Aligner\nattn_soft_tensor, attn_logprob_tensor = aligner(spec=spec, spec_len=spec_len, text=text, text_len=text_len)\n```\n\nSee the [Aligner Inference Tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/tts/Aligner_Inference_Examples.ipynb) for more in-depth examples and use-cases.\n\nThe model can also be used to disambiguate heteronyms for G2P, as in [this example script](https://github.com/NVIDIA/NeMo/blob/main/examples/tts/aligner_heteronym_disambiguation.py).\n\n### Input\nThis model accepts batches of text and their corresponding audio.\n\n### Output\nThis model predicts soft and hard alignments between the pairs of inputs.\n\n## Limitations\nThis checkpoint will work best at aligning samples spoken by the LJSpeech speaker, at 22050Hz. It will be less reliable for other speakers.\n\n## Versions\n\nARPABET_1.11.0: The original version released with NeMo 1.11.0.\n\nIPA_1.13.0: An additional version that uses IPA rather than ARPABET.\n\n## References\nRAD-TTS Aligner paper: [https://arxiv.org/abs/2108.10447](https://arxiv.org/abs/2108.10447)\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 En RAD-TTS Aligner",["D",1680638172491],"/api/image/nvidia/nemo/models/tts_en_radtts_aligner","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","bias","explainability","privacy","safetyAndSecurity",[],[288,289,290,291,292,293],[],[],[],{"_91":305,"_131":306,"_132":307,"_134":135},{"_91":303,"_131":303,"_132":304,"_134":135},{"_91":301,"_131":301,"_132":302,"_134":135},{"_91":298,"_131":298,"_132":299,"_134":135},{"_91":296,"_131":296,"_132":297,"_134":135},{"_91":294,"_131":294,"_132":295,"_134":135},"TTS",{"_136":135,"_131":294},"PytorchLightning",{"_136":135,"_131":296},"NeMo",{"_136":300,"_131":298},"framework","G2P",{"_136":135,"_131":301},"En",{"_136":135,"_131":303},"DL","soln_dl",{"_136":308,"_131":306},"technology",{"_178":1479,"_180":1480},"ngcApps",[1432],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1413":1414,"_1415":1416,"_1417":1418,"_1419":1420,"_1421":320,"_210":344,"_1422":1423,"_1424":51,"_1425":158,"_1426":158,"_1427":1428,"_1429":158,"_1430":1431},"amplitude",{"_1410":1411,"_1016":-7,"_1412":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1404":1405,"_1406":1407,"_1408":1409},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1291":424,"_1292":1293,"_1294":1295,"_1296":1297},"announcements",[],"consentState",{"_382":1281,"_1282":158,"_1283":26,"_1284":26,"_1285":1286},"consentConfig",{"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379,"_380":381,"_382":383},"showContactAdmin",1783991213732,[],[],[358,359,360,361,362],{"_40":55,"_42":66,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":363,"_50":51},5,"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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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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