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Model Overview

This collection contains Self-Supervised Learning (SSL) checkpoints for xlarge size versions of Conformer model (around 120M parameters). Models are trained using unlabeled english audio with contrastive loss. These are similar to w2v-Conformer [3,4] and can be fine-tuned for Automatic Speech Recognition (ASR).

Model Architecture

For details about conformer architecture, refer to [1].

Training

The NeMo toolkit [2] was used for training the models. These model are trained with this example script and this base config.

Datasets

All the models in this collection are trained using LibriLight corpus (~56k hrs of unlabeled English speech).

How to Use this Model

The pre-trained checkpoint is available in NeMo toolkit [3], and has to be fine-tuned on another labeled dataset for ASR.

To load the checkpoint from NGC

import nemo.collections.asr as nemo_asr\nssl_model = nemo_asr.models.ssl_models.SpeechEncDecSelfSupervisedModel.from_pretrained(model_name='ssl_en_conformer_large')\n

To continue ssl training on your own dataset, set init_from_pretrained_model and optim in config appropriately and use the script.

Fine-tune

To fine-tune using a labeled dataset, refer to this example script for transducer loss and to this example script for using CTC loss.

Briefly, you can load the pre-trained checkpoint into fine-tune model as shown below

# define fine-tune model\nasr_model = nemo_asr.models.EncDecRNNTBPEModel(cfg=cfg.model, trainer=trainer)\n\n# load ssl checkpoint\nasr_model.load_state_dict(ssl_model.state_dict(), strict=False)\n\ndel ssl_model\n

Performance

The list of the available models in this collection is shown in the following table. Performances of the ASR models fine-tuned from these check-points are reported in terms of Word Error Rate (WER%) with greedy decoding.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Version	SSL Loss	Fine-tune Dataset	Fine-tune Model	Vocabulary Size	LS dev-clean	LS dev-other	LS test-clean	LS test-other
1.10.0	Contrastive	LS 100h	Conformer-Transducer	128	3.25	6.64	3.19	6.39
1.10.0	Contrastive	LS 960h	Conformer-Transducer	128	1.92	4.38	2.02	4.50
1.10.1	Contrastive+MLM	LS 100h	Conformer-Transducer	128	3.04	6.08	3.17	6.17
1.10.1	Contrastive+MLM	LS 960h	Conformer-Transducer	128	1.91	4.33	1.96	4.20

Limitations

Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.

References

[1] Conformer: Convolution-augmented Transformer for Speech Recognition

[2] NVIDIA NeMo Toolkit

[3] Pushing the Limits of SSL for ASR

[4] W2V-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-training

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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These are similar to w2v-Conformer model and can be fine-tuned for Automatic Speech Recognition (ASR).","## Model Overview\n\nThis collection contains Self-Supervised Learning (SSL) checkpoints for xlarge size versions of Conformer model (around 120M parameters). Models are trained using unlabeled english audio with contrastive loss. These are similar to w2v-Conformer [3,4] and can be fine-tuned for Automatic Speech Recognition (ASR).\n\n## Model Architecture\n\nFor details about conformer architecture, refer to [1].\n\n## Training\n\nThe NeMo toolkit [2] was used for training the models. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_pretraining/speech_pre_training.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/ssl/conformer/conformer_ssl.yaml).\n\n\n### Datasets\n\nAll the models in this collection are trained using LibriLight corpus (~56k hrs of unlabeled English speech).\n\n\n## How to Use this Model\n\nThe pre-trained checkpoint is available in NeMo toolkit [3], and has to be fine-tuned on another labeled dataset for ASR.\n\n### To load the checkpoint from NGC\n\n```python\nimport nemo.collections.asr as nemo_asr\nssl_model = nemo_asr.models.ssl_models.SpeechEncDecSelfSupervisedModel.from_pretrained(model_name='ssl_en_conformer_large')\n```\nTo continue ssl training on your own dataset, set `init_from_pretrained_model` and `optim` in [config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/ssl/conformer/conformer_ssl.yaml) appropriately and use the [script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_pretraining/speech_pre_training.py).\n\n### Fine-tune\nTo fine-tune using a labeled dataset, refer to this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) for transducer loss and to this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) for using CTC loss.\n\nBriefly, you can load the pre-trained checkpoint into fine-tune model as shown below\n\n```python\n# define fine-tune model\nasr_model = nemo_asr.models.EncDecRNNTBPEModel(cfg=cfg.model, trainer=trainer)\n\n# load ssl checkpoint\nasr_model.load_state_dict(ssl_model.state_dict(), strict=False)\n\ndel ssl_model\n```\n\n## Performance\n\nThe list of the available models in this collection is shown in the following table. Performances of the ASR models fine-tuned from these check-points are reported in terms of Word Error Rate (WER%) with greedy decoding.\n\n| Version | SSL Loss | Fine-tune Dataset | Fine-tune Model | Vocabulary Size | LS dev-clean | LS dev-other | LS test-clean | LS test-other |\n|---------|----------|-------------------|-----------------|-----------------|--------------|--------------|---------------|---------------|\n| 1.10.0 | Contrastive | LS 100h | Conformer-Transducer | 128 | 3.25 | 6.64 | 3.19 | 6.39 |\n| 1.10.0 | Contrastive | LS 960h | Conformer-Transducer | 128 | 1.92 | 4.38 | 2.02 | 4.50 |\n| 1.10.1 | Contrastive+MLM | LS 100h | Conformer-Transducer | 128 | 3.04 | 6.08 | 3.17 | 6.17 |\n| 1.10.1 | Contrastive+MLM | LS 960h | Conformer-Transducer | 128 | 1.91 | 4.33 | 1.96 | 4.20 |\n\n## Limitations\n\nSince this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.\n\n## References\n\n[1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)\n\n[2] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n[3] [Pushing the Limits of SSL for ASR](https://arxiv.org/pdf/2010.10504.pdf)\n\n[4] [W2V-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-training](https://arxiv.org/pdf/2108.06209.pdf) \n\n\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. 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","SSL En Conformer Large",["D",1680636633384],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/ssl_en_conformer_large","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[243,244,245,246,247],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_211":263,"_249":264,"_250":265,"_252":253},{"_211":259,"_249":260,"_250":261,"_252":253},{"_211":257,"_249":257,"_250":258,"_252":253},{"_211":255,"_249":255,"_250":256,"_252":253},{"_211":248,"_249":248,"_250":251,"_252":253},"wav2vec","value","filter",{"_254":253,"_249":248},"category","label","field","w2v",{"_254":253,"_249":255},"SSL",{"_254":253,"_249":257},"DL","soln_dl",{"_254":262,"_249":260},"technology","Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_254":266,"_249":264},"application",{"_107":1441,"_109":1442},"ngcApps",[1394],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1375":1376,"_1377":1378,"_1379":1380,"_1381":1382,"_1383":278,"_139":302,"_1384":1385,"_1386":55,"_1387":86,"_1388":86,"_1389":1390,"_1391":86,"_1392":1393},"amplitude",{"_1372":1373,"_974":-7,"_1374":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1366":1367,"_1368":1369,"_1370":1371},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1249":169,"_1250":1251,"_1252":1253,"_1254":1255},"announcements",[],"consentState",{"_340":1239,"_1240":86,"_1241":30,"_1242":30,"_1243":1244},"consentConfig",{"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341},"showContactAdmin",1784017785214,[],[],[316,317,318,319,320,321],{"_44":59,"_46":70,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":102,"_50":-5,"_52":-5,"_54":55},"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.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. 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. 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.",4,[613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633],[578,579,580,581,582],{"_380":472,"_211":472,"_168":169,"_354":604},{"_380":438,"_211":438,"_168":169,"_354":599},{"_380":592,"_211":592,"_168":169,"_354":593},{"_380":588,"_211":588,"_168":169,"_354":589},{"_380":583,"_211":583,"_168":169,"_354":584},"nvcrm.my.site.com",[585],{"_78":586,"_386":583,"_387":30,"_168":587},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[590],{"_78":591,"_386":588,"_387":30,"_168":421},"edge","github.com",[594,595],{"_78":598,"_386":592,"_387":30,"_168":597},{"_78":596,"_386":592,"_387":30,"_168":597},"logged_in","This domain is owned by Github. 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The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website 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