Model Overview

This collection contains Self-Supervised Learning (SSL) checkpoints for xlarge size versions of Conformer model (around 0.6B parameters). Models are trained using unlabeled english audio with contrastive loss. These are similar to w2v-Conformer XL [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 checkpoints are available in NeMo toolkit [2], 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_xlarge')\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

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	2.53	4.23	2.51	4.35
1.10.0	Contrastive	LS 960h	Conformer-Transducer	128	1.56	3.18	1.67	3.21

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 0.6B parameters). Models are trained using unlabeled english audio with contrastive loss. These are similar to w2v-Conformer XL [3,4] and can be fine-tuned for Automatic Speech Recognition (ASR).\n\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 checkpoints are available in NeMo toolkit [2], 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_xlarge')\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\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 | 2.53 | 4.23 | 2.51 | 4.35 |\n| 1.10.0 | Contrastive | LS 960h | Conformer-Transducer | 128 | 1.56 | 3.18 | 1.67 | 3.21 |\n\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## 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 XLarge",["D",1680635738757],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/ssl_en_conformer_xlarge","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239,240,241,242,243],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_207":259,"_245":260,"_246":261,"_248":249},{"_207":255,"_245":256,"_246":257,"_248":249},{"_207":253,"_245":253,"_246":254,"_248":249},{"_207":251,"_245":251,"_246":252,"_248":249},{"_207":244,"_245":244,"_246":247,"_248":249},"wav2vec","value","filter",{"_250":249,"_245":244},"category","label","field","w2v",{"_250":249,"_245":251},"SSL",{"_250":249,"_245":253},"DL","soln_dl",{"_250":258,"_245":256},"technology","Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_250":262,"_245":260},"application",{"_108":1437,"_110":1438},"ngcApps",[1390],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1371":1372,"_1373":1374,"_1375":1376,"_1377":1378,"_1379":274,"_139":298,"_1380":1381,"_1382":56,"_1383":87,"_1384":87,"_1385":1386,"_1387":87,"_1388":1389},"amplitude",{"_1368":1369,"_970":-7,"_1370":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1362":1363,"_1364":1365,"_1366":1367},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1245":169,"_1246":1247,"_1248":1249,"_1250":1251},"announcements",[],"consentState",{"_335":1235,"_1236":87,"_1237":31,"_1238":31,"_1239":1240},"consentConfig",{"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336},"showContactAdmin",1784055795755,[],[],[312,313,314,315,316],{"_45":60,"_47":71,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":59,"_55":56},{"_45":46,"_47":48,"_49":103,"_51":-5,"_53":-5,"_55":56},"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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