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Model Overview

This collection contains ContextNet-256 (around 14M parameters) trained on NeMo ASRSet with over 11,500 hours of english speech.

It utilizes a Google SentencePiece [1] tokenizer with vocabulary size 1024, and transcribes text in lower case english alphabet along with spaces, apostrophes and a few other characters.

Model Architecture

ContextNet [2] model is an autoregressive, transducer based Automatic Speech Recognition model. You may find more info on the detail of this model here: ContextNet Model.

Training

The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this example script and this base config.

The tokenizers for these models were built using the text transcripts of the train set with this script.

Datasets

All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several thousand hours of English speech:

Librispeech 960 hours of English speech
Fisher Corpus
Switchboard-1 Dataset
WSJ-0 and WSJ-1
National Speech Corpus (Part 1, Part 6)
VCTK
VoxPopuli (EN)
Europarl-ASR (EN)
Multilingual Librispeech (MLS EN) - 2,000 hours subset
Mozilla Common Voice (v7.0)

Tokenizer Construction

The tokenizer for this model was built using text corpus provided with the train dataset.

We build a Google Sentencepiece Tokenizer [1] with the following script :

" \\\n --vocab_size=1024 \\\n --tokenizer="spe" \\\n --spe_type="unigram" \\\n --spe_character_coverage=1.0 \\\n --no_lower_case \\\n --log\n\">

python [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \\\n  --manifest=\"train_manifest.json\" \\\n  --data_root=\"<OUTPUT DIRECTORY FOR TOKENIZER>\" \\\n  --vocab_size=1024 \\\n  --tokenizer=\"spe\" \\\n  --spe_type=\"unigram\" \\\n  --spe_character_coverage=1.0 \\\n  --no_lower_case \\\n  --log\n

Performance

The performance of Automatic Speech Recognition models is measuring using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.

The model obtains the following scores on the following evaluation datasets -

3.3 % on Librispeech dev-clean
7.9 % on Librispeech dev-other
1.8 % on Librispeech test-clean
8.0 % on Librispeech test-other
9.7 % on Multilingual Librispeech dev (EN)
11.0 % on Multilingual Librispeech test (EN)
3.2 % on WSJ Eval 92
4.6 % on WSJ Dev 93
7.1 % on NSC Part 1

Note that these scores on Librispeech are not particularly indicative of the quality of transcriptions that models trained on ASR Set will achieve, but they are a useful proxy.

How to Use this Model

The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically load the model from NGC

import nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name=\"stt_en_contextnet_256\")\n

Transcribing text with this model

"\n\">

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n  pretrained_name=\"stt_en_contextnet_256\" \\\n  audio_dir=\"<DIRECTORY CONTAINING AUDIO FILES>\"\n

Input

This model accepts 16000 KHz Mono-channel Audio (wav files) as input.

Output

This model provides transcribed speech as a string for a given audio sample.

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] Google Sentencepiece Tokenizer

[2] ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

[3] NVIDIA NeMo Toolkit

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.

","resourceVersions",[190,191,192],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_164":21,"_165":166,"_48":167,"_168":-5,"_169":170,"_171":172,"_173":80,"_174":16,"_175":30,"_176":-5,"_177":30,"_178":-5,"_179":30,"_180":-5,"_181":30,"_182":-5,"_183":30,"_184":-5,"_185":30,"_186":-5,"_187":30,"_188":189},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/nemo/models/stt_en_contextnet_256/-","version","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/nemo/stt_en_contextnet_256:1.4.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/nemo/models/stt_en_contextnet_256/1.4.0/version-history","accessCheckResult",{"_163":86},"hasRelatedCollections","context",{"_159":160,"_161":162},[],[],[153,154,155,156,157,158],{"_44":59,"_46":70,"_48":61,"_50":62,"_52":71,"_54":55},{"_44":59,"_46":68,"_48":61,"_50":62,"_52":69,"_54":55},{"_44":59,"_46":66,"_48":61,"_50":62,"_52":67,"_54":55},{"_44":59,"_46":60,"_48":61,"_50":62,"_52":63,"_54":55},{"_44":56,"_46":57,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},"orgName","nvidia","teamName","nemo","allowed","versionId","description","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1632262756718],"updatedDate",["D",1740797857914],"totalSizeInBytes","totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_76":196,"_78":197,"_168":-5,"_175":30,"_177":30,"_188":195,"_179":30},{"_76":194,"_78":194,"_168":-5,"_175":30,"_177":30,"_188":195,"_179":30},{"_76":21,"_78":21,"_168":-5,"_175":30,"_177":30,"_188":193,"_179":30},[],"1.6.0",[],"-","Latest (1.6.0)","nvidia/nemo/stt_en_contextnet_256","key","nvidia/nemo/models/stt_en_contextnet_256","resourceType","models","resourceId","stt_en_contextnet_256","shortDescription","ContextNet-256 model for English Automatic Speech Recognition, trained on NeMo ASRSET.","## Model Overview\n\nThis collection contains ContextNet-256 (around 14M parameters) trained on NeMo ASRSet with over 11,500 hours of english speech. \n\nIt utilizes a Google SentencePiece [1] tokenizer with vocabulary size 1024, and transcribes text in lower case english alphabet along with spaces, apostrophes and a few other characters.\n\n## Model Architecture\n\nContextNet [2] model is an autoregressive, transducer based Automatic Speech Recognition model. You may find more info on the detail of this model here: [ContextNet Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html).\n\n\n## Training\n\nThe NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/contextnet/contextnet_rnnt.yaml).\n\nThe tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).\n\n### Datasets\n\nAll the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several thousand hours of English speech:\n\n- Librispeech 960 hours of English speech\n- Fisher Corpus\n- Switchboard-1 Dataset\n- WSJ-0 and WSJ-1\n- National Speech Corpus (Part 1, Part 6)\n- VCTK\n- VoxPopuli (EN)\n- Europarl-ASR (EN)\n- Multilingual Librispeech (MLS EN) - 2,000 hours subset\n- Mozilla Common Voice (v7.0)\n\n### Tokenizer Construction\n\nThe tokenizer for this model was built using text corpus provided with the train dataset.\n\nWe build a Google Sentencepiece Tokenizer [1] with the following script :\n\n```bash\npython [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \\\n --manifest=\"train_manifest.json\" \\\n --data_root=\"\" \\\n --vocab_size=1024 \\\n --tokenizer=\"spe\" \\\n --spe_type=\"unigram\" \\\n --spe_character_coverage=1.0 \\\n --no_lower_case \\\n --log\n```\n\n## Performance\n\nThe performance of Automatic Speech Recognition models is measuring using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.\n\nThe model obtains the following scores on the following evaluation datasets -\n\n- 3.3 % on `Librispeech dev-clean`\n- 7.9 % on `Librispeech dev-other`\n- 1.8 % on `Librispeech test-clean`\n- 8.0 % on `Librispeech test-other`\n- 9.7 % on `Multilingual Librispeech dev (EN)`\n- 11.0 % on `Multilingual Librispeech test (EN)`\n- 3.2 % on `WSJ Eval 92`\n- 4.6 % on `WSJ Dev 93`\n- 7.1 % on `NSC Part 1`\n\nNote that these scores on Librispeech are not particularly indicative of the quality of transcriptions that models trained on ASR Set will achieve, but they are a useful proxy.\n\n## How to Use this Model\n\nThe model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.\n\n### Automatically load the model from NGC\n\n```python\nimport nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name=\"stt_en_contextnet_256\")\n```\n\n### Transcribing text with this model\n\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_en_contextnet_256\" \\\n audio_dir=\"\"\n```\n\n### Input\n\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.\n\n### Output\n\nThis model provides transcribed speech as a string for a given audio sample.\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\n## References\n\n[1] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)\n\n[2] [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://arxiv.org/abs/2005.03191)\n\n[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\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","STT En ContextNet 256",["D",1680635682638],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/stt_en_contextnet_256","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes",52912604,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[241,242,243],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_208":255,"_245":256,"_247":257,"_249":250},{"_208":253,"_245":253,"_247":254,"_249":250},{"_208":244,"_245":246,"_247":248,"_249":250},"Conversational AI","value","soln_conversational_ai","filter",{"_251":252,"_245":246},"category","label","field","technology","ContextNet",{"_251":250,"_245":253},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_251":258,"_245":256},"application",{"_107":1434,"_109":1435},"ngcApps",[1387],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1368":1369,"_1370":1371,"_1372":1373,"_1374":1375,"_1376":270,"_138":294,"_1377":1378,"_1379":55,"_1380":86,"_1381":86,"_1382":1383,"_1384":86,"_1385":1386},"amplitude",{"_1365":1366,"_967":-7,"_1367":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1359":1360,"_1361":1362,"_1363":1364},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1242":166,"_1243":1244,"_1245":1246,"_1247":1248},"announcements",[],"consentState",{"_333":1232,"_1233":86,"_1234":30,"_1235":30,"_1236":1237},"consentConfig",{"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334},"showContactAdmin",1784308410471,[],[],[308,309,310,311,312,313],{"_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":49,"_50":51,"_52":314,"_54":55},4,"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 uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[422,423,424,425],{"_78":430,"_379":420,"_380":30,"_165":427},{"_78":429,"_379":420,"_380":30,"_165":427},{"_78":428,"_379":420,"_380":30,"_165":427},{"_78":426,"_379":420,"_380":30,"_165":427},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[433],{"_78":434,"_379":431,"_380":30,"_165":435},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[438,439,440,441],{"_78":447,"_379":436,"_380":30,"_165":443},{"_78":445,"_379":436,"_380":30,"_165":446},{"_78":444,"_379":436,"_380":30,"_165":443},{"_78":442,"_379":436,"_380":30,"_165":443},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. 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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.",[605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625],[570,571,572,573,574],{"_373":465,"_208":465,"_165":166,"_347":596},{"_373":431,"_208":431,"_165":166,"_347":591},{"_373":584,"_208":584,"_165":166,"_347":585},{"_373":580,"_208":580,"_165":166,"_347":581},{"_373":575,"_208":575,"_165":166,"_347":576},"nvcrm.my.site.com",[577],{"_78":578,"_379":575,"_380":30,"_165":579},"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",[582],{"_78":583,"_379":580,"_380":30,"_165":414},"edge","github.com",[586,587],{"_78":590,"_379":584,"_380":30,"_165":589},{"_78":588,"_379":584,"_380":30,"_165":589},"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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