Overview

NVIDIA NeMo toolkit supports multiple Automatic Speech Recognition(ASR) models such as Jasper and QuartzNet. Pretrained checkpoints for these models trained on standard datasets can be used immediately, use speech_to_text.py script in the examples directory.\nIn addition, models for ASR sub-tasks such as speech classification are also provided; for example MatchboxNet trained on the Google Speech Commands Dataset is available in speech_to_label.py. Voice Activity Detection is also supported with the same script, by simply changing the config file passed to the script!\nNeMo also supports training Speech Recognition models with Byte Pair/Word Piece encoding of the corpus, via the speech_to_text_bpe.py example; these models are still under development.\nIn order to simply perform evaluation on a dataset using these models, use the speech_to_text_infer.py example, which shows how to compute WER over the dataset.

Usage

You can instantiate all these models automatically directly from NGC. To do so, start your script with:

import nemo\nimport nemo.collections.asr as nemo_asr\n

Then chose what type of model you would like to instantiate. See table below for the list of model base classes. Then use base_class.from_pretrained(...) method.\nFor example:

quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name=\"QuartzNet15x5Base-En\")\n

Note that you can also list all available models using API by calling base_class.list_available_models(...) method.

You can also download all models' \".nemo\" files in the \"File Browser\" tab and then instantiate those models with base_class.restore_from(PATH_TO_DOTNEMO_FILE) method. In this case, make sure you are matching NeMo and models' versions.

Here is a list of currently available models together with their base classes and short descriptions.

\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\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\">

Model name	Model Base Class	Description
QuartzNet15x5Base-En	`EncDecCTCModel`	QuartzNet15x5 model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1 for 600 epochs. The model achieves a WER of 3.79% on LibriSpeech dev-clean, and a WER of 10.05% on dev-other.
QuartzNet15x5Base-Zh	`EncDecCTCModel`	QuartzNet15x5 model trained on ai-shell2 Mandarin Chinese dataset.
QuartzNet5x5LS-En	`EncDecCTCModel`	QuartzNet5x5 model trained on LibriSpeech dataset only. The model achieves a WER of 5.37% on LibriSpeech dev-clean, and a WER of 15.69% on dev-other.
QuartzNet15x5NR-En	`EncDecCTCModel`	Quartznet15x5 model trained for presence of noise. The base model QuartzNet15x5Base-En was finetuned with RIR and noise augmentation to make it more robust to noise. This model should be preferred for noisy speech transcription. This model achieves a WER of 3.96% on LibriSpeech dev-clean and a WER of 10.14% on dev-other.
Jasper10x5Dr-En	`EncDecCTCModel`	JasperNet10x5Dr model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1. The model achieves a WER of 3.37% on LibriSpeech dev-clean, 9.81% on dev-other.
ContextNet-192-WPE-1024-8x-Stride	`EncDecCTCModelBPE`	ContextNet initial implementation model trained on the Librispeech corpus and achieves a WER of 10.09% on test-other and 10.11% on dev-other.
MatchboxNet-3x1x64-v1	`EncDecClassificationModel`	MatchboxNet model trained on Google Speech Commands dataset (v1, 30 classes) which obtains 97.32% accuracy on test set.
MatchboxNet-3x2x64-v1	`EncDecClassificationModel`	MatchboxNet model trained on Google Speech Commands dataset (v1, 30 classes) which obtains 97.68% accuracy on test set.
MatchboxNet-3x1x64-v2	`EncDecClassificationModel`	MatchboxNet model trained on Google Speech Commands dataset (v2, 35 classes) which obtains 97.12% accuracy on test set.
MatchboxNet-3x1x64-v2	`EncDecClassificationModel`	MatchboxNet model trained on Google Speech Commands dataset (v2, 30 classes) which obtains 97.29% accuracy on test set.
MatchboxNet-3x1x64-v2-subset-task	`EncDecClassificationModel`	MatchboxNet model trained on Google Speech Commands dataset (v2, 10+2 classes) which obtains 98.2% accuracy on test set.
MatchboxNet-3x2x64-v2-subset-task	`EncDecClassificationModel`	MatchboxNet model trained on Google Speech Commands dataset (v2, 10+2 classes) which obtains 98.4% accuracy on test set.
MatchboxNet-VAD-3x2	`EncDecClassificationModel`	Voice Activity Detection MatchboxNet model trained on google speech command (v2) and freesound background data, which obtains 0.992 accuracy on testset from same source and 0.852 TPR for FPR=0.315 on testset (ALL) of AVA movie data
SpeakerNet_recognition	`EncDecSpeakerLabelModel`	SpeakerNet_recognition model trained end-to-end for speaker recognition purposes with cross_entropy loss. It was trained on voxceleb 1, voxceleb 2 dev datasets and augmented with musan music and noise. Speaker Recognition model achieves 2.65% EER on voxceleb-O cleaned trial file\"
SpeakerNet_verification	`EncDecSpeakerLabelModel`	SpeakerNet_verification model trained end-to-end for speaker verification purposes with arcface angular softmax loss. It was trained on voxceleb 1, voxceleb 2 dev datasets and augmented with musan music and noise. Speaker Verification model achieves 2.12% EER on voxceleb-O cleaned trial file

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These models are used for Automatic Speech Recognition (ASR) and sub-tasks.","## Overview\n[NVIDIA NeMo](https://github.com/NVIDIA/NeMo) toolkit supports multiple Automatic Speech Recognition(ASR) models such as Jasper and QuartzNet. Pretrained checkpoints for these models trained on standard datasets can be used immediately, use speech_to_text.py script in the examples directory.\nIn addition, models for ASR sub-tasks such as speech classification are also provided; for example MatchboxNet trained on the Google Speech Commands Dataset is available in speech_to_label.py. Voice Activity Detection is also supported with the same script, by simply changing the config file passed to the script!\nNeMo also supports training Speech Recognition models with Byte Pair/Word Piece encoding of the corpus, via the speech_to_text_bpe.py example; these models are still under development.\nIn order to simply perform evaluation on a dataset using these models, use the speech_to_text_infer.py example, which shows how to compute WER over the dataset.\n\n## Usage\n\nYou can instantiate all these models automatically directly from NGC. To do so, start your script with:\n```\nimport nemo\nimport nemo.collections.asr as nemo_asr\n```\nThen chose what type of model you would like to instantiate. See table below for the list of model base classes. Then use ```base_class.from_pretrained(...)``` method.\nFor example:\n```\nquartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name=\"QuartzNet15x5Base-En\")\n```\n\nNote that you can also list all available models using API by calling ```base_class.list_available_models(...)``` method.\n\nYou can also download all models' \".nemo\" files in the \"File Browser\" tab and then instantiate those models with ```base_class.restore_from(PATH_TO_DOTNEMO_FILE)``` method. In this case, make sure you are matching NeMo and models' versions.\n\n\nHere is a list of currently available models together with their base classes and short descriptions.\n\n| Model name | Model Base Class | Description |\n|-----------------------------------|---------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| QuartzNet15x5Base-En | ```EncDecCTCModel``` | QuartzNet15x5 model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1 for 600 epochs. The model achieves a WER of 3.79% on LibriSpeech dev-clean, and a WER of 10.05% on dev-other. |\n| QuartzNet15x5Base-Zh | ```EncDecCTCModel``` | QuartzNet15x5 model trained on ai-shell2 Mandarin Chinese dataset. |\n| QuartzNet5x5LS-En | ```EncDecCTCModel``` | QuartzNet5x5 model trained on LibriSpeech dataset only. The model achieves a WER of 5.37% on LibriSpeech dev-clean, and a WER of 15.69% on dev-other. |\n| QuartzNet15x5NR-En | ```EncDecCTCModel``` | Quartznet15x5 model trained for presence of noise. The base model QuartzNet15x5Base-En was finetuned with RIR and noise augmentation to make it more robust to noise. This model should be preferred for noisy speech transcription. This model achieves a WER of 3.96% on LibriSpeech dev-clean and a WER of 10.14% on dev-other. |\n| Jasper10x5Dr-En | ```EncDecCTCModel``` | JasperNet10x5Dr model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1. The model achieves a WER of 3.37% on LibriSpeech dev-clean, 9.81% on dev-other. |\n| ContextNet-192-WPE-1024-8x-Stride | ```EncDecCTCModelBPE``` | ContextNet initial implementation model trained on the Librispeech corpus and achieves a WER of 10.09% on test-other and 10.11% on dev-other. |\n| MatchboxNet-3x1x64-v1 | ```EncDecClassificationModel``` | MatchboxNet model trained on Google Speech Commands dataset (v1, 30 classes) which obtains 97.32% accuracy on test set. |\n| MatchboxNet-3x2x64-v1 | ```EncDecClassificationModel``` | MatchboxNet model trained on Google Speech Commands dataset (v1, 30 classes) which obtains 97.68% accuracy on test set. |\n| MatchboxNet-3x1x64-v2 | ```EncDecClassificationModel``` | MatchboxNet model trained on Google Speech Commands dataset (v2, 35 classes) which obtains 97.12% accuracy on test set. |\n| MatchboxNet-3x1x64-v2 | ```EncDecClassificationModel``` | MatchboxNet model trained on Google Speech Commands dataset (v2, 30 classes) which obtains 97.29% accuracy on test set. |\n| MatchboxNet-3x1x64-v2-subset-task | ```EncDecClassificationModel``` | MatchboxNet model trained on Google Speech Commands dataset (v2, 10+2 classes) which obtains 98.2% accuracy on test set. |\n| MatchboxNet-3x2x64-v2-subset-task | ```EncDecClassificationModel``` | MatchboxNet model trained on Google Speech Commands dataset (v2, 10+2 classes) which obtains 98.4% accuracy on test set. |\n| MatchboxNet-VAD-3x2 | ```EncDecClassificationModel``` | Voice Activity Detection MatchboxNet model trained on google speech command (v2) and freesound background data, which obtains 0.992 accuracy on testset from same source and 0.852 TPR for FPR=0.315 on testset (ALL) of AVA movie data |\n| SpeakerNet_recognition | ```EncDecSpeakerLabelModel``` | SpeakerNet_recognition model trained end-to-end for speaker recognition purposes with cross_entropy loss. It was trained on voxceleb 1, voxceleb 2 dev datasets and augmented with musan music and noise. Speaker Recognition model achieves 2.65% EER on voxceleb-O cleaned trial file\" |\n| SpeakerNet_verification | ```EncDecSpeakerLabelModel``` | SpeakerNet_verification model trained end-to-end for speaker verification purposes with arcface angular softmax loss. It was trained on voxceleb 1, voxceleb 2 dev datasets and augmented with musan music and noise. Speaker Verification model achieves 2.12% EER on voxceleb-O cleaned trial file |","displayName","NeMo Speech Models",["D",1680638126364],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/models/nemospeechmodels","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[241,242,243],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_210":255,"_245":255,"_246":256,"_248":249},{"_210":251,"_245":252,"_246":253,"_248":249},{"_210":244,"_245":244,"_246":247,"_248":249},"Speech","value","filter",{"_250":249,"_245":244},"category","label","field","Conversational AI","soln_conversational_ai",{"_250":254,"_245":252},"technology","ASR",{"_250":249,"_245":255},{"_121":1430,"_123":1431},"ngcApps",[1383],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1364":1365,"_1366":1367,"_1368":1369,"_1370":1371,"_1372":268,"_152":292,"_1373":1374,"_1375":59,"_1376":100,"_1377":100,"_1378":1379,"_1380":100,"_1381":1382},"amplitude",{"_1361":1362,"_964":-7,"_1363":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1355":1356,"_1357":1358,"_1359":1360},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1239":181,"_1240":1241,"_1242":1243,"_1244":1245},"announcements",[],"consentState",{"_331":1229,"_1230":34,"_1231":34,"_1232":100,"_1233":1234},"consentConfig",{"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332},"showContactAdmin",1784171999485,[],[],[306,307,308,309,310,311],{"_48":63,"_50":74,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":68,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":312,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","ApplicationGatewayAffinityCORS","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure all requests from a user during the session are sent to the same server.","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.Supports Amplitude product analytics.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","AMP_TLDTEST","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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