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Model Overview

Jasper model is an end-to-end neural speech recognition models trained with CTC loss, which has been trained on the ASR Set dataset with over 7000 hours of English speech.

Jasper models utilizes a character encoding. The pretrained models here can be used immediately for fine-tuning or dataset evaluation.

Trained or fine-tuned NeMo models (with the file extenstion .nemo) can be converted to Riva models (with the file extension .riva) and then deployed. Here is a pre-trained Jasper speech-to-text (STT) -- a.k.a. automatic speech recognition (ASR) -- Riva model.

Model Architecture

The Jasper model is composed of multiple blocks with residual connections between them, trained with CTC loss. Each block consists of one or more modules with convolutional layers, batch normalization, and ReLU layers.

The Jasper 10x5-DR model consists of a stack of five blocks that repeat ten times plus four additional convolutional layers [1].

Training

This Jasper model was trained on a combination of seven datasets of English speech, with a total of 7,057 hours of audio samples. Samples were limited to a minimum duration of 0.1s long, and a maximum duration of 16.7s long. The model was trained for 600 epochs with Apex/Amp optimization level O1. The NeMo toolkit [2] was used for training this model over several hundred epochs on multiple GPUs.

The model has been fine-tuned with Room Impulse Response (RIR) and noise augmentation to make it more robust to noise.

Datasets

The datasets included in training are detailed in the table below. The \"Duration\" column indicates how many hours of audio are contained in that dataset before length filtering was performed.

\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\">

Dataset	Speed Perturbed	Duration (h)
LibriSpeech	Y	2,903
Wall Street Journal	Y	245
Fisher English Training Speech	N	1,906
Switchboard	N	316
Mozilla Common Voice*	N	1,090
NSC Singapore English (Part 1)	N	1,857

Only non-dev and non-test validated clips from Mozilla Common Voice version en_1488h_2019-12-10.

Performance

The performance of Automatic Speech Recognition models is measuring using Character Error Rate.

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

3.74 % on LibriSpeech dev-clean
10.21 % on LibriSpeech dev-other

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 [2], 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.EncDecCTCModel.from_pretrained(model_name=\"stt_en_jasper10x5dr\")\n

Transcribing text with this model

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n  pretrained_name=\"stt_en_jasper10x5dr\" \\\n  audio_dir=\"\"\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] Jasper: An End-to-End Convolutional Neural Acoustic Model

[2] 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.

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The pretrained models here can be used immediately for fine-tuning or dataset evaluation.\n\nTrained or fine-tuned NeMo models (with the file extenstion `.nemo`) can be converted to Riva models (with the file extension `.riva`) and then deployed. [Here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/speechtotext_en_us_jasper) is a pre-trained Jasper speech-to-text (STT) -- a.k.a. automatic speech recognition (ASR) -- Riva model.\n\n## Model Architecture\n\nThe Jasper model is composed of multiple blocks with residual connections between them, trained with CTC loss. Each block consists of one or more modules with convolutional layers, batch normalization, and ReLU layers.\n\nThe Jasper 10x5-DR model consists of a stack of five blocks that repeat ten times plus four additional convolutional layers [1].\n\n\n## Training\n\nThis Jasper model was trained on a combination of seven datasets of English speech, with a total of 7,057 hours of audio samples. Samples were limited to a minimum duration of 0.1s long, and a maximum duration of 16.7s long. The model was trained for 600 epochs with Apex/Amp optimization level O1. The NeMo toolkit [2] was used for training this model over several hundred epochs on multiple GPUs.\n\nThe model has been fine-tuned with Room Impulse Response (RIR) and noise augmentation to make it more robust to noise.\n\n### Datasets\n\nThe datasets included in training are detailed in the table below. The \"Duration\" column indicates how many hours of audio are contained in that dataset before length filtering was performed.\n\n\n| Dataset \t| Speed Perturbed \t| Duration (h) \t|\n|--------------------------------\t|-----------------\t|--------------\t|\n| LibriSpeech \t| Y \t| 2,903 \t|\n| Wall Street Journal \t| Y \t| 245 \t|\n| Fisher English Training Speech \t| N \t| 1,906 \t|\n| Switchboard \t| N \t| 316 \t|\n| Mozilla Common Voice* \t| N \t| 1,090 \t|\n| NSC Singapore English (Part 1) \t| N \t| 1,857 \t|\n\n* Only non-dev and non-test validated clips from Mozilla Common Voice version en_1488h_2019-12-10.\n\n## Performance\n\nThe performance of Automatic Speech Recognition models is measuring using Character Error Rate. \n\nThe model obtains the following scores on the following evaluation datasets -\n\n- 3.74 % on `LibriSpeech dev-clean`\n- 10.21 % on `LibriSpeech dev-other`\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 [2], 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.EncDecCTCModel.from_pretrained(model_name=\"stt_en_jasper10x5dr\")\n```\n\n### Transcribing text with this model\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_en_jasper10x5dr\" \\\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## References\n\n[1] [Jasper: An End-to-End Convolutional Neural Acoustic Model](https://arxiv.org/abs/1904.03288)\n\n[2] [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 Jasper10x5dr",["D",1680638222290],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/stt_en_jasper10x5dr","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[238,239,240],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":250,"_242":250,"_243":251,"_245":246},{"_206":248,"_242":248,"_243":249,"_245":246},{"_206":241,"_242":241,"_243":244,"_245":246},"STT","value","filter",{"_247":246,"_242":241},"category","label","field","PytorchLightning",{"_247":246,"_242":248},"Jasperdr",{"_247":246,"_242":250},{"_107":1232,"_109":1233},"ngcApps",[1185],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1166":1167,"_1168":1169,"_1170":1171,"_1172":1173,"_1174":263,"_138":289,"_1175":1176,"_1177":56,"_1178":86,"_1179":86,"_1180":1181,"_1182":86,"_1183":1184},"amplitude",{"_1163":1164,"_800":-7,"_1165":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1157":1158,"_1159":1160,"_1161":1162},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789434060284,"1.15.0","defaultSearchSuggestions",{"_1040":168,"_1041":1042,"_1043":1044,"_1045":1046},"announcements",[],"consentState",{"_326":1030,"_1031":86,"_1032":31,"_1033":31,"_1034":1035},"consentConfig",{"_308":309,"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327},"showContactAdmin",1789434060285,[],[],[303,304,305,306,307],{"_45":60,"_47":70,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":68,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":102,"_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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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.","linkedin.com",[418],{"_78":419,"_373":416,"_374":31,"_167":420},"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. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.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.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","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. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","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.",[557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581],[528,529,530,531,532],{"_367":442,"_206":442,"_167":168,"_340":552},{"_367":416,"_206":416,"_167":168,"_340":547},{"_367":542,"_206":542,"_167":168,"_340":543},{"_367":537,"_206":537,"_167":168,"_340":538},{"_367":533,"_206":533,"_167":168,"_340":534},"nvidia.partners",[535],{"_78":536,"_373":533,"_374":31,"_167":410},"edge","app.hushly.com",[539],{"_78":540,"_373":537,"_374":31,"_167":541},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[544],{"_78":545,"_373":542,"_374":31,"_167":546},"_octo","This domain is owned by Github. 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We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_gdxxxxxxxxxxxxx","nvidianews.nvidia.com","docs.api.nvidia.com","research.nvidia.com","readme_library","wcmmode","ai-podcast.nvidia.com","resources.nvidia.com","geo_locator","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","crowdsource.nvidia.com","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","readme_language","vid","This cookie name 'vid' is used for tracking the search request made by the user on the 'Find a practitioner' page.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","blogs.nvidia.com","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.","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 performance.",[640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668],[620,621,622,623],{"_367":635,"_206":635,"_167":168,"_340":636},{"_367":630,"_206":630,"_167":168,"_340":631},{"_367":429,"_206":429,"_167":168,"_340":627},{"_367":542,"_206":542,"_167":168,"_340":624},[625],{"_78":626,"_373":542,"_374":86,"_167":546},"_gh_sess",[628],{"_78":629,"_373":429,"_374":86,"_167":436},"YSC","bizible.com",[632],{"_78":633,"_373":630,"_374":31,"_167":634},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[637],{"_78":638,"_373":635,"_374":31,"_167":639},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_78":710,"_373":488,"_374":31,"_167":711},{"_78":695,"_373":709,"_374":86,"_167":696},{"_78":678,"_373":491,"_374":31,"_167":603},{"_78":678,"_373":591,"_374":31,"_167":603},{"_78":707,"_373":488,"_374":31,"_167":708},{"_78":695,"_373":494,"_374":86,"_167":696},{"_78":705,"_373":488,"_374":31,"_167":706},{"_78":695,"_373":491,"_374":86,"_167":696},{"_78":703,"_373":590,"_374":31,"_167":704},{"_78":695,"_373":612,"_374":86,"_167":696},{"_78":675,"_373":491,"_374":31,"_167":677},{"_78":701,"_373":488,"_374":86,"_167":702},{"_78":699,"_373":488,"_374":31,"_167":700},{"_78":697,"_373":488,"_374":86,"_167":698},{"_78":695,"_373":586,"_374":86,"_167":696},{"_78":693,"_373":488,"_374":31,"_167":694},{"_78":691,"_373":488,"_374":31,"_167":692},{"_78":689,"_373":488,"_374":86,"_167":690},{"_78":687,"_373":595,"_374":31,"_167":688},{"_78":675,"_373":586,"_374":31,"_167":677},{"_78":685,"_373":488,"_374":31,"_167":686},{"_78":683,"_373":488,"_374":86,"_167":684},{"_78":681,"_373":488,"_374":31,"_167":682},{"_78":679,"_373":488,"_374":31,"_167":680},{"_78":678,"_373":488,"_374":31,"_167":603},{"_78":675,"_373":676,"_374":31,"_167":677},{"_78":673,"_373":488,"_374":31,"_167":674},{"_78":671,"_373":488,"_374":86,"_167":672},{"_78":669,"_373":488,"_374":31,"_167":670},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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