Automatic Speech Recognition

ASR, or Automatic Speech Recognition, refers to the problem of getting a program to automatically transcribe spoken language (speech-to-text). Our goal is usually to have a model that minimizes the Word Error Rate (WER) metric when transcribing speech input. In other words, given some audio file (e.g. a WAV file) containing speech.

The best place to get started with TAO Toolkit - ASR would be the TAO - ASR jupyter notebooks sample enclosed in this sample.\nThis resource has two notebooks included.

Training: Sample workflow for training an ASR model and export the model to a .riva file
Deployment: Sample workflow to consume the .riva file and deploy it to Riva.

If you are a seasoned Conversation AI developer we recommend installing TAO and referring to the TAO documentation for detailed information.

Pre-Requisites

Please make sure to install the following before proceeding further:

python 3.6.9
docker-ce > 19.03.5
docker-API 1.40
nvidia-container-toolkit > 1.3.0-1
nvidia-container-runtime > 3.4.0-1
nvidia-docker2 > 2.5.0-1
nvidia-driver >= 455.23

Note: A compatible NVIDIA GPU would be required.

Installation

We recommend that you install TAO Toolkit inside a virtual environment. The steps to do the same are as follows

virtualenv -p python3 \nsource /bin/activate\npip install jupyter notebook # If you need to run the notebooks\n

TAO Toolkit is a python package that is hosted in nvidia python package index. You may install by using python’s package manager, pip.

pip install nvidia-pyindex\npip install nvidia-tao\n

To download the jupyter notebook please:

Download the samples using the ngc cli with the following command

ngc registry resource download-version \"nvidia/tao/speechtotext_notebook:v1.0\"\n

Instantiate the jupyter notebook server

jupyter notebook --ip 0.0.0.0 --allow-root --port 8888\n

License

By downloading and using the models and resources packaged with TAO Toolkit Conversational AI, you would be accepting the terms of the Riva license

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Our goal is usually to have a model that minimizes the Word Error Rate (WER) metric when transcribing speech input. In other words, given some audio file (e.g. a WAV file) containing speech.\n\nThe best place to get started with TAO Toolkit - ASR would be the TAO - ASR jupyter notebooks sample enclosed in this sample.\nThis resource has two notebooks included.\n\n1. Training: Sample workflow for training an ASR model and export the model to a `.riva` file\n2. Deployment: Sample workflow to consume the `.riva` file and deploy it to Riva.\n\nIf you are a seasoned Conversation AI developer we recommend installing TAO and referring to the [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/text/overview.html) for detailed information.\n\n## Pre-Requisites\n\nPlease make sure to install the following before proceeding further:\n\n* python 3.6.9\n* docker-ce > 19.03.5\n* docker-API 1.40\n* nvidia-container-toolkit > 1.3.0-1\n* nvidia-container-runtime > 3.4.0-1\n* nvidia-docker2 > 2.5.0-1\n* nvidia-driver >= 455.23\n\n*Note: A compatible NVIDIA GPU would be required.*\n\n## Installation\n\nWe recommend that you install TAO Toolkit inside a virtual environment. The steps to do the same are as follows \n\n```sh\nvirtualenv -p python3 \nsource /bin/activate\npip install jupyter notebook # If you need to run the notebooks\n```\n\nTAO Toolkit is a python package that is hosted in nvidia python package index. You may install by using python’s package manager, pip.\n\n```sh\npip install nvidia-pyindex\npip install nvidia-tao\n```\n\nTo download the jupyter notebook please:\n\n1. Download the samples using the ngc cli with the following command\n\n```sh\nngc registry resource download-version \"nvidia/tao/speechtotext_notebook:v1.0\"\n```\n\n2. Instantiate the jupyter notebook server\n\n```sh\njupyter notebook --ip 0.0.0.0 --allow-root --port 8888\n```\n\n## License\n\nBy downloading and using the models and resources packaged with TAO Toolkit Conversational AI, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/ga/license)","displayName","Speech to Text Notebook",["D",1680631971417],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/resources/speechtotext_notebook","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO-RIVA.png","canGuestDownload","latestVersionSizeInBytes",22924,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[250,251,252,253,254,255],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_217":273,"_257":274,"_258":275,"_260":261},{"_217":271,"_257":271,"_258":272,"_260":261},{"_217":267,"_257":268,"_258":269,"_260":261},{"_217":265,"_257":265,"_258":266,"_260":261},{"_217":263,"_257":263,"_258":264,"_260":261},{"_217":256,"_257":256,"_258":259,"_260":261},"Transfer Learning","value","filter",{"_262":261,"_257":256},"category","label","field","Quartznet",{"_262":261,"_257":263},"Jasper",{"_262":261,"_257":265},"Inference","soln_inference",{"_262":270,"_257":268},"technology","Finetuning",{"_262":261,"_257":271},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_262":276,"_257":274},"application",{"_113":1451,"_115":1452},"ngcApps",[1404],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1385":1386,"_1387":1388,"_1389":1390,"_1391":1392,"_1393":288,"_145":312,"_1394":1395,"_1396":56,"_1397":92,"_1398":92,"_1399":1400,"_1401":92,"_1402":1403},"amplitude",{"_1382":1383,"_985":-7,"_1384":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1376":1377,"_1378":1379,"_1380":1381},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1260":393,"_1261":1262,"_1263":1264,"_1265":1266},"announcements",[],"consentState",{"_351":1250,"_1251":92,"_1252":31,"_1253":31,"_1254":1255},"consentConfig",{"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352},"showContactAdmin",1784172505402,[],[],[326,327,328,329,330,331],{"_45":59,"_47":70,"_49":108,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":108,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":66,"_49":108,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":108,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":332,"_55":56},{"_45":46,"_47":48,"_49":108,"_51":-5,"_53":-5,"_55":56},5,"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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If you do not allow these cookies, then some or all of these services may not function properly.",[624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644],[589,590,591,592,593],{"_391":484,"_217":484,"_172":393,"_365":615},{"_391":450,"_217":450,"_172":393,"_365":610},{"_391":603,"_217":603,"_172":393,"_365":604},{"_391":599,"_217":599,"_172":393,"_365":600},{"_391":594,"_217":594,"_172":393,"_365":595},"nvcrm.my.site.com",[596],{"_79":597,"_398":594,"_399":31,"_172":598},"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",[601],{"_79":602,"_398":599,"_399":31,"_172":433},"edge","github.com",[605,606],{"_79":609,"_398":603,"_399":31,"_172":608},{"_79":607,"_398":603,"_399":31,"_172":608},"logged_in","This domain is owned by Github. 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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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Name	Size	Updated	Actions
speech-to-text-deployment.ipynbView Notebook	12.2 KB	August 24, 2021 UTC	curl wget Download
speech-to-text-training.ipynbView Notebook	22.32 KB	August 24, 2021 UTC	curl wget Download