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","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",[284,285,286,287,288,289],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_252":306,"_291":307,"_292":308,"_294":88},{"_252":304,"_291":304,"_292":305,"_294":88},{"_252":300,"_291":301,"_292":302,"_294":88},{"_252":298,"_291":298,"_292":299,"_294":88},{"_252":296,"_291":296,"_292":297,"_294":88},{"_252":290,"_291":290,"_292":293,"_294":88},"Transfer Learning","value","filter",{"_295":88,"_291":290},"category","field","Quartznet",{"_295":88,"_291":296},"Jasper",{"_295":88,"_291":298},"Inference","soln_inference",{"_295":303,"_291":301},"technology","Finetuning",{"_295":88,"_291":304},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_295":309,"_291":307},"application",{"_163":1293,"_165":1294},"ngcApps",[1246],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1227":1228,"_1229":1230,"_1231":1232,"_1233":1234,"_1235":321,"_110":349,"_1236":1237,"_1238":60,"_1239":91,"_1240":91,"_1241":1242,"_1243":91,"_1244":1245},"amplitude",{"_1224":1225,"_863":-7,"_1226":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1218":1219,"_1220":1221,"_1222":1223},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1785927837101,"1.11.0","defaultSearchSuggestions",{"_1103":430,"_1104":1105,"_1106":1107,"_1108":1109},"announcements",[],"consentState",{"_387":1093,"_1094":91,"_1095":35,"_1096":35,"_1097":1098},"consentConfig",{"_369":370,"_371":372,"_373":374,"_375":376,"_377":378,"_379":380,"_381":382,"_383":384,"_385":386,"_387":388},"showContactAdmin",1785927837102,[],[],[363,364,365,366,367,368],{"_49":63,"_51":74,"_53":158,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":70,"_53":158,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":68,"_53":158,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":158,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":158,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":16,"_59":60},"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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	Files	Compressed Size
v1.4Selected	2	34.69 KB	June 3, 2022 UTC
v1.3	2	34.52 KB	November 23, 2021 UTC
v1.2	2	34.52 KB	August 20, 2021 UTC