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","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[250,251,252,253,254,255],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_218":273,"_257":274,"_258":275,"_260":261},{"_218":271,"_257":271,"_258":272,"_260":261},{"_218":267,"_257":268,"_258":269,"_260":261},{"_218":265,"_257":265,"_258":266,"_260":261},{"_218":263,"_257":263,"_258":264,"_260":261},{"_218":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":1452,"_115":1453},"ngcApps",[1405],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1386":1387,"_1388":1389,"_1390":1391,"_1392":1393,"_1394":288,"_144":312,"_1395":1396,"_1397":55,"_1398":92,"_1399":92,"_1400":1401,"_1402":92,"_1403":1404},"amplitude",{"_1383":1384,"_986":-7,"_1385":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1377":1378,"_1379":1380,"_1381":1382},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1261":393,"_1262":1263,"_1264":1265,"_1266":1267},"announcements",[],"consentState",{"_350":1251,"_1252":92,"_1253":31,"_1254":31,"_1255":1256},"consentConfig",{"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351},"showContactAdmin",1784229875151,[],[],[326,327,328,329,330,331],{"_45":59,"_47":70,"_49":108,"_51":-5,"_53":-5,"_54":55},{"_45":59,"_47":68,"_49":108,"_51":-5,"_53":-5,"_54":55},{"_45":59,"_47":66,"_49":108,"_51":-5,"_53":-5,"_54":55},{"_45":59,"_47":60,"_49":108,"_51":-5,"_53":-5,"_54":55},{"_45":56,"_47":57,"_49":108,"_51":-5,"_53":-5,"_54":55},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":58,"_54":55},"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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Name	Size	Updated	Actions
speech-to-text-deployment.ipynbView Notebook	12.3 KB	June 3, 2022 UTC	curl wget Download
speech-to-text-training.ipynbView Notebook	22.39 KB	June 3, 2022 UTC	curl wget Download