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 TLT - ASR would be the TLT - 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 .ejrvs file
Deployment: Sample workflow to consume the .ejrvs file and deploy it to Jarvis.

If you are a seasoned Conversation AI developer we recommend installing TLT and referring to the TLT 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 TLT inside a virtual environment. The steps to do the same are as follows

\nsource /bin/activate\npip install jupyter notebook # If you need to run the notebooks\n\">

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

TLT is 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-tlt\n

To download the jupyter notebook please:

Download the samples using the ngc cli with the following command

ngc registry resource download-version \"nvidia/tlt-jarvis/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 TLT Conversational AI, you would be accepting the terms of the Jarvis 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 TLT - ASR would be the TLT - 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 `.ejrvs` file\n2. Deployment: Sample workflow to consume the `.ejrvs` file and deploy it to Jarvis.\n\nIf you are a seasoned Conversation AI developer we recommend installing TLT and referring to the [TLT documentation](https://docs.nvidia.com/TLT/tlt-user-guide/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 TLT 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\nTLT is 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-tlt\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/tlt-jarvis/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 TLT Conversational AI, you would be accepting the terms of the Jarvis [license](https://developer.nvidia.com/ngc/nvidia-jarvis-license)","displayName","Speech to Text Notebook",["D",1680632601497],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tlt-jarvis/resources/speechtotext_notebook","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/TLT-Jarvis.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[244,245,246,247,248,249,250,251],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_212":274,"_253":275,"_254":276,"_256":257},{"_212":272,"_253":272,"_254":273,"_256":257},{"_212":269,"_253":270,"_254":271,"_256":257},{"_212":267,"_253":267,"_254":268,"_256":257},{"_212":263,"_253":264,"_254":265,"_256":257},{"_212":261,"_253":261,"_254":262,"_256":257},{"_212":259,"_253":259,"_254":260,"_256":257},{"_212":252,"_253":252,"_254":255,"_256":257},"Transfer Learning","value","filter",{"_258":257,"_253":252},"category","label","field","Quartznet",{"_258":257,"_253":259},"Jasper",{"_258":257,"_253":261},"Inference","soln_inference",{"_258":266,"_253":264},"technology","Finetuning",{"_258":257,"_253":267},"Conversational AI","soln_conversational_ai",{"_258":266,"_253":270},"Citrinet",{"_258":257,"_253":272},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_258":277,"_253":275},"application",{"_112":1259,"_114":1260},"ngcApps",[1212],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1193":1194,"_1195":1196,"_1197":1198,"_1199":1200,"_1201":289,"_143":313,"_1202":1203,"_1204":56,"_1205":91,"_1206":91,"_1207":1208,"_1209":91,"_1210":1211},"amplitude",{"_1190":1191,"_827":-7,"_1192":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1184":1185,"_1186":1187,"_1188":1189},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1067":394,"_1068":1069,"_1070":1071,"_1072":1073},"announcements",[],"consentState",{"_352":1057,"_1058":91,"_1059":31,"_1060":31,"_1061":1062},"consentConfig",{"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353},"showContactAdmin",1785261899292,[],[],[327,328,329,330,331,332],{"_45":59,"_47":70,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":66,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":333,"_55":56},{"_45":46,"_47":48,"_49":107,"_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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Name	Size	Updated	Actions
speech-to-text-deployment.ipynbView Notebook	12.28 KB	July 28, 2021 UTC	curl wget Download
speech-to-text-training.ipynbView Notebook	21.6 KB	July 28, 2021 UTC	curl wget Download