Speech Recognition: CitriNet Model Card ========================================

Model Overview --------------

CitriNet models are end-to-end neural automatic speech recognition (ASR) models that transcribe segments of audio to text.

Model Architecture ------------------

Citrinet is a version of QuartzNet that extends ContextNet, utilizing subword encoding (via Word Piece tokenization) and Squeeze-and-Excitation(SE) mechanism and are therefore smaller than QuartzNet models.

CitriNet models take in audio segments and transcribe them to letter, byte pair, or word piece sequences.\nThe pretrained models here can be used immediately for fine-tuning or dataset evaluation.

Intended Use ------------

Primary use case intended for these models is automatic speech recognition.

Input:\nSingle-channel audio files (WAV) with a 16kHz sample rate

Output:\nTranscripts, which are sequences of valid vocabulary labels as given by the specification file

How to Use This Model ---------------------

CitriNet is an end-to-end architecture that is trained using CTC loss.\nThese model checkpoints are intended to be used with the Train Adapt Optimize (TAO) Toolkit.\nIn order to use these checkpoints, there should be a specification file (.yaml) that specifies hyperparameters, datasets for training and evaluation, and any other information needed for the experiment.\nFor more information on the experiment spec files for each use case, please refer to the TAO Toolkit User Guide.

The models are encrypted with the key tlt_encode.

To fine-tune from a model checkpoint (.tlt), use the following command:

!tao speech_to_text_citrinet finetune -e \\\n -m \\\n -g \\\n -r \n

Where the `` parameter should be a valid path to the file that specifies the fine-tuning hyperparameters, the dataset to fine-tune on, the dataset to evaluate on, and whether or not a change of vocabulary from the default (lowercase English letters, space, and apostrophe) is needed.

To evaluate an existing dataset using a model checkpoint (.tlt), use the following command:

!tao speech_to_text_citrinet evaluate -e \\\n -m \\\n -g \\\n -r \n

The `` parameter should be a valid path to the file that specifies the dataset that is being evaluated.

Training Information --------------------

The model was trained on various proprietary and open-source datasets. These datasets include noisy data,\nmultiple sampling rates including 8khz for call centers, variety of accents, domain specific data for various\ndomains, spontaneous speech and dialogue, all of which contribute to the model’s accuracy.

This model delivers WER that is better than or comparable to popular alternate Speech to Text solutions\nfor a range of domains and use cases.

Limitations -----------

Currently, TAO CitriNet models only support training and inference on .wav audio files.\nAll models included here were trained and evaluated on audio files with a sample rate of 16kHz, so for best performance you may need to upsample or downsample audio files to 16kHz.

In addition, the model will perform best on audio samples that are longer than 0.1 seconds long.\nFor training and fine-tuning CitriNet models, it is recommended that samples are capped at a maximum length of around 15 seconds, depending on the amount of memory available to you.\nYou do not need to place a maximum length limitation for evaluation.

License -------

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

Ethical AI ----------

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications.\nConsider potential algorithmic bias when choosing or creating the models being deployed.\nWork with the model’s developer to ensure that it meets the requirements for the relevant industry and use case;\nthat the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

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fine-tuning or dataset evaluation.\n\n# Intended Use ------------\n\nPrimary use case intended for these models is automatic speech recognition.\n\nInput:\nSingle-channel audio files (WAV) with a 16kHz sample rate\n\nOutput:\nTranscripts, which are sequences of valid vocabulary labels as given by the specification file\n\n# How to Use This Model ---------------------\n\nCitriNet is an end-to-end architecture that is trained using CTC loss.\nThese model checkpoints are intended to be used with the Train Adapt Optimize (TAO) Toolkit.\nIn order to use these checkpoints, there should be a specification file (.yaml) that specifies hyperparameters, datasets for training and evaluation, and any other information needed for the experiment.\nFor more information on the experiment spec files for each use case, please refer to the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/text/overview.html).\n\nThe models are encrypted with the key `tlt_encode`.\n\nTo fine-tune from a model checkpoint (.tlt), use the following command:\n\n```sh\n!tao speech_to_text_citrinet finetune -e \\\n -m \\\n -g \\\n -r \n```\n\nWhere the `` parameter should be a valid path to the file that specifies the fine-tuning hyperparameters, the dataset to fine-tune on, the dataset to evaluate on, and whether or not a change of vocabulary from the default (lowercase English letters, space, and apostrophe) is needed.\n\nTo evaluate an existing dataset using a model checkpoint (.tlt), use the following command:\n\n```sh\n!tao speech_to_text_citrinet evaluate -e \\\n -m \\\n -g \\\n -r \n```\n\nThe `` parameter should be a valid path to the file that specifies the dataset that is being evaluated.\n\n# Training Information --------------------\n\nThe model was trained on various proprietary and open-source datasets. These datasets include noisy data,\nmultiple sampling rates including 8khz for call centers, variety of accents, domain specific data for various\ndomains, spontaneous speech and dialogue, all of which contribute to the model’s accuracy.\n\nThis model delivers WER that is better than or comparable to popular alternate Speech to Text solutions\nfor a range of domains and use cases.\n\n# Limitations -----------\n\nCurrently, TAO CitriNet models only support training and inference on .wav audio files.\nAll models included here were trained and evaluated on audio files with a sample rate of 16kHz, so for best performance you may need to upsample or downsample audio files to 16kHz.\n\nIn addition, the model will perform best on audio samples that are longer than 0.1 seconds long.\nFor training and fine-tuning CitriNet models, it is recommended that samples are capped at a maximum length of around 15 seconds, depending on the amount of memory available to you.\nYou do not need to place a maximum length limitation for evaluation.\n\n# License -------\n\nBy downloading and using the models and resources packaged with TAO Conversational AI, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/license)\n\n# Suggested Reading -----------------\n\n- More information about the TAO Toolkit can be found at the NVIDIA Developer Zone: https://developer.nvidia.com/tao-toolkit\n- Read the [TAO getting Started guide](https://docs.nvidia.com/tao/tao-toolkit/text/tao_quick_start_guide.html) and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- More information about the experiment spec files can be found in the [TAO User Guide](https://docs.nvidia.com/tao/tao-toolkit/text/overview.html)\n\n# Ethical AI ----------\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications.\nConsider potential algorithmic bias when choosing or creating the models being deployed.\nWork with the model’s developer to ensure that it meets the requirements for the relevant industry and use case;\nthat the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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