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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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618],[565,566,567,568,569],{"_403":478,"_225":478,"_167":168,"_376":589},{"_403":452,"_225":452,"_167":168,"_376":584},{"_403":579,"_225":579,"_167":168,"_376":580},{"_403":574,"_225":574,"_167":168,"_376":575},{"_403":570,"_225":570,"_167":168,"_376":571},"nvidia.partners",[572],{"_77":573,"_409":570,"_410":30,"_167":446},"edge","app.hushly.com",[576],{"_77":577,"_409":574,"_410":30,"_167":578},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[581],{"_77":582,"_409":579,"_410":30,"_167":583},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705],[657,658,659,660],{"_403":672,"_225":672,"_167":168,"_376":673},{"_403":667,"_225":667,"_167":168,"_376":668},{"_403":465,"_225":465,"_167":168,"_376":664},{"_403":579,"_225":579,"_167":168,"_376":661},[662],{"_77":663,"_409":579,"_410":85,"_167":583},"_gh_sess",[665],{"_77":666,"_409":465,"_410":85,"_167":472},"YSC","bizible.com",[669],{"_77":670,"_409":667,"_410":30,"_167":671},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[674],{"_77":675,"_409":672,"_410":30,"_167":676},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_77":747,"_409":524,"_410":30,"_167":748},{"_77":732,"_409":746,"_410":85,"_167":733},{"_77":715,"_409":527,"_410":30,"_167":640},{"_77":715,"_409":628,"_410":30,"_167":640},{"_77":744,"_409":524,"_410":30,"_167":745},{"_77":732,"_409":530,"_410":85,"_167":733},{"_77":742,"_409":524,"_410":30,"_167":743},{"_77":732,"_409":527,"_410":85,"_167":733},{"_77":740,"_409":627,"_410":30,"_167":741},{"_77":732,"_409":649,"_410":85,"_167":733},{"_77":712,"_409":527,"_410":30,"_167":714},{"_77":738,"_409":524,"_410":85,"_167":739},{"_77":736,"_409":524,"_410":30,"_167":737},{"_77":734,"_409":524,"_410":85,"_167":735},{"_77":732,"_409":623,"_410":85,"_167":733},{"_77":730,"_409":524,"_410":30,"_167":731},{"_77":728,"_409":524,"_410":30,"_167":729},{"_77":726,"_409":524,"_410":85,"_167":727},{"_77":724,"_409":632,"_410":30,"_167":725},{"_77":712,"_409":623,"_410":30,"_167":714},{"_77":722,"_409":524,"_410":30,"_167":723},{"_77":720,"_409":524,"_410":85,"_167":721},{"_77":718,"_409":524,"_410":30,"_167":719},{"_77":716,"_409":524,"_410":30,"_167":717},{"_77":715,"_409":524,"_410":30,"_167":640},{"_77":712,"_409":713,"_410":30,"_167":714},{"_77":710,"_409":524,"_410":30,"_167":711},{"_77":708,"_409":524,"_410":85,"_167":709},{"_77":706,"_409":524,"_410":30,"_167":707},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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