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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(trainable_v3.0)","nvidia/tao/speechtotext_english_citrinet","key","nvidia/tao/models/speechtotext_english_citrinet","resourceType","models","resourceId","speechtotext_english_citrinet","shortDescription","Speech to Text Citrinet models for English.","# Speech Recognition: CitriNet Model Card ========================================\n\n# Model Overview --------------\n\nCitriNet models are end-to-end neural automatic speech recognition (ASR) models that transcribe segments of audio to text.\n\n# Model Architecture ------------------\n\nCitrinet is a version of [QuartzNet](https://arxiv.org/pdf/1910.10261.pdf) that extends [ContextNet](https://arxiv.org/pdf/2005.03191.pdf), utilizing subword encoding (via Word Piece tokenization) and Squeeze-and-Excitation(SE) mechanism and are therefore smaller than QuartzNet models.\n\nCitriNet 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.\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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If you do not allow these cookies, then some or all of these services may not function properly.",[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],{"_404":479,"_225":479,"_167":168,"_377":589},{"_404":453,"_225":453,"_167":168,"_377":584},{"_404":579,"_225":579,"_167":168,"_377":580},{"_404":574,"_225":574,"_167":168,"_377":575},{"_404":570,"_225":570,"_167":168,"_377":571},"nvidia.partners",[572],{"_77":573,"_410":570,"_411":30,"_167":447},"edge","app.hushly.com",[576],{"_77":577,"_410":574,"_411":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,"_410":579,"_411":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. 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