Speech Recognition: Jasper Model Card

Model Overview

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

Model Architecture

These models are based on the Jasper architecture. They are acoustic, end-to-end neural speech recognition models trained with CTC loss.

Jasper 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

Jasper is an end-to-end architecture that is trained using CTC loss.\nThese model checkpoints are intended to be used with the Transfer Learning Toolkit (TLT).\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 Transfer Learning Toolkit User Guide.

The model is encrypted and will only operate with the model encryption key tlt_encode.

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

\\\n -m \\\n -g \n\">

!tlt speech-to-text finetune -e <experiment_spec> \\\n                             -m <model_checkpoint> \\\n                             -g <num_gpus>\n

Where the <experiment_spec> 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:

\\\n -m \\\n -g \n\">

!tlt speech-to-text evaluate -e <experiment_spec> \\\n                             -m <model_checkpoint> \\\n                             -g <num_gpus>\n

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

Training Information

This Jasper model was trained on a combination of seven datasets of English speech, with a total\nof 7,133 hours of audio samples.\nSamples were limited to a minimum duration of 0.1s long, and a maximum duration of 16.7s long.\nThe model was trained for 600 epochs with Apex/Amp optimization level O1.

The model achieves a Word Error Rate (WER) of 3.74% on LibriSpeech dev-clean, and a WER of\n10.21% on LibriSpeech dev-other.

The model has been fine-tuned with Room Impulse Response (RIR) and noise augmentation to make it\nmore robust to noise.

The datasets included in training are detailed in the table below.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Dataset	Duration (h)
LibriSpeech	961
Wall Street Journal	81
Fisher English Training Speech	1,906
Switchboard	316
Mozilla Common Voice*	1,039
Appen English Speech	972
NSC Singapore English (Part 1)	1,857

* Only non-dev and non-test validated clips from Mozilla Common Voice version en_1488h_2019-12-10.

Limitations

Currently, TLT Jasper 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 Jasper 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 TLT Conversational AI, you would be accepting the terms of the Jarvis 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.

","resourceVersions",[193,194,195],"resourceVersionsTotal",2,"hasMoreVersions","versionMetadata",{"_167":21,"_168":169,"_48":170,"_171":-5,"_172":173,"_174":175,"_176":80,"_177":16,"_178":30,"_179":-5,"_180":30,"_181":-5,"_182":30,"_183":-5,"_184":30,"_185":-5,"_186":30,"_187":-5,"_188":30,"_189":-5,"_190":30,"_191":192},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/tlt-jarvis/models/speechtotext_english_jasper/-","version","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/tlt-jarvis/speechtotext_english_jasper:trainable_v1.2","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/tlt-jarvis/models/speechtotext_english_jasper/trainable_v1.2/version-history","accessCheckResult",{"_166":86},"hasRelatedCollections","context",{"_162":163,"_164":165},1785174667171,[],[],[155,156,157,158,159,160,161],{"_44":59,"_46":70,"_48":61,"_50":62,"_52":71,"_54":55},{"_44":59,"_46":68,"_48":61,"_50":62,"_52":69,"_54":55},{"_44":59,"_46":66,"_48":61,"_50":62,"_52":67,"_54":55},{"_44":59,"_46":64,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":61,"_50":62,"_52":63,"_54":55},{"_44":56,"_46":57,"_48":49,"_50":51,"_52":58,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},"orgName","nvidia","teamName","tlt-jarvis","allowed","versionId","description","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1613169662613],"updatedDate",["D",1740843328917],"totalSizeInBytes","totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_76":199,"_78":200,"_171":-5,"_178":30,"_180":30,"_191":197,"_182":30},{"_76":21,"_78":21,"_171":-5,"_178":30,"_180":30,"_191":198,"_182":30},{"_76":196,"_78":196,"_171":-5,"_178":30,"_180":30,"_191":197,"_182":30},"deployable_v1.2",[],[],"-","Latest (deployable_v1.2)","nvidia/tlt-jarvis/speechtotext_english_jasper","key","nvidia/tlt-jarvis/models/speechtotext_english_jasper","resourceType","models","resourceId","speechtotext_english_jasper","shortDescription","Speech to Text Jasper model for English.","Speech Recognition: Jasper Model Card\n======================================\n\nModel Overview\n--------------\nJasper models are end-to-end neural automatic speech recognition (ASR) models that transcribe segments of audio to text.\n\nModel Architecture\n------------------\nThese models are based on the [Jasper](https://arxiv.org/pdf/1904.03288.pdf) architecture. They are acoustic, end-to-end neural speech recognition models trained with CTC loss.\n\nJasper 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\nIntended 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\nHow to Use This Model\n---------------------\n\nJasper is an end-to-end architecture that is trained using CTC loss.\nThese model checkpoints are intended to be used with the Transfer Learning Toolkit (TLT).\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 [Transfer Learning Toolkit User Guide](https://docs.nvidia.com/TLT/tlt-user-guide/text/overview.html).\n\nThe model is encrypted and will only operate with the model encryption key `tlt_encode`.\n\nTo fine-tune from a model checkpoint (.tlt), use the following command:\n\n```sh\n!tlt speech-to-text finetune -e \\\n -m \\\n -g \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!tlt speech-to-text evaluate -e \\\n -m \\\n -g \n```\n\nThe `` parameter should be a valid path to the file that specifies the dataset that is being evaluated.\n\nTraining Information\n--------------------\n\nThis Jasper model was trained on a combination of seven datasets of English speech, with a total\nof 7,133 hours of audio samples.\nSamples were limited to a minimum duration of 0.1s long, and a maximum duration of 16.7s long.\nThe model was trained for 600 epochs with Apex/Amp optimization level O1.\n\nThe model achieves a Word Error Rate (WER) of 3.74% on LibriSpeech dev-clean, and a WER of\n10.21% on LibriSpeech dev-other.\n\nThe model has been fine-tuned with Room Impulse Response (RIR) and noise augmentation to make it\nmore robust to noise.\n\nThe datasets included in training are detailed in the table below.\n\n| Dataset | Duration (h) |\n| :------ | -----------: |\n| LibriSpeech | 961 |\n| Wall Street Journal | 81 |\n| Fisher English Training Speech | 1,906 |\n| Switchboard | 316 |\n| Mozilla Common Voice\\* | 1,039 |\n| Appen English Speech | 972 |\n| NSC Singapore English (Part 1) | 1,857 |\n\n\\* Only non-dev and non-test validated clips from Mozilla Common Voice version en\\_1488h\\_2019-12-10.\n\nLimitations\n-----------\nCurrently, TLT Jasper 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 Jasper 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\nLicense\n-------\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)\n\nSuggested Reading\n-----------------\n\n- More information about the Transfer Learning Toolkit can be found at the NVIDIA Developer Zone: https://developer.nvidia.com/transfer-learning-toolkit\n- Read the [TLT getting Started guide](https://docs.nvidia.com/TLT/tlt-user-guide) and [release notes](https://docs.nvidia.com/TLT/tlt-user-guide/text/release_notes.html).\n- If you would like to read more about the Jasper architecture, the paper is available here: https://arxiv.org/pdf/1904.03288.pdf\n- More information about the experiment spec files can be found in the [TLT User Guide](https://docs.nvidia.com/TLT/tlt-user-guide/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 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