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Model Overview

The Jasper model is an end-to-end neural acoustic model for automatic speech recognition (ASR).

Model Architecture

Details on the model architecture can be found in the paper Jasper: An End-to-End Convolutional Neural Acoustic Model.

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

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


Figure 1: Jasper BxR model: B- number of blocks, R- number of sub-blocks	Figure 2: Jasper Dense Residual

Jasper is an end-to-end neural acoustic model that is based on convolutions.\nIn the audio processing stage, each frame is transformed into mel-scale spectrogram features, which the acoustic model takes as input and outputs a probability distribution over the vocabulary for each frame.\nThe acoustic model has a modular block structure and can be parametrized accordingly:\na Jasper BxR model has B blocks, each consisting of R repeating sub-blocks.

Each sub-block applies the following operations in sequence: 1D-Convolution, Batch Normalization, ReLU activation, and Dropout.

Each block input is connected directly to the last subblock of all following blocks via a residual connection, which is referred to as dense residual in the paper.\nEvery block differs in kernel size and number of filters, which are increasing in size from the bottom to the top layers.\nIrrespective of the exact block configuration parameters B and R, every Jasper model has four additional convolutional blocks:\none immediately succeeding the input layer (Prologue) and three at the end of the B blocks (Epilogue).

The Prologue is to decimate the audio signal\nin time in order to process a shorter time sequence for efficiency. The Epilogue with dilation captures a bigger context around an audio time step, which decreases the model word error rate (WER).\nThe paper achieves best results with Jasper 10x5 with dense residual connections, which is also the focus of this repository and is in the following referred to as Jasper Large.

Training

This model was trained using script available on NGC and in GitHub repo.

Dataset

The following datasets were used to train this model:

LibriSpeech - Corpus of approximately 1000 hours of 16kHz read English speech derived from audiobooks from the LibriVox project, carefully segmented and aligned.

Performance

Performance numbers for this model are available in NGC.

References

License

This model was trained using open-source software available in Deep Learning Examples repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.

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|\n|:---:|:---:|\n|Figure 1: Jasper BxR model: B- number of blocks, R- number of sub-blocks | Figure 2: Jasper Dense Residual |\n\nJasper is an end-to-end neural acoustic model that is based on convolutions.\nIn the audio processing stage, each frame is transformed into mel-scale spectrogram features, which the acoustic model takes as input and outputs a probability distribution over the vocabulary for each frame.\nThe acoustic model has a modular block structure and can be parametrized accordingly:\na Jasper BxR model has B blocks, each consisting of R repeating sub-blocks.\n\nEach sub-block applies the following operations in sequence: 1D-Convolution, Batch Normalization, ReLU activation, and Dropout.\n\nEach block input is connected directly to the last subblock of all following blocks via a residual connection, which is referred to as `dense residual` in the paper.\nEvery block differs in kernel size and number of filters, which are increasing in size from the bottom to the top layers.\nIrrespective of the exact block configuration parameters B and R, every Jasper model has four additional convolutional blocks:\none immediately succeeding the input layer (Prologue) and three at the end of the B blocks (Epilogue).\n\nThe Prologue is to decimate the audio signal\nin time in order to process a shorter time sequence for efficiency. The Epilogue with dilation captures a bigger context around an audio time step, which decreases the model word error rate (WER).\nThe paper achieves best results with Jasper 10x5 with dense residual connections, which is also the focus of this repository and is in the following referred to as Jasper Large.\n\n\n \n## Training\n\nThis model was trained using script available on [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/jasper_pyt) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechRecognition/Jasper).\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [LibriSpeech](http://www.openslr.org/12) - Corpus of approximately 1000 hours of 16kHz read English speech derived from audiobooks from the LibriVox project, carefully segmented and aligned.\n\n\n## Performance\n\nPerformance numbers for this model are available in [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/jasper_pyt/performance).\n\n## References\n- [Original paper](https://arxiv.org/pdf/1904.03288.pdf)\n- [NVIDIA model implementation in NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/jasper_pyt)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechRecognition/Jasper)\n\n## License\n\nThis model was trained using open-source software available in [Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechRecognition/Jasper) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","Jasper checkpoint (PyTorch, AMP, LibriSpeech)",["D",1680638302331],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/jasper__pyt_ckpt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239,240],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_207":248,"_242":248,"_243":249,"_245":246},{"_207":241,"_242":241,"_243":244,"_245":246},"NLU","value","filter",{"_247":246,"_242":241},"category","label","field","NLP",{"_247":246,"_242":248},{"_108":1227,"_110":1228},"ngcApps",[1180],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1161":1162,"_1163":1164,"_1165":1166,"_1167":1168,"_1169":261,"_139":287,"_1170":1171,"_1172":56,"_1173":87,"_1174":87,"_1175":1176,"_1177":87,"_1178":1179},"amplitude",{"_1158":1159,"_798":-7,"_1160":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1152":1153,"_1154":1155,"_1156":1157},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787666716647,"1.12.0","defaultSearchSuggestions",{"_1038":169,"_1039":1040,"_1041":1042,"_1043":1044},"announcements",[],"consentState",{"_324":1028,"_1029":87,"_1030":31,"_1031":31,"_1032":1033},"consentConfig",{"_306":307,"_308":309,"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325},"showContactAdmin",1787666716648,[],[],[301,302,303,304,305],{"_45":60,"_47":71,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":59,"_55":56},"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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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. 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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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