Speaker Diarization: MarbleNet Model Card

Model Overview

This model can be used for Voice Activity Detection (VAD) and served as first step for Speaker Diarization (SD).

Model Architecture

The model is based on MarbleNet architecture presented in MarbleNet paper [1]. Different from the paper, the input feature of this model is log-mel spectrogram with n_mels=80 so it can be easily and efficiently integrated with speaker diarization.

Training

The model was trained on mutiple publicly available datasets. The NeMo toolkit [2] was used for training this model for 50 epochs on multiple GPUs.

How to Use this Model

To use this model, we can use Riva Skills Quick start guide, it is a starting point to try out Riva models. Information regarding Quick start guide can be found : here.\nTo use Riva Speech ASR service using this model, document has the necessary information.

Input

This model accepts 16000 KHz Mono-channel Audio (wav files) as input.

Output

This model provides frame-level voice activity prediction.

References

[1] Jia, Fei, Somshubra Majumdar, and Boris Ginsburg. MarbleNet: Deep 1D Time-Channel Separable Convolutional Neural Network for Voice Activity Detection ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021.\n[2] NVIDIA NeMo Toolkit

License

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

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Different from the paper, the input feature of this model is log-mel spectrogram with n_mels=80 so it can be easily and efficiently integrated with speaker diarization.\n\n#### Training\n\nThe model was trained on mutiple publicly available datasets. The NeMo toolkit [2] was used for training this model for 50 epochs on multiple GPUs.\n\n#### How to Use this Model\n\nTo use this model, we can use Riva Skills Quick start guide, it is a starting point to try out Riva models. Information regarding Quick start guide can be found : [here](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart).\nTo use Riva Speech ASR service using this model, [document](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-overview.html) has the necessary information.\n\n#### Input\n\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.\n\n#### Output\n\nThis model provides frame-level voice activity prediction.\n\n#### References\n\n[1] Jia, Fei, Somshubra Majumdar, and Boris Ginsburg. [MarbleNet: Deep 1D Time-Channel Separable Convolutional Neural Network for Voice Activity Detection](https://arxiv.org/pdf/2010.13886.pdf) ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021.\n[2] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n#### License\n\nBy downloading and using the models and resources packaged with Riva Conversational AI, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/ga/license)","RIVA Diarizer Neural VAD",["D",1680634782981],"deployable_v1.0","/api/image/nvidia/riva/models/diarizer_vad_multilingual_marblenet","https://github.com/kbojo/images/raw/master/Nvidia-Centric-RIVA.png",348955,"bias","explainability","privacy","safetyAndSecurity",[],[249,250,251],[],[],[],{"_92":256,"_128":256,"_129":257,"_131":132},{"_92":254,"_128":254,"_129":255,"_131":132},{"_92":252,"_128":252,"_129":253,"_131":132},"VAD",{"_133":132,"_128":252},"Speaker Recognition",{"_133":132,"_128":254},"Speaker Diarization",{"_133":132,"_128":256},{"_171":1236,"_173":1237},"ngcApps",[1189],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1170":1171,"_1172":1173,"_1174":1175,"_1176":1177,"_1178":269,"_203":295,"_1179":1180,"_1181":51,"_1182":214,"_1183":214,"_1184":1185,"_1186":214,"_1187":1188},"amplitude",{"_1167":1168,"_809":-7,"_1169":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1161":1162,"_1163":1164,"_1165":1166},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789401174464,"1.15.0","defaultSearchSuggestions",{"_1049":376,"_1050":1051,"_1052":1053,"_1054":1055},"announcements",[],"consentState",{"_333":1039,"_1040":214,"_1041":26,"_1042":26,"_1043":1044},"consentConfig",{"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334},"showContactAdmin",1789401174470,[],[],[309,310,311,312,313,314],{"_40":55,"_42":66,"_44":62,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":63,"_44":62,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":62,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":62,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":62,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":62,"_46":-5,"_48":-5,"_50":51},"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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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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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.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","nv_co","__uxq412__ses.88c1","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. 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