Model Overview

This model for Voice Activity Detection (VAD), which can serve as the first step for Automatic Speech Recognition (ASR) and Speaker Diarization (SD). Different from segment-based VAD that predicts whether the input audio contains speech or not, this model is a frame-based VAD, which outputs a speech probablity for each 20ms frame of the input audio. The model is trained on a combination of synthetic and real-world data to achieve more robust performance in very noisy situations.

Model Architecture

The model is based on MarbleNet architecture presented in MarbleNet paper [1]. Different from the paper, the stride for the first convolution is 2, so that there is a 2x subsampling rate of the model. Also, the input feature of this model is un-normalized log-mel spectrogram with n_mels=80 so it can be easily and efficiently integrated with ASR. For ASR+VAD pipeline, please refer to this example.

Training

The NeMo toolkit [2] was used for training the models for 50 epochs, with noise and gain augmentation. This model is trained with this example script and this base config.

Full config can be found inside the .nemo files.

Datasets

While training this model, we used the following datasets:

Synthetic Data:

We use the NeMo ASR data simulator to generate synthetic data. Each session is 3 minutes long, and the mean silence ratio is set to 0.3, while the mean overlap is set to 0.05. The variance of both silence and overlap is set to 0.005. The generated audios are split into segments of 20 seconds for training.

The synthetic dataset consists of the following:

500 hours of audio synthesized from LibriSpeech training set.
500 hours of audio synthesized from Fisher.

Real-world Data:

Subset from cleaned German (mcv7.0), Mandarin (aishell2), French (mls), Russian (mcv, ruls, sova) , Spanish (mls) from NeMo ASR set, total of about 2.5K hours.

Noise Augmentation:

Subset of Freesound background data
Subset of MUSAN
Real room white noise

Performance

The AUROC performance is listed in the following table.

\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\">

Version	AMI	AVA	CallHome-109	VoxConv-test
1.20.0	95.83	93.77	92.43	96.45

How to Use this Model

The model is available for use in the NeMo toolkit [2], and can be used as a pre-trained checkpoint for inference.

Automatically load the model from NGC

import nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.EncDecFrameClassificationModel.from_pretrained(model_name=\"vad_multilingual_frame_marblenet\")\n

Perform VAD Inference

/examples/asr/speech_classification/frame_vad_infer.py --config-path="../conf/vad" --config-name="frame_vad_infer_postprocess.yaml" dataset=\n\">

python <NEMO_ROOT>/examples/asr/speech_classification/frame_vad_infer.py --config-path=\"../conf/vad\" --config-name=\"frame_vad_infer_postprocess.yaml\" dataset=<Path of manifest file of evaluation data, where audio files should have unique names>\n

Input

This model accepts 16 KHz Mono-channel Audio (wav files) as input.

Output

This model provides a sequance of speech probabilities for each 20ms frame of the input audio.

Limitations

Since this model was trained on publically available datasets, the performance of this model might degrade for custom data that the model has not been trained on.

Reference

[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.

[2] NVIDIA NeMo Toolkit

Licence

License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.

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Different from [segment-based VAD](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_marblenet) that predicts whether the input audio contains speech or not, this model is a frame-based VAD, which outputs a speech probablity for each 20ms frame of the input audio. The model is trained on a combination of synthetic and real-world data to achieve more robust performance in very noisy situations.\n\n## Model Architecture\n\nThe model is based on MarbleNet architecture presented in MarbleNet paper [1]. Different from the paper, the stride for the first convolution is 2, so that there is a 2x subsampling rate of the model. Also, the input feature of this model is un-normalized log-mel spectrogram with n_mels=80 so it can be easily and efficiently integrated with ASR. For ASR+VAD pipeline, please refer to [this example](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_vad).\n\n## Training\n\nThe NeMo toolkit [2] was used for training the models for 50 epochs, with noise and gain augmentation. This model is trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_classification/speech_to_frame_label.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/marblenet/marblenet_3x2x64_20ms.yaml).\n\nFull config can be found inside the .nemo files.\n\n### Datasets\n\nWhile training this model, we used the following datasets:\n\n#### Synthetic Data:\n\nWe use the [NeMo ASR data simulator](https://github.com/NVIDIA/NeMo/tree/main/tools/speech_data_simulator) to generate synthetic data. Each session is 3 minutes long, and the mean silence ratio is set to 0.3, while the mean overlap is set to 0.05. The variance of both silence and overlap is set to 0.005. The generated audios are split into segments of 20 seconds for training.\n\nThe synthetic dataset consists of the following:\n- 500 hours of audio synthesized from [LibriSpeech](http://www.openslr.org/12) training set.\n- 500 hours of audio synthesized from [Fisher](https://catalog.ldc.upenn.edu/LDC2004T19).\n\n#### Real-world Data:\n\nSubset from cleaned German (mcv7.0), Mandarin (aishell2), French (mls), Russian (mcv, ruls, sova) , Spanish (mls) from NeMo ASR set, total of about 2.5K hours.\n\n#### Noise Augmentation:\n\n- Subset of [Freesound background data](https://freesound.org/)\n- Subset of [MUSAN](https://www.openslr.org/17/)\n- Real room white noise\n\n## Performance\n\nThe AUROC performance is listed in the following table.\n| Version | AMI | AVA | CallHome-109 | VoxConv-test|\n|:-------:|:-----:|:----:|:------------:|:-----------:|\n| 1.20.0 | 95.83 |93.77 | 92.43 | 96.45 |\n\n## How to Use this Model\n\nThe model is available for use in the NeMo toolkit [2], and can be used as a pre-trained checkpoint for inference.\n\n### Automatically load the model from NGC\n\n```python\nimport nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.EncDecFrameClassificationModel.from_pretrained(model_name=\"vad_multilingual_frame_marblenet\")\n```\n\n### Perform VAD Inference\n\n```python\npython /examples/asr/speech_classification/frame_vad_infer.py --config-path=\"../conf/vad\" --config-name=\"frame_vad_infer_postprocess.yaml\" dataset=\n```\n\n### Input\n\nThis model accepts 16 KHz Mono-channel Audio (wav files) as input.\n\n### Output\n\nThis model provides a sequance of speech probabilities for each 20ms frame of the input audio.\n\n### Limitations\n\nSince this model was trained on publically available datasets, the performance of this model might degrade for custom data that the model has not been trained on.\n\n## Reference\n\n[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\n[2] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n## Licence\n\nLicense to use this model is covered by the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms).","displayName","Frame-VAD Multilingual MarbleNet",["D",1683052848511],"publisher","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/vad_multilingual_frame_marblenet","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[272,273,274,275,276,277,278,279,280,281],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_242":305,"_110":305,"_283":306,"_285":286},{"_242":303,"_110":303,"_283":304,"_285":286},{"_242":301,"_110":301,"_283":302,"_285":286},{"_242":299,"_110":299,"_283":300,"_285":286},{"_242":297,"_110":297,"_283":298,"_285":286},{"_242":294,"_110":294,"_283":295,"_285":286},{"_242":292,"_110":292,"_283":293,"_285":286},{"_242":290,"_110":290,"_283":291,"_285":286},{"_242":288,"_110":288,"_283":289,"_285":286},{"_242":282,"_110":282,"_283":284,"_285":286},"Zh","filter",{"_287":286,"_110":282},"category","label","field","Voice Activity Detection",{"_287":286,"_110":288},"Ru",{"_287":286,"_110":290},"PytorchLightning",{"_287":286,"_110":292},"PyTorch",{"_287":296,"_110":294},"framework","NeMo",{"_287":296,"_110":297},"Fr",{"_287":286,"_110":299},"Es",{"_287":286,"_110":301},"En",{"_287":286,"_110":303},"De",{"_287":286,"_110":305},{"_154":1285,"_156":1286},"ngcApps",[1238],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1219":1220,"_1221":1222,"_1223":1224,"_1225":1226,"_1227":318,"_185":342,"_1228":1229,"_1230":59,"_1231":133,"_1232":133,"_1233":1234,"_1235":133,"_1236":1237},"amplitude",{"_1216":1217,"_854":-7,"_1218":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1210":1211,"_1212":1213,"_1214":1215},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1094":214,"_1095":1096,"_1097":1098,"_1099":1100},"announcements",[],"consentState",{"_380":1084,"_1085":34,"_1086":34,"_1087":133,"_1088":1089},"consentConfig",{"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379,"_380":381},"showContactAdmin",1785046292341,[],[],[356,357,358,359,360,361],{"_48":63,"_50":74,"_52":149,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":149,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":68,"_52":149,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":149,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":57,"_58":59},{"_48":49,"_50":51,"_52":149,"_54":-5,"_56":-5,"_58":59},"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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","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. 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.",[611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635],[582,583,584,585,586],{"_421":496,"_242":496,"_213":214,"_394":606},{"_421":470,"_242":470,"_213":214,"_394":601},{"_421":596,"_242":596,"_213":214,"_394":597},{"_421":591,"_242":591,"_213":214,"_394":592},{"_421":587,"_242":587,"_213":214,"_394":588},"nvidia.partners",[589],{"_78":590,"_427":587,"_428":34,"_213":464},"edge","app.hushly.com",[593],{"_78":594,"_427":591,"_428":34,"_213":595},"*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",[598],{"_78":599,"_427":596,"_428":34,"_213":600},"_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.",[694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722],[674,675,676,677],{"_421":689,"_242":689,"_213":214,"_394":690},{"_421":684,"_242":684,"_213":214,"_394":685},{"_421":483,"_242":483,"_213":214,"_394":681},{"_421":596,"_242":596,"_213":214,"_394":678},[679],{"_78":680,"_427":596,"_428":133,"_213":600},"_gh_sess",[682],{"_78":683,"_427":483,"_428":133,"_213":490},"YSC","bizible.com",[686],{"_78":687,"_427":684,"_428":34,"_213":688},"_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",[691],{"_78":692,"_427":689,"_428":34,"_213":693},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_78":764,"_427":542,"_428":34,"_213":765},{"_78":749,"_427":763,"_428":133,"_213":750},{"_78":732,"_427":545,"_428":34,"_213":657},{"_78":732,"_427":645,"_428":34,"_213":657},{"_78":761,"_427":542,"_428":34,"_213":762},{"_78":749,"_427":548,"_428":133,"_213":750},{"_78":759,"_427":542,"_428":34,"_213":760},{"_78":749,"_427":545,"_428":133,"_213":750},{"_78":757,"_427":644,"_428":34,"_213":758},{"_78":749,"_427":666,"_428":133,"_213":750},{"_78":729,"_427":545,"_428":34,"_213":731},{"_78":755,"_427":542,"_428":133,"_213":756},{"_78":753,"_427":542,"_428":34,"_213":754},{"_78":751,"_427":542,"_428":133,"_213":752},{"_78":749,"_427":640,"_428":133,"_213":750},{"_78":747,"_427":542,"_428":34,"_213":748},{"_78":745,"_427":542,"_428":34,"_213":746},{"_78":743,"_427":542,"_428":133,"_213":744},{"_78":741,"_427":649,"_428":34,"_213":742},{"_78":729,"_427":640,"_428":34,"_213":731},{"_78":739,"_427":542,"_428":34,"_213":740},{"_78":737,"_427":542,"_428":133,"_213":738},{"_78":735,"_427":542,"_428":34,"_213":736},{"_78":733,"_427":542,"_428":34,"_213":734},{"_78":732,"_427":542,"_428":34,"_213":657},{"_78":729,"_427":730,"_428":34,"_213":731},{"_78":727,"_427":542,"_428":34,"_213":728},{"_78":725,"_427":542,"_428":133,"_213":726},{"_78":723,"_427":542,"_428":34,"_213":724},"_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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These cookies are required for the site to function and cannot be turned off.",[911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983],[771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795],{"_421":908,"_242":908,"_213":214,"_394":909},{"_421":902,"_242":902,"_213":214,"_394":903},{"_421":899,"_242":899,"_213":214,"_394":900},{"_421":887,"_242":887,"_213":214,"_394":888},{"_421":884,"_242":884,"_213":214,"_394":885},{"_421":881,"_242":881,"_213":214,"_394":882},{"_421":876,"_242":876,"_213":214,"_394":877},{"_421":483,"_242":483,"_213":214,"_394":873},{"_421":870,"_242":870,"_213":214,"_394":871},{"_421":475,"_242":475,"_213":214,"_394":868},{"_421":470,"_242":470,"_213":214,"_394":866},{"_421":861,"_242":861,"_213":214,"_394":862},{"_421":856,"_242":856,"_213":214,"_394":857},{"_421":851,"_242":851,"_213":214,"_394":852},{"_421":848,"_242":848,"_213":214,"_394":849},{"_421":596,"_242":596,"_213":214,"_394":845},{"_421":455,"_242":455,"_213":214,"_394":842},{"_421":591,"_242":591,"_213":214,"_394":835},{"_421":587,"_242":587,"_213":214,"_394":829},{"_421":823,"_242":823,"_213":214,"_394":824},{"_421":818,"_242":818,"_213":214,"_394":819},{"_421":813,"_242":813,"_213":214,"_394":814},{"_421":809,"_242":809,"_213":214,"_394":810},{"_421":804,"_242":804,"_213":214,"_394":805},{"_421":796,"_242":796,"_213":214,"_394":797},"polyfill.io",[798,799],{"_78":802,"_427":796,"_428":34,"_213":803},{"_78":800,"_427":796,"_428":34,"_213":801},"cf_ob_info","This cookie is associated with sites using CloudFlare. 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Key	Value
Architecture	MarbleNet-3x2x64-20ms
Outputs	Sequence of speech probability for each 20ms frame
Inputs	Audio
Number of Weights	94K