Overview

QuarzNet is a end-to-end neural acoustic model for automatic speech recognition. The model is composed of multiple blocks with residual connections between them. Each block consists of one or\nmore modules with 1D time-channel separable convolutional layers, batch normalization, and ReLU layers and it is trained with CTC loss.\nQuartzNet is a Jasper-like network which uses separable convolutions and larger filter sizes. It\nhas comparable accuracy to Jasper while having much fewer parameters.

We provide a QuartzNet model pre-trained on WSJ, LibriSpeech and Mozilla's Common Voice En. Specifically, we fine-tune the pre-trained QuartzNet model available in NGC with Wall Street Journal data (CSR-I (WSJ0) Complete and CSR-II (WSJ1) Complete).

Datasets

Pre-trained on: LibriSpeech +- 10% speed perturbation and Mozilla’s Common Voice En (Validated).
Fine-tuned on: Wall Street Journal +- 10% speed perturbation (CSR-I (WSJ0) Complete and CSR-II (WSJ1) Complete).

Word Error Rate

WSJ eval-92: (Greedy) 4.45%, (+ 6-Gram WSJ Language Model) 2.39%
WSJ dev-93: (Greedy) 6.59%, (+ 6-Gram WSJ Language Model) 3.76%

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The model is composed of multiple blocks with residual connections between them. Each block consists of one or\nmore modules with 1D time-channel separable convolutional layers, batch normalization, and ReLU layers and it is trained with CTC loss. \nQuartzNet is a Jasper-like network which uses separable convolutions and larger filter sizes. It\nhas comparable accuracy to Jasper while having much fewer parameters.\n\nWe provide a QuartzNet model pre-trained on WSJ, LibriSpeech and Mozilla's Common Voice En. Specifically, we fine-tune the [pre-trained QuartzNet model available in NGC](https://ngc.nvidia.com/catalog/models/nvidia:quartznet15x5) with Wall Street Journal data (CSR-I (WSJ0) Complete and CSR-II (WSJ1) Complete).\n\n## Datasets\n- Pre-trained on: LibriSpeech +- 10% speed perturbation and Mozilla’s Common Voice En (Validated).\n- Fine-tuned on: Wall Street Journal +- 10% speed perturbation (CSR-I (WSJ0) Complete and CSR-II (WSJ1) Complete).\n\n## Word Error Rate\n- WSJ eval-92: (Greedy) 4.45%, (+ 6-Gram WSJ Language Model) 2.39% \n- WSJ dev-93: (Greedy) 6.59%, (+ 6-Gram WSJ Language Model) 3.76%","displayName","QuartzNet15x5: WSJ, LibriSpeech & MCV",["D",1680636361121],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/wsj_quartznet_15x5","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[254,255,256,257,258,259],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_223":276,"_261":276,"_262":277,"_264":265},{"_223":274,"_261":274,"_262":275,"_264":265},{"_223":272,"_261":272,"_262":273,"_264":265},{"_223":269,"_261":269,"_262":270,"_264":265},{"_223":267,"_261":267,"_262":268,"_264":265},{"_223":260,"_261":260,"_262":263,"_264":265},"Speech","value","filter",{"_266":265,"_261":260},"category","label","field","QuartzNet",{"_266":265,"_261":267},"PyTorch",{"_266":271,"_261":269},"framework","NeMo",{"_266":271,"_261":272},"Jasper",{"_266":265,"_261":274},"ASR",{"_266":265,"_261":276},{"_131":1450,"_133":1451},"ngcApps",[1403],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1385":1386,"_1387":1388,"_1389":1390,"_1391":1392,"_1393":289,"_162":313,"_1394":96,"_1395":59,"_1396":110,"_1397":110,"_1398":1399,"_1400":110,"_1401":1402},"amplitude",{"_1382":1383,"_986":-7,"_1384":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1376":1377,"_1378":1379,"_1380":1381},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1261":192,"_1262":1263,"_1264":1265,"_1266":1267},"announcements",[],"consentState",{"_352":1251,"_1252":110,"_1253":35,"_1254":35,"_1255":1256},"consentConfig",{"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353},"showContactAdmin",1784148225000,[],[],[327,328,329,330,331,332],{"_49":63,"_51":73,"_53":126,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":70,"_53":126,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":68,"_53":126,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":64,"_53":126,"_55":-5,"_57":-5,"_58":59},{"_49":60,"_51":61,"_53":126,"_55":-5,"_57":-5,"_58":59},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":333,"_58":59},7,"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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