Overview

NVIDIA NeMo toolkit supports multiple Automatic Speech Recognition(ASR) models such as Jasper and QuartzNet. Pretrained checkpoints for these models trained on standard datasets can be used immediately, use speech_to_text.py script in the examples directory.\nIn addition, models for ASR sub-tasks such as speech classification are also provided; for example MatchboxNet trained on the Google Speech Commands Dataset is available in speech_to_label.py. Voice Activity Detection is also supported with the same script, by simply changing the config file passed to the script!\nNeMo also supports training Speech Recognition models with Byte Pair/Word Piece encoding of the corpus, via the speech_to_text_bpe.py example; these models are still under development.\nIn order to simply perform evaluation on a dataset using these models, use the speech_to_text_infer.py example, which shows how to compute WER over the dataset.

Usage

You can instantiate all these models automatically directly from NGC. To do so, start your script with:

import nemo\nimport nemo.collections.asr as nemo_asr\n

Then chose what type of model you would like to instantiate. See table below for the list of model base classes. Then use base_class.from_pretrained(...) method.\nFor example:

quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name=\"QuartzNet15x5Base-En\")\n

Note that you can also list all available models using API by calling base_class.list_available_models(...) method.

You can also download all models' \".nemo\" files in the \"File Browser\" tab and then instantiate those models with base_class.restore_from(PATH_TO_DOTNEMO_FILE) method. In this case, make sure you are matching NeMo and models' versions.

Here is a list of currently available models together with their base classes and short descriptions.

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

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

Model name	Model Base Class	Description
QuartzNet15x5Base-En	`EncDecCTCModel`	QuartzNet15x5 model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1 for 600 epochs. The model achieves a WER of 3.79% on LibriSpeech dev-clean, and a WER of 10.05% on dev-other.
QuartzNet15x5Base-Zh	`EncDecCTCModel`	QuartzNet15x5 model trained on ai-shell2 Mandarin Chinese dataset.
QuartzNet5x5LS-En	`EncDecCTCModel`	QuartzNet5x5 model trained on LibriSpeech dataset only. The model achieves a WER of 5.37% on LibriSpeech dev-clean, and a WER of 15.69% on dev-other.
QuartzNet15x5NR-En	`EncDecCTCModel`	Quartznet15x5 model trained for presence of noise. The base model QuartzNet15x5Base-En was finetuned with RIR and noise augmentation to make it more robust to noise. This model should be preferred for noisy speech transcription. This model achieves a WER of 3.96% on LibriSpeech dev-clean and a WER of 10.14% on dev-other.
Jasper10x5Dr-En	`EncDecCTCModel`	JasperNet10x5Dr model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1. The model achieves a WER of 3.37% on LibriSpeech dev-clean, 9.81% on dev-other.
ContextNet-192-WPE-1024-8x-Stride	`EncDecCTCModelBPE`	ContextNet initial implementation model trained on the Librispeech corpus and achieves a WER of 10.09% on test-other and 10.11% on dev-other.
MatchboxNet-3x1x64-v1	`EncDecClassificationModel`	MatchboxNet model trained on Google Speech Commands dataset (v1, 30 classes) which obtains 97.32% accuracy on test set.
MatchboxNet-3x2x64-v1	`EncDecClassificationModel`	MatchboxNet model trained on Google Speech Commands dataset (v1, 30 classes) which obtains 97.68% accuracy on test set.
MatchboxNet-3x1x64-v2	`EncDecClassificationModel`	MatchboxNet model trained on Google Speech Commands dataset (v2, 35 classes) which obtains 97.12% accuracy on test set.
MatchboxNet-3x1x64-v2	`EncDecClassificationModel`	MatchboxNet model trained on Google Speech Commands dataset (v2, 30 classes) which obtains 97.29% accuracy on test set.
MatchboxNet-3x1x64-v2-subset-task	`EncDecClassificationModel`	MatchboxNet model trained on Google Speech Commands dataset (v2, 10+2 classes) which obtains 98.2% accuracy on test set.
MatchboxNet-3x2x64-v2-subset-task	`EncDecClassificationModel`	MatchboxNet model trained on Google Speech Commands dataset (v2, 10+2 classes) which obtains 98.4% accuracy on test set.
MatchboxNet-VAD-3x2	`EncDecClassificationModel`	Voice Activity Detection MatchboxNet model trained on google speech command (v2) and freesound background data, which obtains 0.992 accuracy on testset from same source and 0.852 TPR for FPR=0.315 on testset (ALL) of AVA movie data
SpeakerNet_recognition	`EncDecSpeakerLabelModel`	SpeakerNet_recognition model trained end-to-end for speaker recognition purposes with cross_entropy loss. It was trained on voxceleb 1, voxceleb 2 dev datasets and augmented with musan music and noise. Speaker Recognition model achieves 2.65% EER on voxceleb-O cleaned trial file\"
SpeakerNet_verification	`EncDecSpeakerLabelModel`	SpeakerNet_verification model trained end-to-end for speaker verification purposes with arcface angular softmax loss. It was trained on voxceleb 1, voxceleb 2 dev datasets and augmented with musan music and noise. Speaker Verification model achieves 2.12% EER on voxceleb-O cleaned trial file

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These models are used for Automatic Speech Recognition (ASR) and sub-tasks.","## Overview\n[NVIDIA NeMo](https://github.com/NVIDIA/NeMo) toolkit supports multiple Automatic Speech Recognition(ASR) models such as Jasper and QuartzNet. Pretrained checkpoints for these models trained on standard datasets can be used immediately, use speech_to_text.py script in the examples directory.\nIn addition, models for ASR sub-tasks such as speech classification are also provided; for example MatchboxNet trained on the Google Speech Commands Dataset is available in speech_to_label.py. Voice Activity Detection is also supported with the same script, by simply changing the config file passed to the script!\nNeMo also supports training Speech Recognition models with Byte Pair/Word Piece encoding of the corpus, via the speech_to_text_bpe.py example; these models are still under development.\nIn order to simply perform evaluation on a dataset using these models, use the speech_to_text_infer.py example, which shows how to compute WER over the dataset.\n\n## Usage\n\nYou can instantiate all these models automatically directly from NGC. To do so, start your script with:\n```\nimport nemo\nimport nemo.collections.asr as nemo_asr\n```\nThen chose what type of model you would like to instantiate. See table below for the list of model base classes. Then use ```base_class.from_pretrained(...)``` method.\nFor example:\n```\nquartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name=\"QuartzNet15x5Base-En\")\n```\n\nNote that you can also list all available models using API by calling ```base_class.list_available_models(...)``` method.\n\nYou can also download all models' \".nemo\" files in the \"File Browser\" tab and then instantiate those models with ```base_class.restore_from(PATH_TO_DOTNEMO_FILE)``` method. In this case, make sure you are matching NeMo and models' versions.\n\n\nHere is a list of currently available models together with their base classes and short descriptions.\n\n| Model name | Model Base Class | Description |\n|-----------------------------------|---------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| QuartzNet15x5Base-En | ```EncDecCTCModel``` | QuartzNet15x5 model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1 for 600 epochs. The model achieves a WER of 3.79% on LibriSpeech dev-clean, and a WER of 10.05% on dev-other. |\n| QuartzNet15x5Base-Zh | ```EncDecCTCModel``` | QuartzNet15x5 model trained on ai-shell2 Mandarin Chinese dataset. |\n| QuartzNet5x5LS-En | ```EncDecCTCModel``` | QuartzNet5x5 model trained on LibriSpeech dataset only. The model achieves a WER of 5.37% on LibriSpeech dev-clean, and a WER of 15.69% on dev-other. |\n| QuartzNet15x5NR-En | ```EncDecCTCModel``` | Quartznet15x5 model trained for presence of noise. The base model QuartzNet15x5Base-En was finetuned with RIR and noise augmentation to make it more robust to noise. This model should be preferred for noisy speech transcription. This model achieves a WER of 3.96% on LibriSpeech dev-clean and a WER of 10.14% on dev-other. |\n| Jasper10x5Dr-En | ```EncDecCTCModel``` | JasperNet10x5Dr model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1. The model achieves a WER of 3.37% on LibriSpeech dev-clean, 9.81% on dev-other. |\n| ContextNet-192-WPE-1024-8x-Stride | ```EncDecCTCModelBPE``` | ContextNet initial implementation model trained on the Librispeech corpus and achieves a WER of 10.09% on test-other and 10.11% on dev-other. |\n| MatchboxNet-3x1x64-v1 | ```EncDecClassificationModel``` | MatchboxNet model trained on Google Speech Commands dataset (v1, 30 classes) which obtains 97.32% accuracy on test set. |\n| MatchboxNet-3x2x64-v1 | ```EncDecClassificationModel``` | MatchboxNet model trained on Google Speech Commands dataset (v1, 30 classes) which obtains 97.68% accuracy on test set. |\n| MatchboxNet-3x1x64-v2 | ```EncDecClassificationModel``` | MatchboxNet model trained on Google Speech Commands dataset (v2, 35 classes) which obtains 97.12% accuracy on test set. |\n| MatchboxNet-3x1x64-v2 | ```EncDecClassificationModel``` | MatchboxNet model trained on Google Speech Commands dataset (v2, 30 classes) which obtains 97.29% accuracy on test set. |\n| MatchboxNet-3x1x64-v2-subset-task | ```EncDecClassificationModel``` | MatchboxNet model trained on Google Speech Commands dataset (v2, 10+2 classes) which obtains 98.2% accuracy on test set. |\n| MatchboxNet-3x2x64-v2-subset-task | ```EncDecClassificationModel``` | MatchboxNet model trained on Google Speech Commands dataset (v2, 10+2 classes) which obtains 98.4% accuracy on test set. |\n| MatchboxNet-VAD-3x2 | ```EncDecClassificationModel``` | Voice Activity Detection MatchboxNet model trained on google speech command (v2) and freesound background data, which obtains 0.992 accuracy on testset from same source and 0.852 TPR for FPR=0.315 on testset (ALL) of AVA movie data |\n| SpeakerNet_recognition | ```EncDecSpeakerLabelModel``` | SpeakerNet_recognition model trained end-to-end for speaker recognition purposes with cross_entropy loss. It was trained on voxceleb 1, voxceleb 2 dev datasets and augmented with musan music and noise. Speaker Recognition model achieves 2.65% EER on voxceleb-O cleaned trial file\" |\n| SpeakerNet_verification | ```EncDecSpeakerLabelModel``` | SpeakerNet_verification model trained end-to-end for speaker verification purposes with arcface angular softmax loss. It was trained on voxceleb 1, voxceleb 2 dev datasets and augmented with musan music and noise. Speaker Verification model achieves 2.12% EER on voxceleb-O cleaned trial file |","displayName","NeMo Speech Models",["D",1680638126364],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/models/nemospeechmodels","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[240,241,242],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_209":254,"_244":254,"_245":255,"_247":248},{"_209":250,"_244":251,"_245":252,"_247":248},{"_209":243,"_244":243,"_245":246,"_247":248},"Speech","value","filter",{"_249":248,"_244":243},"category","label","field","Conversational AI","soln_conversational_ai",{"_249":253,"_244":251},"technology","ASR",{"_249":248,"_244":254},{"_120":1233,"_122":1234},"ngcApps",[1186],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1167":1168,"_1169":1170,"_1171":1172,"_1173":1174,"_1175":267,"_151":293,"_1176":1177,"_1178":59,"_1179":99,"_1180":99,"_1181":1182,"_1183":99,"_1184":1185},"amplitude",{"_1164":1165,"_806":-7,"_1166":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1158":1159,"_1160":1161,"_1162":1163},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787817309095,"1.12.0","defaultSearchSuggestions",{"_1046":180,"_1047":1048,"_1049":1050,"_1051":1052},"announcements",[],"consentState",{"_331":1036,"_1037":99,"_1038":34,"_1039":34,"_1040":1041},"consentConfig",{"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332},"showContactAdmin",1787817309096,[],[],[307,308,309,310,311,312],{"_48":62,"_50":73,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":67,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":115,"_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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These cookies may store a unique ID so that our system will remember you when you return. 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