Model Overview

This model performs joint intent classification and slot filling, directly from audio input. The model treats the problem as an audio-to-text problem, where the output text is the flattened string representation of the semantics annotation. The model is trained on the SLURP dataset [1].

Model Architecture

The model is has an encoder-decoder architecture, where the encoder is a Conformer-Large model [2], and the decoder is a three-layer Transformer Decoder [3]. We use the Conformer encoder pretrained on NeMo ASR-Set (details here), while the decoder is trained from scratch. A start-of-sentence (BOS) and an end-of-sentence (EOS) tokens are added to each sentence. The model is trained end-to-end by minimizing the negative log-likelihood loss with teacher forcing. During inference, the prediction is generated by beam search, where a BOS token is used to trigger the generation process.

Training

The NeMo toolkit [4] was used for training the models for around 100 epochs. These model are trained with this example script and this base config.

The tokenizers for these models were built using the semantics annotations of the train set with this script. We use a vocabulary size of 58, including the BOS, EOS and padding tokens.

Datasets

The model is trained on the combined real and synthetic training sets of the SLURP dataset.

Performance

\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\">

				Intent (Scenario_Action)		Entity			SLURP Metrics
Version	Model	Params (M)	Pretrained	Accuracy	Precision	Recall	F1	Precsion	Recall	F1
1.13.0	Conformer-Transformer-Large	127	NeMo ASR-Set 3.0	90.14	78.95	74.93	76.89	84.31	80.33	82.27
Baseline	Conformer-Transformer-Large	127	None	72.56	43.19	43.5	43.34	53.59	53.92	53.76

Note: during inference, we use beam size of 32, and a temperature of 1.25.

How to Use this Model

The model is available for use in the NeMo toolkit [3], and can be used on another dataset with the same annotation format.

Automatically load the model from NGC

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

Predict intents and slots with this model

" \\\n sequence_generator.type="<'beam' OR 'greedy' FOR BEAM/GREEDY SEARCH>" \\\n sequence_generator.beam_size="" \\\n sequence_generator.temperature=""\n\">

python [NEMO_GIT_FOLDER]/examples/slu/speech_intent_slot/eval_utils/inference.py \\\n pretrained_name=\"slu_conformer_transformer_slurp\" \\\n audio_dir=\"<DIRECTORY CONTAINING AUDIO FILES>\" \\\n sequence_generator.type=\"<'beam' OR 'greedy' FOR BEAM/GREEDY SEARCH>\" \\\n sequence_generator.beam_size=\"<SIZE OF BEAM>\" \\\n sequence_generator.temperature=\"<TEMPERATURE FOR BEAM SEARCH>\"\n

Input

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

Output

This model provides the intent and slot annotaions as a string for a given audio sample.

Limitations

Since this model was trained on only the SLURP dataset [1], the performance of this model might degrade on other datasets.

References

[1] SLURP: A Spoken Language Understanding Resource Package

[2] Conformer: Convolution-augmented Transformer for Speech Recognition

[3] Attention Is All You Need

[4] 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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The model treats the problem as an audio-to-text problem, where the output text is the flattened string representation of the semantics annotation.","## Model Overview\n\nThis model performs joint intent classification and slot filling, directly from audio input. The model treats the problem as an audio-to-text problem, where the output text is the flattened string representation of the semantics annotation. The model is trained on the SLURP dataset [1].\n\n## Model Architecture\n\nThe model is has an encoder-decoder architecture, where the encoder is a Conformer-Large model [2], and the decoder is a three-layer Transformer Decoder [3]. We use the Conformer encoder pretrained on NeMo ASR-Set (details [here](https://ngc.nvidia.com/models/nvidia:nemo:stt_en_conformer_ctc_large)), while the decoder is trained from scratch. A start-of-sentence (BOS) and an end-of-sentence (EOS) tokens are added to each sentence. The model is trained end-to-end by minimizing the negative log-likelihood loss with teacher forcing. During inference, the prediction is generated by beam search, where a BOS token is used to trigger the generation process.\n\n## Training\n\nThe NeMo toolkit [4] was used for training the models for around 100 epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/slu/speech_intent_slot/run_slurp_train.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/slu/speech_intent_slot/configs/conformer_transformer_large_bpe.yaml).\n\nThe tokenizers for these models were built using the semantics annotations of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). We use a vocabulary size of 58, including the BOS, EOS and padding tokens.\n\n\n### Datasets\n\nThe model is trained on the combined real and synthetic training sets of the SLURP dataset.\n\n\n## Performance\n\n| | | | | **Intent (Scenario_Action)** | | **Entity** | | | **SLURP Metrics** | |\n|-------|--------------------------------------------------|----------------|--------------------------|------------------------------|---------------|------------|--------|--------------|-------------------|---------------------|\n|**Version**| **Model** | **Params (M)** | **Pretrained** | **Accuracy** | **Precision** | **Recall** | **F1** | **Precsion** | **Recall** | **F1** |\n|1.13.0| Conformer-Transformer-Large | 127 | NeMo ASR-Set 3.0 | 90.14 | 78.95 | 74.93 | 76.89 | 84.31 | 80.33 | 82.27 |\n|Baseline| Conformer-Transformer-Large | 127 | None | 72.56 | 43.19 | 43.5 | 43.34 | 53.59 | 53.92 | 53.76 |\n\nNote: during inference, we use beam size of 32, and a temperature of 1.25.\n\n\n## How to Use this Model\n\nThe model is available for use in the NeMo toolkit [3], and can be used on another dataset with the same annotation format.\n\n### Automatically load the model from NGC\n\n```python\nimport nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.SLUIntentSlotBPEModel.from_pretrained(model_name=\"slu_conformer_transformer_large_slurp\")\n```\n\n### Predict intents and slots with this model\n\n```shell\npython [NEMO_GIT_FOLDER]/examples/slu/speech_intent_slot/eval_utils/inference.py \\\n pretrained_name=\"slu_conformer_transformer_slurp\" \\\n audio_dir=\"\" \\\n sequence_generator.type=\"<'beam' OR 'greedy' FOR BEAM/GREEDY SEARCH>\" \\\n sequence_generator.beam_size=\"\" \\\n sequence_generator.temperature=\"\"\n```\n\n### Input\n\nThis model accepts 16000 Hz Mono-channel Audio (wav files) as input.\n\n### Output\n\nThis model provides the intent and slot annotaions as a string for a given audio sample.\n\n## Limitations\n\nSince this model was trained on only the SLURP dataset [1], the performance of this model might degrade on other datasets.\n\n\n## References\n\n\n[1] [SLURP: A Spoken Language Understanding Resource Package](https://arxiv.org/abs/2011.13205)\n\n[2] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)\n\n[3] [Attention Is All You Need](https://arxiv.org/abs/1706.03762?context=cs)\n\n[4] [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","SLU Conformer-Transformer-Large SLURP",["D",1680636477893],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/slu_conformer_transformer_large_slurp","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[238,239,240,241],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":254,"_243":255,"_244":256,"_246":247},{"_206":252,"_243":252,"_244":253,"_246":247},{"_206":249,"_243":249,"_244":250,"_246":247},{"_206":242,"_243":242,"_244":245,"_246":247},"PytorchLightning","value","filter",{"_248":247,"_243":242},"category","label","field","PyTorch",{"_248":251,"_243":249},"framework","NeMo",{"_248":251,"_243":252},"Conversational AI","soln_conversational_ai",{"_248":257,"_243":255},"technology",{"_107":1431,"_109":1432},"ngcApps",[1384],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1365":1366,"_1367":1368,"_1369":1370,"_1371":1372,"_1373":269,"_138":293,"_1374":1375,"_1376":56,"_1377":86,"_1378":86,"_1379":1380,"_1381":86,"_1382":1383},"amplitude",{"_1362":1363,"_965":-7,"_1364":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1356":1357,"_1358":1359,"_1360":1361},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1240":168,"_1241":1242,"_1243":1244,"_1245":1246},"announcements",[],"consentState",{"_330":1230,"_1231":31,"_1232":31,"_1233":86,"_1234":1235},"consentConfig",{"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331},"showContactAdmin",1783730238999,[],[],[307,308,309,310,311],{"_45":60,"_47":70,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":68,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":59,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_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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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","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. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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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.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","ApplicationGatewayAffinityCORS","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure all requests from a user during the session are sent to the same server.","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.Supports Amplitude product analytics.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","AMP_TLDTEST","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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