Model Overview

Conformer-Transducer Large model, which was initially pre-trained on the English NeMo ASRSet with around 16000 hours of english speech and then finetuned on a combination of Spanish and English labeled speech data.

It can transcribe audio samples into English or Spanish. The language is detected automatically.

We started with the english Conformer-Transducer large model and finetuned it using labelled Spanish portions of the Mozilla CommonVoice (MCV7.0) [2], Multilingual LibriSpeech (MLS) [3], and Voxpopuli [4] training sets, as well as a subset of the Spanish Fisher dataset [5, 6], plus English Librispeech [7]. This amounted to 1,340 hours of Spanish training data and 960 hours of English data.

Model Architecture

Conformer-Transducer model is an autoregressive variant of Conformer model [1] for Automatic Speech Recognition. You may find more info on the detail of this model here: Conformer-Transducer Model.

Training

The NeMo toolkit [8] was used for training this model over several hundred epochs on multiple GPUs using bfloat16.

The English and Spanish tokenizers for these models were built separately using the English and Spanish text transcripts respectively\nof the train set with this script, idential to the\nprocedure for large mono-lingual models (e.g. 1024 tokens). Both tokenizers were then used during training via the AggregateTokenizer construct.\nThe total number of tokens in the model is 2048 (1024 for english and 1024 for spanish).

Datasets

While finetuning this model, we used the following datasets:

Mozilla Common Voice 7.0 (Spanish) - 289 hours of training data after data cleaning
Multilingual LibriSpeech (Spanish) - 801 hours of training data after data cleaning
Voxpopuli transcribed subset (Spanish) - 110 hours of training data after data cleaning
Fisher dataset (Spanish) - 140 hours of training data after data cleaning
Librispeech (English) - 960 hours of training data

Performance

The performance of Automatic Speech Recognition models is measured using Word Error Rate (WER).

The model obtains the following greedy scores on the following evaluation datasets:

5.0 % WER on MCV7.0 dev set (es)
3.3 % WER on MLS dev set (es)
5.3 % WER on Voxpopuli dev set (es)
16.2 % WER on Fisher dev set (es)

\n\n

2.0 % WER on LibriSpeech dev-clean (en)
2.2 % WER on LibriSpeech test-clean (en)
4.6 % WER on LibriSpeech dev-other (en)
4.6 % WER on LibriSpeech test-other (en)

\n\n

The model was not trained on the above datasets.

How to Use this Model

The model is available for use in the NeMo toolkit [5], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically load the model from NGC

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

Transcribing text with this model

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n  pretrained_name=\"stt_enes_conformer_transducer_large\" \\\n  audio_dir=\"\"\n

Input

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

Output

This model provides transcribed speech as a string for a given audio sample. The output string may contain English or Spanish characters, depending on the detected language in the audio sample.

Limitations

Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.

Finally, the model may not peform well on true \"code-switching\" samples, where each sample contains more than one language. This is because the model was trained only on samples where each sample had only one language. We will address this in the future releases.

References

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

[2] Mozilla CommonVoice (MCV7.0)

[3] Multilingual LibriSpeech (MLS)

[4] VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation

[5] Fisher Spanish - Transcripts

[6] Fisher Spanish Speech

[7] LibriSpeech ASR Corpus

[8] NVIDIA NeMo Toolkit

[9] CALLHOME Spanish Transcripts

[10] CALLHOME Spanish Speech

License

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 language is detected automatically.\n\nWe started with the [english Conformer-Transducer large model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_large) and finetuned it using labelled Spanish portions of the Mozilla CommonVoice (MCV7.0) [2], Multilingual LibriSpeech (MLS) [3], and Voxpopuli [4] training sets, as well as a subset of the Spanish Fisher dataset [5, 6], plus English Librispeech [7]. This amounted to 1,340 hours of Spanish training data and 960 hours of English data.\n\n# Model Architecture\n\nConformer-Transducer model is an autoregressive variant of Conformer model [1] for Automatic Speech Recognition. You may find more info on the detail of this model here: [Conformer-Transducer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html). \n\n# Training\nThe NeMo toolkit [8] was used for training this model over several hundred epochs on multiple GPUs using bfloat16.\n\nThe English and Spanish tokenizers for these models were built separately using the English and Spanish text transcripts respectively\nof the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py), idential to the\nprocedure for large mono-lingual models (e.g. 1024 tokens). Both tokenizers were then used during training via the AggregateTokenizer construct. \nThe total number of tokens in the model is 2048 (1024 for english and 1024 for spanish).\n\n\n## Datasets\n\nWhile finetuning this model, we used the following datasets:\n* Mozilla Common Voice 7.0 (Spanish) - 289 hours of training data after data cleaning\n* Multilingual LibriSpeech (Spanish) - 801 hours of training data after data cleaning\n* Voxpopuli transcribed subset (Spanish) - 110 hours of training data after data cleaning\n* Fisher dataset (Spanish) - 140 hours of training data after data cleaning\n* Librispeech (English) - 960 hours of training data\n\n# Performance\nThe performance of Automatic Speech Recognition models is measured using Word Error Rate (WER). \n\nThe model obtains the following greedy scores on the following evaluation datasets:\n\n* 5.0 % WER on MCV7.0 dev set (es)\n* 3.3 % WER on MLS dev set (es)\n* 5.3 % WER on Voxpopuli dev set (es)\n* 16.2 % WER on Fisher dev set (es)\n\n* 2.0 % WER on LibriSpeech dev-clean (en)\n* 2.2 % WER on LibriSpeech test-clean (en)\n* 4.6 % WER on LibriSpeech dev-other (en)\n* 4.6 % WER on LibriSpeech test-other (en)\n\n\nThe model was not trained on the above datasets.\n\n# How to Use this Model\n\nThe model is available for use in the NeMo toolkit [5], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.\n\n## Automatically load the model from NGC\n```python\nimport nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name=\"stt_enes_conformer_transducer_large\")\n```\n\n## Transcribing text with this model\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_enes_conformer_transducer_large\" \\\n audio_dir=\"\"\n```\n\n## Input\n\nThis model accepts 16000 Hz Mono-channel Audio (wav files) as input. \n\n## Output\n\nThis model provides transcribed speech as a string for a given audio sample. The output string may contain English or Spanish characters, depending on the detected language in the audio sample.\n\n# Limitations\n\nSince this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. \n\nFinally, the model may not peform well on true \"code-switching\" samples, where each sample contains more than one language. This is because the model was trained only on samples where each sample had only one language. We will address this in the future releases.\n\n# References\n[1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)\n\n[2] [Mozilla CommonVoice (MCV7.0)](https://commonvoice.mozilla.org/en)\n\n[3] [Multilingual LibriSpeech (MLS)](https://arxiv.org/abs/2012.03411)\n\n[4] [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)\n\n[5] [Fisher Spanish - Transcripts](https://catalog.ldc.upenn.edu/LDC2010T04)\n\n[6] [Fisher Spanish Speech](https://catalog.ldc.upenn.edu/LDC2010S01)\n\n[7] [LibriSpeech ASR Corpus](https://www.openslr.org/12)\n\n[8] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n[9] [CALLHOME Spanish Transcripts](https://catalog.ldc.upenn.edu/LDC96T17)\n\n[10] [CALLHOME Spanish Speech](https://catalog.ldc.upenn.edu/LDC96S35)\n\n# License\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","STT En Es Conformer Transducer Large",["D",1680634751961],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/stt_enes_conformer_transducer_large","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[237,238,239,240,241],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_205":261,"_243":262,"_244":263,"_246":247},{"_205":257,"_243":258,"_244":259,"_246":247},{"_205":254,"_243":255,"_244":256,"_246":247},{"_205":250,"_243":251,"_244":252,"_246":247},{"_205":242,"_243":242,"_244":245,"_246":247},"PyTorch","value","filter",{"_248":249,"_243":242},"category","label","field","framework","DL","soln_dl",{"_248":253,"_243":251},"technology","Conversational AI","soln_conversational_ai",{"_248":253,"_243":255},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_248":260,"_243":258},"application","AI","soln_ai",{"_248":253,"_243":262},{"_106":1436,"_108":1437},"ngcApps",[1389],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1370":1371,"_1372":1373,"_1374":1375,"_1376":1377,"_1378":275,"_137":299,"_1379":1380,"_1381":56,"_1382":85,"_1383":85,"_1384":1385,"_1386":85,"_1387":1388},"amplitude",{"_1367":1368,"_970":-7,"_1369":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1361":1362,"_1363":1364,"_1365":1366},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1245":167,"_1246":1247,"_1248":1249,"_1250":1251},"announcements",[],"consentState",{"_336":1235,"_1236":85,"_1237":31,"_1238":31,"_1239":1240},"consentConfig",{"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337},"showContactAdmin",1783815279260,[],[],[313,314,315,316,317],{"_45":60,"_47":70,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":101,"_51":-5,"_53":-5,"_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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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. 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