Model Overview

This collection contains xlarge size versions of Conformer-Transducer (around 0.6B parameters) trained on NeMo ASRSet with around 24000 hours of english speech. The model transcribes speech in lower case english alphabet along with spaces and apostrophes.

Model Architecture

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

Training

The NeMo toolkit [3] was used for training the models. These model are trained with this example script and this base config.

The tokenizers for these models were built using the text transcripts of the train set with this script.

Datasets

All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several thousand hours of English speech:

Librispeech 960 hours of English speech
Fisher Corpus
Switchboard-1 Dataset
WSJ-0 and WSJ-1
National Speech Corpus (Part 1, Part 6)
VCTK
VoxPopuli (EN)
Europarl-ASR (EN)
Multilingual Librispeech (MLS EN) - 2,000 hrs subset
Mozilla Common Voice (v8.0)
People's Speech - 12,000 hrs subset

Note: older versions of the model may have trained on smaller set of datasets.

Performance

The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.

\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\">

Version	Tokenizer	Vocabulary Size	LS test-other	LS test-clean	WSJ Eval92	WSJ Dev93	NSC Part 1	MLS Test	MLS Dev	MCV Test 8.0	Train Dataset
1.8.0	SentencePiece Unigram	1024	3.18	1.70	1.40	2.20	6.30	6.02	5.26	-	NeMo ASRSET 2.0
1.10.0	SentencePiece Unigram	1024	3.01	1.62	1.17	2.05	5.70	5.32	4.59	6.46	NeMo ASRSET 3.0

How to Use this Model

The model is available for use in the NeMo toolkit [3], 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_en_conformer_transducer_xlarge\")\n

Transcribing text with this model

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

Input

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

Output

This model provides transcribed speech as a string for a given audio sample.

Limitations

Since this model was trained on publically 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.

References

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

[2] Google Sentencepiece Tokenizer

[3] 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 transcribes speech in lower case english alphabet along with spaces and apostrophes.\n\n## Model Architecture\n\nConformer-Transducer model is an autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses Transducer loss/decoding. 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\n\nThe NeMo toolkit [3] was used for training the models. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_transducer_bpe.yaml).\n\nThe tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).\n\n### Datasets\n\nAll the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several thousand hours of English speech:\n\n- Librispeech 960 hours of English speech\n- Fisher Corpus\n- Switchboard-1 Dataset\n- WSJ-0 and WSJ-1\n- National Speech Corpus (Part 1, Part 6)\n- VCTK\n- VoxPopuli (EN)\n- Europarl-ASR (EN)\n- Multilingual Librispeech (MLS EN) - 2,000 hrs subset\n- Mozilla Common Voice (v8.0)\n- People's Speech - 12,000 hrs subset\n\nNote: older versions of the model may have trained on smaller set of datasets.\n\n## Performance\n\nThe list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.\n\n| Version | Tokenizer | Vocabulary Size | LS test-other | LS test-clean | WSJ Eval92 | WSJ Dev93 | NSC Part 1 | MLS Test | MLS Dev | MCV Test 8.0 | Train Dataset |\n|---------|-----------------------|-----------------|---------------|---------------|------------|-----------|-----|-------|------|----|------|\n| 1.8.0 | SentencePiece Unigram | 1024 | 3.18 | 1.70 | 1.40 | 2.20 | 6.30 | 6.02 | 5.26 | - | NeMo ASRSET 2.0 |\n| 1.10.0 | SentencePiece Unigram | 1024 | 3.01 | 1.62 | 1.17 | 2.05 | 5.70 | 5.32 | 4.59 | 6.46 | NeMo ASRSET 3.0 |\n\n## How to Use this Model\n\nThe model is available for use in the NeMo toolkit [3], 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\n```python\nimport nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name=\"stt_en_conformer_transducer_xlarge\")\n```\n\n### Transcribing text with this model\n\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_en_conformer_transducer_xlarge\" \\\n audio_dir=\"PATH_TO_AUDIO_FILES\"\n```\n\n### Input\n\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.\n\n### Output\n\nThis model provides transcribed speech as a string for a given audio sample.\n\n## Limitations\n\nSince this model was trained on publically 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\n## References\n\n[1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)\n\n[2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)\n\n[3] [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","STT En Conformer-Transducer XLarge",["D",1680638489601],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/stt_en_conformer_transducer_xlarge","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes",2577971200,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239,240,241],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":253,"_243":254,"_245":255,"_247":248},{"_206":251,"_243":251,"_245":252,"_247":248},{"_206":242,"_243":244,"_245":246,"_247":248},"DL","value","soln_dl","filter",{"_249":250,"_243":244},"category","label","field","technology","Conformer-Transducer",{"_249":248,"_243":251},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_249":256,"_243":254},"application",{"_106":1432,"_108":1433},"ngcApps",[1385],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1367":1368,"_1369":1370,"_1371":1372,"_1373":1374,"_1375":268,"_134":292,"_1376":1377,"_1365":55,"_1378":85,"_1379":85,"_1380":1381,"_1382":85,"_1383":1384},"amplitude",{"_1363":1364,"_1365":55,"_965":-7,"_1366":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1357":1358,"_1359":1360,"_1361":1362},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1240":164,"_1241":1242,"_1243":1244,"_1245":1246},"announcements",[],"consentState",{"_331":1230,"_1231":30,"_1232":30,"_1233":85,"_1234":1235},"consentConfig",{"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332},"showContactAdmin",1782948420357,[],[],[306,307,308,309,310,311],{"_44":58,"_46":69,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":67,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":65,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":59,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":49,"_50":51,"_52":312,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},4,"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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This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. 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