Model Overview

Conformer-Transducer model which was pre-trained in a self supervised manner [11] and then finetuned on Spanish labelled speech data. The model was pretrained on a subset of the unlabeled Voxpopuli [4] dataset. Only the English, German, Spanish and French unlabelled subsets were used, which amounted to 100,000 hours. Then the model was finetuned 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]. This amounted to 1,340 hours of Spanish training data.

The model utilizes a Google SentencePiece [7] tokenizer with vocabulary size 128, and transcribes text in lower case Spanish alphabet characters.

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 [8] was used for training this model over several hundred epochs on multiple GPUs.

Datasets

While pre-training this model (in a self-supervised manner), we used the following datasets:

Voxpopuli unlabelled speech data for English, German, Spanish and French - 100,000 hours

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

Tokenizer Construction

The tokenizer for this model was built using the text that was in the training set.

We build a Google Sentencepiece Tokenizer with the following script:

" \\\n --vocab_size=128 \\\n --tokenizer="spe" \\\n --spe_type="unigram" \\\n --spe_character_coverage=1.0 \\\n --no_lower_case \\\n --log\n\">

python [NEMO_GIT_FOLDER]/scripts/process_asr_text_tokenizer.py \\\n  --manifest=\"train_manifest.json\" \\\n  --data_root=\"<OUTPUT DIRECTORY FOR TOKENIZER>\" \\\n  --vocab_size=128 \\\n  --tokenizer=\"spe\" \\\n  --spe_type=\"unigram\" \\\n  --spe_character_coverage=1.0 \\\n  --no_lower_case \\\n  --log\n

Performance

The performance of Automatic Speech Recognition models is measuring using Word Error Rate.

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

4.6 % WER on MCV7.0 dev set
2.7 % WER on MLS dev set
4.7 % WER on Voxpopuli dev set
14.7 % WER on Fisher dev set (manually created)

\n\n

5.2 % WER on MCV7.0 test set
3.2 % WER on MLS test set
6.0 % WER on Voxpopuli test set
14.8 % WER on Fisher test set (manually created)

\n\n

17.2 % WER on Spanish CALLHOME train set [9,10] (model was not trained on this data)
18.0 % WER on Spanish CALLHOME devtest set [9,10] (model was not trained on this data)
19.4 % WER on Spanish CALLHOME evltest set [9,10] (model was not trained on this data)

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.EncDecCTCModelBPE.from_pretrained(model_name=\"stt_es_conformer_transducer_large\")\n

Transcribing text with this model

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

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] 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] Google Sentencepiece Tokenizer

[8] NVIDIA NeMo Toolkit

[9] CALLHOME Spanish Transcripts

[10] CALLHOME Spanish Speech

[11] Self-Supervised pre-training for ASR in NeMo

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 was pretrained on a subset of the unlabeled Voxpopuli [4] dataset. Only the English, German, Spanish and French unlabelled subsets were used, which amounted to 100,000 hours. Then the model was finetuned 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]. This amounted to 1,340 hours of Spanish training data.\n\nThe model utilizes a Google SentencePiece [7] tokenizer with vocabulary size 128, and transcribes text in lower case Spanish alphabet characters.\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\nThe NeMo toolkit [8] was used for training this model over several hundred epochs on multiple GPUs.\n\n## Datasets\n\nWhile pre-training this model (in a self-supervised manner), we used the following datasets:\n* Voxpopuli unlabelled speech data for English, German, Spanish and French - 100,000 hours\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\n## Tokenizer Construction\nThe tokenizer for this model was built using the text that was in the training set.\n\nWe build a Google Sentencepiece Tokenizer with the following script:\n```bash\npython [NEMO_GIT_FOLDER]/scripts/process_asr_text_tokenizer.py \\\n --manifest=\"train_manifest.json\" \\\n --data_root=\"\" \\\n --vocab_size=128 \\\n --tokenizer=\"spe\" \\\n --spe_type=\"unigram\" \\\n --spe_character_coverage=1.0 \\\n --no_lower_case \\\n --log\n ```\n\n# Performance\nThe performance of Automatic Speech Recognition models is measuring using Word Error Rate. \n\nThe model obtains the following greedy scores on the following evaluation datasets:\n\n* 4.6 % WER on MCV7.0 dev set\n* 2.7 % WER on MLS dev set\n* 4.7 % WER on Voxpopuli dev set\n* 14.7 % WER on Fisher dev set (manually created)\n\n* 5.2 % WER on MCV7.0 test set\n* 3.2 % WER on MLS test set\n* 6.0 % WER on Voxpopuli test set\n* 14.8 % WER on Fisher test set (manually created)\n\n* 17.2 % WER on Spanish CALLHOME train set [9,10] (model was not trained on this data)\n* 18.0 % WER on Spanish CALLHOME devtest set [9,10] (model was not trained on this data)\n* 19.4 % WER on Spanish CALLHOME evltest set [9,10] (model was not trained on this data)\n\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.EncDecCTCModelBPE.from_pretrained(model_name=\"stt_es_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_es_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.\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[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] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)\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[11] [Self-Supervised pre-training for ASR in NeMo](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Self_Supervised_Pre_Training.ipynb)\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 Es Conformer Transducer Large",["D",1680637884770],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/stt_es_conformer_transducer_large","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[234,235,236,237],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_202":251,"_239":252,"_240":253,"_242":243},{"_202":249,"_239":249,"_240":250,"_242":243},{"_202":245,"_239":246,"_240":247,"_242":243},{"_202":238,"_239":238,"_240":241,"_242":243},"Deep-Learning","value","filter",{"_244":243,"_239":238},"category","label","field","Conversational AI","soln_conversational_ai",{"_244":248,"_239":246},"technology","Conformer Transducer",{"_244":243,"_239":249},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_244":254,"_239":252},"application",{"_106":1431,"_108":1432},"ngcApps",[1384],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1366":1367,"_1368":1369,"_1370":1371,"_1372":1373,"_1374":266,"_133":290,"_1375":1376,"_1364":55,"_1377":85,"_1378":85,"_1379":1380,"_1381":85,"_1382":1383},"amplitude",{"_1362":1363,"_1364":55,"_964":-7,"_1365":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","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,"1.4.0","defaultSearchSuggestions",{"_1239":163,"_1240":1241,"_1242":1243,"_1244":1245},"announcements",[],"consentState",{"_329":1229,"_1230":30,"_1231":30,"_1232":85,"_1233":1234},"consentConfig",{"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330},"showContactAdmin",1782951923600,[],[],[304,305,306,307,308,309],{"_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":310,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},3,"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. 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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. 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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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