Model Overview

Citrinet-1024 [1] model with kernel scaling factor (gamma) of 25% which was fine-tuned from English language to Spanish. We took a model trained on over 7,000 hours of English speech. Then we changed the model's decoder to output characters from the Spanish alphabet and fine-tuned this model using 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.

It utilizes a Google SentencePiece [7] tokenizer with vocabulary size 1024, and transcribes text in lower case Spanish alphabet characters.

Model Architecture

Citrinet is a deep residual convolutional neural network architecture that is optimized for Automatic Speech Recognition tasks. There are many variants of the Citrinet family of models, which are further discussed in the paper [1].

Training

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

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

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=1024 \\\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=1024 \\\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:

6.1 % WER on MCV7.0 dev set
3.5 % WER on MLS dev set
5.6 % WER on Voxpopuli dev set
15.8 % WER on Fisher dev set (manually created)

\n\n

6.8 % WER on MCV7.0 test set
4.1 % WER on MLS test set
7.0 % WER on Voxpopuli test set
15.9 % WER on Fisher test set (manually created)

\n\n

19.1 % WER on Spanish CALLHOME train set [9,10] (model was not trained on this data)
19.9 % WER on Spanish CALLHOME devtest set [9,10] (model was not trained on this data)
21.3 % WER on Spanish CALLHOME evltest set [9,10] (model was not trained on this data)

We also evaluated the model performance with beam search with a 4-gram KenLM model (included with this NGC model).\nThe KenLM model was trained on the transcriptions of the Spanish speech data used to finetune the ASR model, and also a dataset of Spanish news text (about 50 million sentences) from the WMT competition's News Crawl corpus [11].

The following results were obtained with beam search with the 4-gram KenLM model, using hyperparameters alpha = 0.5, beta = 1.3, beam width = 128.

5.0 % WER on MCV7.0 dev set
3.2 % WER on MLS dev set
5.2 % WER on Voxpopuli dev set
15.4 % WER on Fisher dev set (manually created)

\n\n

5.7 % WER on MCV7.0 test set
3.6 % WER on MLS test set
6.4 % WER on Voxpopuli test set
15.5 % WER on Fisher test set (manually created)

\n\n

18.1 % WER on Spanish CALLHOME train set [9,10] (model was not trained on this data)
19.3 % WER on Spanish CALLHOME devtest set [9,10] (model was not trained on this data)
20.3 % 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_citrinet_1024_gamma_0_25\")\n

Transcribing text with this model

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n  pretrained_name=\"stt_es_citrinet_1024_gamma_0_25\" \\\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] Majumdar, Somshubra, et al. \"Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition.\" arXiv preprint arXiv:2104.01721 (2021). link

[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] WMT competition text datasets

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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We took a [model](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_citrinet_1024_gamma_0_25) trained on over 7,000 hours of English speech. Then we changed the model's decoder to output characters from the Spanish alphabet and fine-tuned this model using 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\nIt utilizes a Google SentencePiece [7] tokenizer with vocabulary size 1024, and transcribes text in lower case Spanish alphabet characters.\n\n# Model Architecture\n\nCitrinet is a deep residual convolutional neural network architecture that is optimized for Automatic Speech Recognition tasks. There are many variants of the Citrinet family of models, which are further discussed in the paper [1].\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 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=1024 \\\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* 6.1 % WER on MCV7.0 dev set\n* 3.5 % WER on MLS dev set\n* 5.6 % WER on Voxpopuli dev set\n* 15.8 % WER on Fisher dev set (manually created)\n\n* 6.8 % WER on MCV7.0 test set\n* 4.1 % WER on MLS test set\n* 7.0 % WER on Voxpopuli test set\n* 15.9 % WER on Fisher test set (manually created)\n\n* 19.1 % WER on Spanish CALLHOME train set [9,10] (model was not trained on this data)\n* 19.9 % WER on Spanish CALLHOME devtest set [9,10] (model was not trained on this data)\n* 21.3 % WER on Spanish CALLHOME evltest set [9,10] (model was not trained on this data)\n\nWe also evaluated the model performance with beam search with a 4-gram KenLM model (included with this NGC model). \nThe KenLM model was trained on the transcriptions of the Spanish speech data used to finetune the ASR model, and also a dataset of Spanish news text (about 50 million sentences) from the WMT competition's News Crawl corpus [11].\n\nThe following results were obtained with beam search with the 4-gram KenLM model, using hyperparameters alpha = 0.5, beta = 1.3, beam width = 128.\n\n* 5.0 % WER on MCV7.0 dev set\n* 3.2 % WER on MLS dev set\n* 5.2 % WER on Voxpopuli dev set\n* 15.4 % WER on Fisher dev set (manually created)\n\n* 5.7 % WER on MCV7.0 test set\n* 3.6 % WER on MLS test set\n* 6.4 % WER on Voxpopuli test set\n* 15.5 % WER on Fisher test set (manually created)\n\n* 18.1 % WER on Spanish CALLHOME train set [9,10] (model was not trained on this data)\n* 19.3 % WER on Spanish CALLHOME devtest set [9,10] (model was not trained on this data)\n* 20.3 % 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_citrinet_1024_gamma_0_25\")\n```\n\n## Transcribing text with this model\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_es_citrinet_1024_gamma_0_25\" \\\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] Majumdar, Somshubra, et al. \"Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition.\" arXiv preprint arXiv:2104.01721 (2021). [link](https://arxiv.org/abs/2104.01721)\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] [WMT competition text datasets](https://www.statmt.org/wmt20/translation-task.html)\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 Citrinet 1024 Gamma 0.25",["D",1680635627584],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/stt_es_citrinet_1024_gamma_0_25","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[246,247,248,249],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_214":264,"_251":265,"_253":266,"_255":256},{"_214":262,"_251":262,"_253":263,"_255":256},{"_214":259,"_251":260,"_253":261,"_255":256},{"_214":250,"_251":252,"_253":254,"_255":256},"DL","value","soln_dl","filter",{"_257":258,"_251":252},"category","label","field","technology","Conversational AI","soln_conversational_ai",{"_257":258,"_251":260},"Citrinet",{"_257":256,"_251":262},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_257":267,"_251":265},"application",{"_114":1442,"_116":1443},"ngcApps",[1395],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1376":1377,"_1378":1379,"_1380":1381,"_1382":1383,"_1384":279,"_145":303,"_1385":1386,"_1387":57,"_1388":93,"_1389":93,"_1390":1391,"_1392":93,"_1393":1394},"amplitude",{"_1373":1374,"_975":-7,"_1375":32},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1367":1368,"_1369":1370,"_1371":1372},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1250":175,"_1251":1252,"_1253":1254,"_1255":1256},"announcements",[],"consentState",{"_340":1240,"_1241":93,"_1242":32,"_1243":32,"_1244":1245},"consentConfig",{"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341},"showContactAdmin",1784222895293,[],[],[317,318,319,320,321],{"_46":60,"_48":71,"_50":109,"_52":-5,"_54":-5,"_56":57},{"_46":60,"_48":69,"_50":109,"_52":-5,"_54":-5,"_56":57},{"_46":60,"_48":61,"_50":109,"_52":-5,"_54":-5,"_56":57},{"_46":58,"_48":59,"_50":109,"_52":-5,"_54":-5,"_56":57},{"_46":47,"_48":49,"_50":109,"_52":-5,"_54":-5,"_56":57},"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. 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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.","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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Name	Size	Updated	Actions
kenlm.4gram	1.03 GB	March 1, 2025 UTC	curl wget Download
stt_es_citrinet_1024_gamma_0_25.nemo	453.35 MB	March 1, 2025 UTC	curl wget Download