Model Overview

Citrinet-512 model which has been trained on various Spanish corpus.

It utilizes a Google SentencePiece [1] tokenizer with vocabulary size 1024, and transcribes text in lower case Spanish alphabet 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 [2].

Training

We took an encoder from the English version of the CitriNet network 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 (MCV) [3] and Multilingual LibriSpeech (MLS) [4] training sets. The NeMo toolkit [5] was used for training this model over several hundred epochs on multiple GPUs.

While finetuning this model, we used the following datasets:

Mozilla Common Voice (Spanish) - 830 hours of training data after data cleaning
Multilingual LibriSpeech (Spanish) - 235 hours of training data after data cleaning

Datasets

While training this model, we used the following datasets:

Librispeech 960 hours of English speech (pre-training)
Fisher Corpus (pre-training)
Switchboard-1 Dataset (pre-training)
WSJ-0 and WSJ-1 (pre-training)
National Speech Corpus - 1 (pre-training)
Mozilla Common Voice (pre-training)
Mozilla Common Voice (Spanish) (fine-tuning)

Tokenizer Construction

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

We build a Google Sentencepiece Tokenizer [1] 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/tokenizers/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 scores on the following evaluation datasets -

9.1 % on MCV dev set
10.3 % on MCV test set
4.9 % on MLS dev set
5.2 % on MLS test set

Note that these scores above are not particularly indicative of the quality of transcriptions that models trained on ASR Set will achieve, but they are a useful proxy.

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\n\nasr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name=\"stt_es_citrinet_512\")\n

Transcribing text with this model

"\n\">

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n  pretrained_name=\"stt_es_citrinet_512\" \\\n  audio_dir=\"<DIRECTORY CONTAINING 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] Google Sentencepiece Tokenizer

[1] Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition

[3] Mozilla CommonVoice (MCV)

[4] Multilingual LibriSpeech (MLS)

[5] 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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There are many variants of the Citrinet family of models, which are further discussed in the paper [2].\n\n## Training\n\nWe took an encoder from the English version of the CitriNet network 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 (MCV) [3] and Multilingual LibriSpeech (MLS) [4] training sets. The NeMo toolkit [5] was used for training this model over several hundred epochs on multiple GPUs.\n\nWhile finetuning this model, we used the following datasets:\n\n- Mozilla Common Voice (Spanish) - 830 hours of training data after data cleaning\n- Multilingual LibriSpeech (Spanish) - 235 hours of training data after data cleaning\n\n### Datasets\n\nWhile training this model, we used the following datasets:\n\n- Librispeech 960 hours of English speech (pre-training)\n- Fisher Corpus (pre-training)\n- Switchboard-1 Dataset (pre-training)\n- WSJ-0 and WSJ-1 (pre-training)\n- National Speech Corpus - 1 (pre-training)\n- Mozilla Common Voice (pre-training)\n- Mozilla Common Voice (Spanish) (fine-tuning)\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 [1] with the following script :\n\n```bash\npython [NEMO_GIT_FOLDER]/scripts/tokenizers/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\n\nThe performance of Automatic Speech Recognition models is measuring using Word Error Rate.\n\nThe model obtains the following scores on the following evaluation datasets -\n\n- 9.1 % on `MCV dev set`\n- 10.3 % on `MCV test set`\n- 4.9 % on `MLS dev set`\n- 5.2 % on `MLS test set`\n\nNote that these scores above are not particularly indicative of the quality of transcriptions that models trained on ASR Set will achieve, but they are a useful proxy.\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\n```python\nimport nemo.collections.asr as nemo_asr\n\nasr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name=\"stt_es_citrinet_512\")\n```\n\n### Transcribing text with this model\n\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_es_citrinet_512\" \\\n audio_dir=\"\"\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] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)\n\n[1] [Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition](https://arxiv.org/abs/2104.01721)\n\n[3] [Mozilla CommonVoice (MCV)](https://commonvoice.mozilla.org/en)\n\n[4] [Multilingual LibriSpeech (MLS)](https://arxiv.org/abs/2012.03411)\n\n[5] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\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).","STT Es Citrinet 512",["D",1680635623364],"/api/image/nvidia/nemo/models/stt_es_citrinet_512","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","bias","explainability","privacy","safetyAndSecurity",[],[311,312,313,314,315,316,317,318,319,320],[],[],[],{"_90":343,"_138":344,"_139":345,"_141":142},{"_90":340,"_138":341,"_139":342,"_141":142},{"_90":176,"_138":176,"_139":339,"_141":142},{"_90":336,"_138":337,"_139":338,"_141":142},{"_90":332,"_138":333,"_139":334,"_141":142},{"_90":330,"_138":330,"_139":331,"_141":142},{"_90":328,"_138":328,"_139":329,"_141":142},{"_90":325,"_138":325,"_139":326,"_141":142},{"_90":323,"_138":323,"_139":324,"_141":142},{"_90":321,"_138":321,"_139":322,"_141":142},"STT",{"_143":142,"_138":321},"PytorchLightning",{"_143":142,"_138":323},"PyTorch",{"_143":327,"_138":325},"framework","NeMo",{"_143":327,"_138":328},"Es",{"_143":142,"_138":330},"DL","soln_dl",{"_143":335,"_138":333},"technology","Conversational AI","soln_conversational_ai",{"_143":335,"_138":337},{"_143":142,"_138":176},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_143":149,"_138":341},"AI","soln_ai",{"_143":335,"_138":344},{"_205":1320,"_207":1321},"ngcApps",[1273],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1254":1255,"_1256":1257,"_1258":1259,"_1260":1261,"_1262":357,"_236":383,"_1263":1264,"_1265":51,"_1266":183,"_1267":183,"_1268":1269,"_1270":183,"_1271":1272},"amplitude",{"_1251":1252,"_897":-7,"_1253":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1245":1246,"_1247":1248,"_1249":1250},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787629775065,"1.12.0","defaultSearchSuggestions",{"_1137":263,"_1138":1139,"_1140":1141,"_1142":1143},"announcements",[],"consentState",{"_422":1127,"_1128":183,"_1129":26,"_1130":26,"_1131":1132},"consentConfig",{"_404":405,"_406":407,"_408":409,"_410":411,"_412":413,"_414":415,"_416":417,"_418":419,"_420":421,"_422":423},"showContactAdmin",1787629775071,[],[],[397,398,399,400,401,402,403],{"_40":54,"_42":65,"_44":200,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":63,"_44":200,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":61,"_44":200,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":200,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":55,"_44":200,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":200,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":200,"_46":-5,"_48":-5,"_50":51},"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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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. 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These cookies may store a unique ID so that our system will remember you when you return. 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