Model Overview

Citrinet-1024 model which has been trained on over 2300 hours of German speech.

It utilizes a Google SentencePiece [1] tokenizer with vocabulary size 1024, and transcribes text in lower case German alphabet with spaces but without punctuation.

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

This model was pre-trained for 100 epochs on unlabeled German speech data, and then fine-tuned on a composite dataset comprising of several thousand labeled hours of speech, compiled from various publicly available sources. The NeMo toolkit [3] was used for training this model over several hundred epochs on multiple GPUs.

Datasets

For pre-training, we used the German unlabeled speech from the Vox Populi dataset (~20k hours).

While fine-tuning this model, we used the following cleaned datasets:

MCV7.0 567 hours
MLS 1524 hours
VoxPopuli 214 hours

Tokenizer Construction

The tokenizer for this model was built using text corpus provided with the train dataset.

We build a Google Sentencepiece Tokenizer [1] with the following script :

python [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

Performance

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

The latest model obtains the following greedy scores on the following evaluation datasets -

6.63 % on MCV7.0 dev
7.59 % on MCV7.0 test
4.06 % on MLS dev
5.07 % on MLS test
12.33 % on Vox Populi dev
10.02 % on Vox Populi test

After beam search with a beam size of 128 and 4-gram KenLM model, the Word Error Rates are:

5.00 % on MCV7.0 dev
5.84 % on MCV7.0 test
3.76 % on MLS dev
4.50 % on MLS test
11.79 % on Vox Populi dev
9.42 % on Vox Populi test

Note that these evaluation datasets have been filtered and preprocessed to only contain German alphabet characters and are removed of punctuation.

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.EncDecCTCModelBPE.from_pretrained(model_name=\"stt_de_citrinet_1024\")\n

Transcribing text with this model

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_de_citrinet_1024\" \\\n audio_dir=\"\"\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

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

[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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There are many variants of the Citrinet family of models, which are further discussed in the paper [2].\n\n## Training\n\nThis model was pre-trained for 100 epochs on unlabeled German speech data, and then fine-tuned on a composite dataset comprising of several thousand labeled hours of speech, compiled from various publicly available sources. The NeMo toolkit [3] was used for training this model over several hundred epochs on multiple GPUs.\n\n### Datasets\n\nFor pre-training, we used the German unlabeled speech from the Vox Populi dataset (~20k hours).\n\nWhile fine-tuning this model, we used the following cleaned datasets:\n\n- MCV7.0 567 hours\n- MLS 1524 hours\n- VoxPopuli 214 hours\n\n### Tokenizer Construction\n\nThe tokenizer for this model was built using text corpus provided with the train dataset.\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 measured using Word Error Rate.\n\nThe latest model obtains the following greedy scores on the following evaluation datasets -\n\n- 6.63 % on `MCV7.0 dev`\n- 7.59 % on `MCV7.0 test`\n- 4.06 % on `MLS dev`\n- 5.07 % on `MLS test`\n- 12.33 % on `Vox Populi dev`\n- 10.02 % on `Vox Populi test`\n\nAfter beam search with a beam size of 128 and 4-gram KenLM model, the Word Error Rates are:\n\n- 5.00 % on `MCV7.0 dev`\n- 5.84 % on `MCV7.0 test`\n- 3.76 % on `MLS dev`\n- 4.50 % on `MLS test`\n- 11.79 % on `Vox Populi dev`\n- 9.42 % on `Vox Populi test`\n\nNote that these evaluation datasets have been filtered and preprocessed to only contain German alphabet characters and are removed of punctuation. \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.EncDecCTCModelBPE.from_pretrained(model_name=\"stt_de_citrinet_1024\")\n```\n\n### Transcribing text with this model\n\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_de_citrinet_1024\" \\\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[2] [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] [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).","displayName","STT DE Citrinet 1024",["D",1680637949246],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/stt_de_citrinet_1024","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes",532299907,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[256,257,258,259],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_223":276,"_261":277,"_262":278,"_264":265},{"_223":272,"_261":273,"_262":274,"_264":265},{"_223":268,"_261":269,"_262":270,"_264":265},{"_223":260,"_261":260,"_262":263,"_264":265},"PyTorch","value","filter",{"_266":267,"_261":260},"category","label","field","framework","DL","soln_dl",{"_266":271,"_261":269},"technology","Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_266":275,"_261":273},"application","AI","soln_ai",{"_266":271,"_261":277},{"_113":1452,"_115":1453},"ngcApps",[1405],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1386":1387,"_1388":1389,"_1390":1391,"_1392":1393,"_1394":290,"_145":314,"_1395":1396,"_1397":56,"_1398":92,"_1399":92,"_1400":1401,"_1402":92,"_1403":1404},"amplitude",{"_1383":1384,"_986":-7,"_1385":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1377":1378,"_1379":1380,"_1381":1382},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1261":394,"_1262":1263,"_1264":1265,"_1266":1267},"announcements",[],"consentState",{"_352":1251,"_1252":92,"_1253":31,"_1254":31,"_1255":1256},"consentConfig",{"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353},"showContactAdmin",1784249414815,[],[],[328,329,330,331,332,333],{"_45":60,"_47":71,"_49":108,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":108,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":108,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":108,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":137,"_55":56},{"_45":46,"_47":48,"_49":108,"_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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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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Name	Size	Updated	Actions
kenlm.4gram	1.23 GB	March 1, 2025 UTC	curl wget Download
stt_de_citrinet_1024.nemo	462.55 MB	March 1, 2025 UTC	curl wget Download