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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",[247,248,249,250],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_214":267,"_252":268,"_253":269,"_255":256},{"_214":263,"_252":264,"_253":265,"_255":256},{"_214":259,"_252":260,"_253":261,"_255":256},{"_214":251,"_252":251,"_253":254,"_255":256},"PyTorch","value","filter",{"_257":258,"_252":251},"category","label","field","framework","DL","soln_dl",{"_257":262,"_252":260},"technology","Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_257":266,"_252":264},"application","AI","soln_ai",{"_257":262,"_252":268},{"_106":1247,"_108":1248},"ngcApps",[1200],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1181":1182,"_1183":1184,"_1185":1186,"_1187":1188,"_1189":281,"_137":307,"_1190":1191,"_1192":55,"_1193":85,"_1194":85,"_1195":1196,"_1197":85,"_1198":1199},"amplitude",{"_1178":1179,"_820":-7,"_1180":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1172":1173,"_1174":1175,"_1176":1177},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1788791451450,"1.15.0","defaultSearchSuggestions",{"_1060":388,"_1061":1062,"_1063":1064,"_1065":1066},"announcements",[],"consentState",{"_345":1050,"_1051":85,"_1052":30,"_1053":30,"_1054":1055},"consentConfig",{"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346},"showContactAdmin",1788791451451,[],[],[321,322,323,324,325,326],{"_44":59,"_46":70,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":49,"_50":51,"_52":53,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},"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 uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","linkedin.com",[438],{"_77":439,"_393":436,"_394":30,"_166":440},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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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. If you do not allow these cookies, then some or all of these services may not function properly.",[577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601],[548,549,550,551,552],{"_386":462,"_214":462,"_166":388,"_359":572},{"_386":436,"_214":436,"_166":388,"_359":567},{"_386":562,"_214":562,"_166":388,"_359":563},{"_386":557,"_214":557,"_166":388,"_359":558},{"_386":553,"_214":553,"_166":388,"_359":554},"nvidia.partners",[555],{"_77":556,"_393":553,"_394":30,"_166":430},"edge","app.hushly.com",[559],{"_77":560,"_393":557,"_394":30,"_166":561},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[564],{"_77":565,"_393":562,"_394":30,"_166":566},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688],[640,641,642,643],{"_386":655,"_214":655,"_166":388,"_359":656},{"_386":650,"_214":650,"_166":388,"_359":651},{"_386":449,"_214":449,"_166":388,"_359":647},{"_386":562,"_214":562,"_166":388,"_359":644},[645],{"_77":646,"_393":562,"_394":85,"_166":566},"_gh_sess",[648],{"_77":649,"_393":449,"_394":85,"_166":456},"YSC","bizible.com",[652],{"_77":653,"_393":650,"_394":30,"_166":654},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[657],{"_77":658,"_393":655,"_394":30,"_166":659},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_77":730,"_393":508,"_394":30,"_166":731},{"_77":715,"_393":729,"_394":85,"_166":716},{"_77":698,"_393":511,"_394":30,"_166":623},{"_77":698,"_393":611,"_394":30,"_166":623},{"_77":727,"_393":508,"_394":30,"_166":728},{"_77":715,"_393":514,"_394":85,"_166":716},{"_77":725,"_393":508,"_394":30,"_166":726},{"_77":715,"_393":511,"_394":85,"_166":716},{"_77":723,"_393":610,"_394":30,"_166":724},{"_77":715,"_393":632,"_394":85,"_166":716},{"_77":695,"_393":511,"_394":30,"_166":697},{"_77":721,"_393":508,"_394":85,"_166":722},{"_77":719,"_393":508,"_394":30,"_166":720},{"_77":717,"_393":508,"_394":85,"_166":718},{"_77":715,"_393":606,"_394":85,"_166":716},{"_77":713,"_393":508,"_394":30,"_166":714},{"_77":711,"_393":508,"_394":30,"_166":712},{"_77":709,"_393":508,"_394":85,"_166":710},{"_77":707,"_393":615,"_394":30,"_166":708},{"_77":695,"_393":606,"_394":30,"_166":697},{"_77":705,"_393":508,"_394":30,"_166":706},{"_77":703,"_393":508,"_394":85,"_166":704},{"_77":701,"_393":508,"_394":30,"_166":702},{"_77":699,"_393":508,"_394":30,"_166":700},{"_77":698,"_393":508,"_394":30,"_166":623},{"_77":695,"_393":696,"_394":30,"_166":697},{"_77":693,"_393":508,"_394":30,"_166":694},{"_77":691,"_393":508,"_394":85,"_166":692},{"_77":689,"_393":508,"_394":30,"_166":690},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. 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.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. 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