Model Overview

Citrinet-1024 models with kernel scaling factor (gamma) of 25%, which has been trained on the ASR Set dataset with over 1500 hours of French speech. Includes two models wtih separate tokenization sets, along with accompanying language models (n=4).

Utilizes a Google SentencePiece [1] tokenizer with vocabulary size 1024, and transcribes text in lower case French alphabet along with spaces, apostrophes, and hyphens. Secondary model and associated language model (indicated with \"no_hyphen\" infix) omits hyphen from tokenization.

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

These models were trained on a composite dataset comprising over fifteen hundred 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

While training this model, we used the following cleaned datasets:

MozillaCommonVoice7.0 356 hours
MultilingualLibreSpeech 1036 hours
VoxPopuli 182 hours

Both models use same dataset, excluding a preprocessing step to strip hyphen from data for secondary model's training.

Tokenizer Construction

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

We build two 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 measuring using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.

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

10.76 % on MCV7.0 dev
12.2 % on MCV7.0 test
6.66 % on MLS dev
5.53 % on MLS test

With 128 beam search and 4gram KenLM model:

9.85 % on MCV7.0 dev
11.16 % on MCV7.0 test
6.25 % on MLS dev
5.17 % on MLS test

Note that these evaluation datasets have been filtered and preprocessed to only contain French alphabet characters and are removed of punctuation outside of hyphenation and apostrophe.\nFor the secondary model trained without hyphenation, scores are slightly improved (see version sheet).

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_fr_citrinet_1024_gamma_0_25\")\n

Transcribing text with this model

"\n\">

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n  pretrained_name=\"stt_fr_citrinet_1024_gamma_0_25\" \\\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 publicly 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.

Further, as this data contains orthography from pre- and post- 1990 reform, transcriptions may vary in style. If consistency is required, downstream processing or finetuning may be required. If exact orthography is not necessary, then using secondary model is advised.

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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Includes two models wtih separate tokenization sets, along with accompanying language models (n=4).\n\nUtilizes a Google SentencePiece [1] tokenizer with vocabulary size 1024, and transcribes text in lower case French alphabet along with spaces, apostrophes, and hyphens. Secondary model and associated language model (indicated with \"_no_hyphen_\" infix) omits hyphen from tokenization.\n\n## Model Architecture\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 [2].\n\n## Training\nThese models were trained on a composite dataset comprising over fifteen hundred 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\nWhile training this model, we used the following cleaned datasets:\n\n- MozillaCommonVoice7.0 356 hours\n- MultilingualLibreSpeech 1036 hours\n- VoxPopuli 182 hours\n\nBoth models use same dataset, excluding a preprocessing step to strip hyphen from data for secondary model's training.\n\n### Tokenizer Construction\nThe tokenizer for this model was built using text corpus provided with the train dataset.\n\nWe build two Google Sentencepiece Tokenizer [1] with the following script :\n\n```\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\nThe performance of Automatic Speech Recognition models is measuring using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.\n\nThe latest model obtains the following greedy scores on the following evaluation datasets\n\n- 10.76 % on MCV7.0 dev\n- 12.2 % on MCV7.0 test\n- 6.66 % on MLS dev\n- 5.53 % on MLS test\n\nWith 128 beam search and 4gram KenLM model:\n\n- 9.85 % on MCV7.0 dev\n- 11.16 % on MCV7.0 test\n- 6.25 % on MLS dev\n- 5.17 % on MLS test\n\nNote that these evaluation datasets have been filtered and preprocessed to only contain French alphabet characters and are removed of punctuation outside of hyphenation and apostrophe. \nFor the secondary model trained without hyphenation, scores are slightly improved (see version sheet).\n\n## How to Use this Model\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\nAutomatically load the model from NGC\n\n```\nimport nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name=\"stt_fr_citrinet_1024_gamma_0_25\")\n```\n\nTranscribing text with this model\n```\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_fr_citrinet_1024_gamma_0_25\" \\\n audio_dir=\"\"\n```\n\n## Input\nThis model accepts 16000 KHz Mono-channel Audio (wav files) as input.\n\n## Output\nThis model provides transcribed speech as a string for a given audio sample.\n\n## Limitations\nSince this model was trained on publicly 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\nFurther, as this data contains orthography from pre- and post- 1990 reform, transcriptions may vary in style. If consistency is required, downstream processing or finetuning may be required. If exact orthography is not necessary, then using secondary model is advised.\n\n## References\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## Licence\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 Fr Citrinet 1024 Gamma 0.25",["D",1680634924337],"/api/image/nvidia/nemo/models/stt_fr_citrinet_1024_gamma_0_25","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","bias","explainability","privacy","safetyAndSecurity",[],[311,312,313,314],[],[],[],{"_90":323,"_138":324,"_139":325,"_141":142},{"_90":176,"_138":176,"_139":322,"_141":142},{"_90":319,"_138":320,"_139":321,"_141":142},{"_90":315,"_138":316,"_139":317,"_141":142},"DL","soln_dl",{"_143":318,"_138":316},"technology","Conversational AI","soln_conversational_ai",{"_143":318,"_138":320},{"_143":142,"_138":176},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_143":149,"_138":324},{"_204":1301,"_206":1302},"ngcApps",[1254],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1235":1236,"_1237":1238,"_1239":1240,"_1241":1242,"_1243":337,"_235":363,"_1244":1245,"_1246":51,"_1247":183,"_1248":183,"_1249":1250,"_1251":183,"_1252":1253},"amplitude",{"_1232":1233,"_877":-7,"_1234":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1226":1227,"_1228":1229,"_1230":1231},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787526391107,"1.12.0","defaultSearchSuggestions",{"_1117":263,"_1118":1119,"_1120":1121,"_1122":1123},"announcements",[],"consentState",{"_402":1107,"_1108":183,"_1109":26,"_1110":26,"_1111":1112},"consentConfig",{"_384":385,"_386":387,"_388":389,"_390":391,"_392":393,"_394":395,"_396":397,"_398":399,"_400":401,"_402":403},"showContactAdmin",1787526391108,[],[],[377,378,379,380,381,382,383],{"_40":54,"_42":65,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":63,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":61,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":55,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":60,"_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. If you do not allow these cookies, then some or all of these services may not function properly.",[633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657],[604,605,606,607,608],{"_443":518,"_90":518,"_89":263,"_416":628},{"_443":492,"_90":492,"_89":263,"_416":623},{"_443":618,"_90":618,"_89":263,"_416":619},{"_443":613,"_90":613,"_89":263,"_416":614},{"_443":609,"_90":609,"_89":263,"_416":610},"nvidia.partners",[611],{"_289":612,"_449":609,"_450":26,"_89":486},"edge","app.hushly.com",[615],{"_289":616,"_449":613,"_450":26,"_89":617},"*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",[620],{"_289":621,"_449":618,"_450":26,"_89":622},"_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.",3,[717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745],[697,698,699,700],{"_443":712,"_90":712,"_89":263,"_416":713},{"_443":707,"_90":707,"_89":263,"_416":708},{"_443":505,"_90":505,"_89":263,"_416":704},{"_443":618,"_90":618,"_89":263,"_416":701},[702],{"_289":703,"_449":618,"_450":183,"_89":622},"_gh_sess",[705],{"_289":706,"_449":505,"_450":183,"_89":512},"YSC","bizible.com",[709],{"_289":710,"_449":707,"_450":26,"_89":711},"_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",[714],{"_289":715,"_449":712,"_450":26,"_89":716},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_289":787,"_449":564,"_450":26,"_89":788},{"_289":772,"_449":786,"_450":183,"_89":773},{"_289":755,"_449":567,"_450":26,"_89":679},{"_289":755,"_449":667,"_450":26,"_89":679},{"_289":784,"_449":564,"_450":26,"_89":785},{"_289":772,"_449":570,"_450":183,"_89":773},{"_289":782,"_449":564,"_450":26,"_89":783},{"_289":772,"_449":567,"_450":183,"_89":773},{"_289":780,"_449":666,"_450":26,"_89":781},{"_289":772,"_449":688,"_450":183,"_89":773},{"_289":752,"_449":567,"_450":26,"_89":754},{"_289":778,"_449":564,"_450":183,"_89":779},{"_289":776,"_449":564,"_450":26,"_89":777},{"_289":774,"_449":564,"_450":183,"_89":775},{"_289":772,"_449":662,"_450":183,"_89":773},{"_289":770,"_449":564,"_450":26,"_89":771},{"_289":768,"_449":564,"_450":26,"_89":769},{"_289":766,"_449":564,"_450":183,"_89":767},{"_289":764,"_449":671,"_450":26,"_89":765},{"_289":752,"_449":662,"_450":26,"_89":754},{"_289":762,"_449":564,"_450":26,"_89":763},{"_289":760,"_449":564,"_450":183,"_89":761},{"_289":758,"_449":564,"_450":26,"_89":759},{"_289":756,"_449":564,"_450":26,"_89":757},{"_289":755,"_449":564,"_450":26,"_89":679},{"_289":752,"_449":753,"_450":26,"_89":754},{"_289":750,"_449":564,"_450":26,"_89":751},{"_289":748,"_449":564,"_450":183,"_89":749},{"_289":746,"_449":564,"_450":26,"_89":747},"_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. 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