Model Overview

This collection contains large size versions of Conformer-CTC (around 120M parameters) trained on NeMo ASRSet 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 128, 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

Conformer-CTC model is a non-autoregressive variant of Conformer model [2] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: Conformer-CTC Model.

Training

The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this example script and this base config.

The tokenizers for these models were built using the text transcripts of the train set with this script.

Datasets

All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of over a thousand hours of French speech:

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.

(Note: The MultiliingualLibreSpeech set includes orthography of both pre- and post- 1990 reform, which may affect predictability of hyphenation.)

Tokenizer Construction

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

We build a token set 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=128 \\\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

8.35 % on MCV7.0 dev
9.63 % on MCV7.0 test
5.88 % on MLS dev
4.91 % on MLS test

With 128 beam search and 4gram KenLM model (included with this model):

7.95 % on MCV7.0 dev
9.16 % on MCV7.0 test
5.57 % on MLS dev
4.66 % 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. For 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_conformer_ctc_large\")\n

Transcribing text with this model

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

Further, since portions of the training set contain text from both pre- and post- 1990 orthographic reform, regularity of punctuation may vary between the two styles.\nFor downstream tasks requiring more consistency, finetuning or downstream processing may be required. If exact orthography is not necessary, then using secondary model is advised.

References

[1] Conformer: Convolution-augmented Transformer for Speech Recognition

[2] Google Sentencepiece Tokenizer

[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 128, 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\n\nConformer-CTC model is a non-autoregressive variant of Conformer model [2] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: [Conformer-CTC Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html).\n\n## Training\n\nThe NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_to_text_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_ctc_bpe.yaml).\n\nThe tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).\n\n### Datasets\nAll the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of over a thousand hours of French speech:\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(Note: The MultiliingualLibreSpeech set includes orthography of both pre- and post- 1990 reform, which may affect predictability of hyphenation.) \n\n### Tokenizer Construction\nThe tokenizer for this model was built using text corpus provided with the train dataset.\n\nWe build a token set 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=128 \\\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- 8.35 % on MCV7.0 dev\n- 9.63 % on MCV7.0 test\n- 5.88 % on MLS dev\n- 4.91 % on MLS test\n\nWith 128 beam search and 4gram KenLM model (included with this model):\n\n- 7.95 % on MCV7.0 dev\n- 9.16 % on MCV7.0 test\n- 5.57 % on MLS dev\n- 4.66 % 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. For 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\n### Automatically load the model from NGC\n```\nimport nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name=\"stt_fr_conformer_ctc_large\")\n```\n\n### Transcribing text with this model\n```\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_fr_conformer_ctc_large\" \\\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 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\nFurther, since portions of the training set contain text from both pre- and post- 1990 orthographic reform, regularity of punctuation may vary between the two styles. \nFor downstream tasks requiring more consistency, finetuning or downstream processing may be required. If exact orthography is not necessary, then using secondary model is advised.\n\n## References\n[1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)\n\n[2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)\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](https://ngc.nvidia.com/legal/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](https://ngc.nvidia.com/legal/terms).","displayName","STT Fr Conformer-CTC Large",["D",1680635818170],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/stt_fr_conformer_ctc_large","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes",1031064795,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[244,245,246,247],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_211":262,"_249":263,"_251":264,"_253":254},{"_211":260,"_249":260,"_251":261,"_253":254},{"_211":257,"_249":258,"_251":259,"_253":254},{"_211":248,"_249":250,"_251":252,"_253":254},"DL","value","soln_dl","filter",{"_255":256,"_249":250},"category","label","field","technology","Conversational AI","soln_conversational_ai",{"_255":256,"_249":258},"Conformer CTC",{"_255":254,"_249":260},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_255":265,"_249":263},"application",{"_112":1442,"_114":1443},"ngcApps",[1395],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1377":1378,"_1379":1380,"_1381":1382,"_1383":1384,"_1385":277,"_139":301,"_1386":1387,"_1375":56,"_1388":91,"_1389":91,"_1390":1391,"_1392":91,"_1393":1394},"amplitude",{"_1373":1374,"_1375":56,"_975":-7,"_1376":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","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,"1.4.0","defaultSearchSuggestions",{"_1250":382,"_1251":1252,"_1253":1254,"_1255":1256},"announcements",[],"consentState",{"_340":1240,"_1241":31,"_1242":31,"_1243":91,"_1244":1245},"consentConfig",{"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341},"showContactAdmin",1783408102360,[],[],[315,316,317,318,319,320],{"_45":59,"_47":69,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":67,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":65,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":107,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":321,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},4,"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. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.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.","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. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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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.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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Name	Size	Updated	Actions
kenlm.4gram	60.82 MB	March 1, 2025 UTC	curl wget Download
stt_fr_conformer_ctc_large.nemo	430.79 MB	March 1, 2025 UTC	curl wget Download