Model Overview

This collection contains large size versions of Conformer-CTC (around 120M parameters) trained on Mozilla Common Voice 9.0 Kinyarwanda dataset with around 2000 hours of Kinyarwanda speech. The model transcribes speech in lower case latin alphabet along with spaces and apostrophes.

Model Architecture

Conformer-CTC model is a non-autoregressive variant of Conformer model [1] 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 [2] was used for training the models for over several hundred epochs. These models are trained with this example script and this base config.

Datasets

Mozilla Common Voice (v9.0) Kinyarwanda dataset

Tokenizer Construction

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

We build a Google Sentencepiece Tokenizer [3] with the following script:

python [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \\\n  --manifest=dev_manifest.json,train_manifest.json \\\n  --vocab_size=128 \\\n  --data_root=\"\" \\\n  --tokenizer=\"spe\" \\\n  --spe_type=bpe \\\n  --spe_character_coverage=1.0 \\\n  --spe_max_sentencepiece_length=2\n  --log\n

Performance

The performance of Automatic Speech Recognition models is measured using Word Error Rate(WER) and Character Error Rate(CER).

The model obtains the following scores on the Mozilla Common Voice test set: 18.22 % WER, 5.45 % CER

How to Use this Model

The model is available for use in the NeMo toolkit [2], 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_rw_conformer_ctc_large\")\n

Transcribing text with this model

"\n\">

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n  pretrained_name=\"stt_rw_conformer_ctc_large\" \\\n  audio_dir=\"<DIRECTORY CONTAINING AUDIO FILES>\"\n

Input

This model accepts 16000 Hz 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] Conformer: Convolution-augmented Transformer for Speech Recognition

[2] NVIDIA NeMo Toolkit

[3] Google Sentencepiece Tokenizer

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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The model transcribes speech in lower case latin alphabet along with spaces and apostrophes.\n\n## Model Architecture\n\nConformer-CTC model is a non-autoregressive variant of Conformer model [1] 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 [2] was used for training the models for over several hundred epochs. These models are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_ctc_bpe.yaml).\n\n### Datasets\n\nMozilla Common Voice (v9.0) Kinyarwanda dataset\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 [3] with the following script:\n\n```bash\npython [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \\\n --manifest=dev_manifest.json,train_manifest.json \\\n --vocab_size=128 \\\n --data_root=\"\" \\\n --tokenizer=\"spe\" \\\n --spe_type=bpe \\\n --spe_character_coverage=1.0 \\\n --spe_max_sentencepiece_length=2\n --log\n```\n\n## Performance\n\nThe performance of Automatic Speech Recognition models is measured using Word Error Rate(WER) and Character Error Rate(CER).\n\nThe model obtains the following scores on the Mozilla Common Voice test set: 18.22 % WER, 5.45 % CER\n\n## How to Use this Model\n\nThe model is available for use in the NeMo toolkit [2], 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_rw_conformer_ctc_large\")\n```\n\n### Transcribing text with this model\n\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_rw_conformer_ctc_large\" \\\n audio_dir=\"\"\n```\n\n### Input\n\nThis model accepts 16000 Hz 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] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)\n\n[2] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n[3] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)\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 Rw Conformer-CTC Large",["D",1680635609578],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/stt_rw_conformer_ctc_large","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239,240,241,242],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_207":254,"_244":255,"_245":256,"_247":248},{"_207":252,"_244":252,"_245":253,"_247":248},{"_207":250,"_244":250,"_245":251,"_247":248},{"_207":243,"_244":243,"_245":246,"_247":248},"Rw","value","filter",{"_249":248,"_244":243},"category","label","field","PyTorchLightning",{"_249":248,"_244":250},"Conformer-CTC",{"_249":248,"_244":252},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_249":257,"_244":255},"application",{"_108":1431,"_110":1432},"ngcApps",[1384],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1365":1366,"_1367":1368,"_1369":1370,"_1371":1372,"_1373":269,"_139":293,"_1374":1375,"_1376":56,"_1377":87,"_1378":87,"_1379":1380,"_1381":87,"_1382":1383},"amplitude",{"_1362":1363,"_964":-7,"_1364":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1356":1357,"_1358":1359,"_1360":1361},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1239":169,"_1240":1241,"_1242":1243,"_1244":1245},"announcements",[],"consentState",{"_331":1229,"_1230":31,"_1231":31,"_1232":87,"_1233":1234},"consentConfig",{"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332},"showContactAdmin",1784063272061,[],[],[307,308,309,310,311],{"_45":60,"_47":71,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":312,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},5,"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 stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.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.","AMP_MKTG_91bf70adfe","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.",[603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623],[568,569,570,571,572],{"_371":463,"_207":463,"_168":169,"_345":594},{"_371":429,"_207":429,"_168":169,"_345":589},{"_371":582,"_207":582,"_168":169,"_345":583},{"_371":578,"_207":578,"_168":169,"_345":579},{"_371":573,"_207":573,"_168":169,"_345":574},"nvcrm.my.site.com",[575],{"_79":576,"_377":573,"_378":31,"_168":577},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[580],{"_79":581,"_377":578,"_378":31,"_168":412},"edge","github.com",[584,585],{"_79":588,"_377":582,"_378":31,"_168":587},{"_79":586,"_377":582,"_378":31,"_168":587},"logged_in","This domain is owned by Github. 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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).","__sec_tid","__sec__fid","crowdsource.nvidia.com","This cookie name is associated with 6sense which is used to collect visitor data related to the user’s visit to the website, such as number of visits, average time spent on the website and which pages have been loaded, for the purpose of serving targeted advertisements.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","__sec__ghost","_gd_svisitor","info.nvidia.com","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.","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. It is either correlated to the 6suuid or a unique value, and can be changed during the lifetime of this cookie.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","s_ips","This cookie is associated with Adobe Analytics. The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_gd_session","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. 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