Model Overview

This collection contains large size versions of Conformer-CTC (around 120M parameters) trained on MCV-9.0 Catalan dataset.

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 [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 [2] for these models were built using the text transcripts of the train set with this script, and can be found inside the .nemo files.

The vocabulary we use contains 44 characters:\n[' ', \"'\", '-', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '·', 'à', 'á', 'ç', 'è', 'é', 'í', 'ï', 'ñ', 'ò', 'ó', 'ú', 'ü', 'ı', '–', '—']

Full config can be found inside the .nemo files.

Datasets

All the models in this collection are trained on MCV-9.0 Catalan dataset, which contains around 1203 hours training, 28 hours of development and 27 hours of testing speech audios.

Performance

The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Version	Tokenizer	Vocabulary Size	Dev WER	Test WER	Train Dataset
1.11.0	SentencePiece Unigram	128	4.70	4.27	MCV-9.0 Train set

You may use language models (LMs) and beam search to improve the accuracy of the models, as reported in the follwoing table.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Language Model	Test WER	Test WER w/ Oracle LM	Train Dataset	Settings
N-gram LM	3.77	1.54	MCV-9.0 Train set	N=6, beam_width=128, ngram_alpha=1.5, ngram_beta=2.0

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_ca_conformer_ctc_large\")\n

Transcribing text with this model

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

Input

This model accepts 16 kHz mono-channel audio (wav files) as input.

Output

This model provides transcribed speech as a string for a given audio sample.

Limitations

Since all models are trained on just MCV-9.0 dataset, 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.

Reference

[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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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/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\nThe tokenizers [2] 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), and can be found inside the .nemo files.\n\nThe vocabulary we use contains 44 characters:\n`[' ', \"'\", '-', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '·', 'à', 'á', 'ç', 'è', 'é', 'í', 'ï', 'ñ', 'ò', 'ó', 'ú', 'ü', 'ı', '–', '—']`\n\nFull config can be found inside the .nemo files.\n\n### Datasets\n\nAll the models in this collection are trained on MCV-9.0 Catalan dataset, which contains around 1203 hours training, 28 hours of development and 27 hours of testing speech audios.\n\n## Performance\n\nThe list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.\n\n| Version | Tokenizer | Vocabulary Size | Dev WER| Test WER| Train Dataset |\n|---------|-----------------------|-----------------|-----|------|-----------------|\n| 1.11.0 | SentencePiece Unigram | 128 |4.70 | 4.27 | MCV-9.0 Train set |\n\nYou may use language models (LMs) and beam search to improve the accuracy of the models, as reported in the follwoing table.\n\n| Language Model | Test WER | Test WER w/ Oracle LM | Train Dataset | Settings |\n|----------------|----------|-----------------------|------------------|-------------------------------------------------------|\n| N-gram LM | 3.77 | 1.54 |MCV-9.0 Train set |N=6, beam_width=128, ngram_alpha=1.5, ngram_beta=2.0 |\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_ca_conformer_ctc_large\")\n```\n\n### Transcribing text with this model\n\n```python\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_ca_conformer_ctc_large\" \\\n audio_dir=\"[PATH_TO_AUDIO_FOLDER]\"\n```\n\n### Input\n\nThis model accepts 16 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 all models are trained on just MCV-9.0 dataset, 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## Reference\n\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\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 Ca Conformer-CTC Large",["D",1680635976107],"publisher","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/stt_ca_conformer_ctc_large","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[234,235,236,237],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_203":250,"_239":251,"_240":252,"_242":243},{"_203":248,"_239":248,"_240":249,"_242":243},{"_203":245,"_239":245,"_240":246,"_242":243},{"_203":238,"_239":238,"_240":241,"_242":243},"PytorchLightning","value","filter",{"_244":243,"_239":238},"category","label","field","PyTorch",{"_244":247,"_239":245},"framework","Ca",{"_244":243,"_239":248},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_244":253,"_239":251},"application",{"_105":1235,"_107":1236},"ngcApps",[1188],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1169":1170,"_1171":1172,"_1173":1174,"_1175":1176,"_1177":265,"_136":291,"_1178":1179,"_1180":55,"_1181":84,"_1182":84,"_1183":1184,"_1185":84,"_1186":1187},"amplitude",{"_1166":1167,"_803":-7,"_1168":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1160":1161,"_1162":1163,"_1164":1165},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789114526727,"1.15.0","defaultSearchSuggestions",{"_1043":164,"_1044":1045,"_1046":1047,"_1048":1049},"announcements",[],"consentState",{"_329":1033,"_1034":84,"_1035":30,"_1036":30,"_1037":1038},"consentConfig",{"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330},"showContactAdmin",1789114526730,[],[],[305,306,307,308,309,310],{"_44":59,"_46":68,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":67,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":100,"_50":-5,"_52":-5,"_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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