Model Overview

This collection contains large size versions of Conformer-CTC (around 120M parameters) trained on NeMo ASRSet with around 24500 hours of english speech. The model transcribes speech in lower case english 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 [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.

The checkpoint of the language model used as the neural rescorer can be found here. You may find more info on how to train and use language models for ASR models here: ASR Language Modeling

Datasets

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

Librispeech 960 hours of English speech
Fisher Corpus
Switchboard-1 Dataset
WSJ-0 and WSJ-1
National Speech Corpus (Part 1, Part 6)
VCTK
VoxPopuli (EN)
Europarl-ASR (EN)
Multilingual Librispeech (MLS EN) - 2,000 hours subset
Mozilla Common Voice (v7.0)
People's Speech - 12,000 hrs subset

Note: older versions of the model may have trained on smaller set of datasets.

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\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	LS test-other	LS test-clean	WSJ Eval92	WSJ Dev93	NSC Part 1	MLS Test	MCV Test 6.1	MCV Test 8.0	Train Dataset
1.10.0	SentencePiece Unigram	128	4.5	2.1	1.7	2.7	6.7	6.6	8.2	9.4	NeMo ASRSET 3.0
1.6.0	SentencePiece Unigram	128	4.3	2.2	2.0	2.9	7.0	7.2	8.0	9.4	NeMo ASRSET 2.0
1.0.0	SentencePiece Unigram	128	5.4	2.5	2.1	3.0	7.9	-	-	-	NeMo ASRSET 1.4.1
rc1.0.0	WordPiece	128	6.3	2.7	-	-	-	-	-	-	LibriSpeech

You may use language models to improve the accuracy of the models. The WER(%) of the latest model with different language modeling techniques are 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\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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

Language Modeling	Training Dataset	LS test-other	LS test-clean	Comment
N-gram LM	LS Train + LS LM Corpus	3.6	1.8	N=10, beam_width=128, n_gram_alpha=1.0, n_gram_beta=1.0
Neural Rescorer(Transformer)	LS Train + LS LM Corpus	3.5	1.7	N=10, beam_width=128
N-gram + Neural Rescorer(Transformer)	LS Train + LS LM Corpus	3.3	1.7	N=10, beam_width=128, n_gram_alpha=1.0, n_gram_beta=1.0

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_en_conformer_ctc_large\")\n

Transcribing text with this model

"\n\">

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_en_conformer_ctc_large\" \\\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 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] 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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The model transcribes speech in lower case english 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 [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 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\nThe checkpoint of the language model used as the neural rescorer can be found [here](https://ngc.nvidia.com/catalog/models/nvidia:nemo:asrlm_en_transformer_large_ls). You may find more info on how to train and use language models for ASR models here: [ASR Language Modeling](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_modeling.html)\n\n### Datasets\n\nAll the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several thousand hours of English speech:\n\n- Librispeech 960 hours of English speech\n- Fisher Corpus\n- Switchboard-1 Dataset\n- WSJ-0 and WSJ-1\n- National Speech Corpus (Part 1, Part 6)\n- VCTK\n- VoxPopuli (EN)\n- Europarl-ASR (EN)\n- Multilingual Librispeech (MLS EN) - 2,000 hours subset\n- Mozilla Common Voice (v7.0)\n- People's Speech - 12,000 hrs subset\n\nNote: older versions of the model may have trained on smaller set of datasets.\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 | LS test-other | LS test-clean | WSJ Eval92 | WSJ Dev93 | NSC Part 1 | MLS Test | MCV Test 6.1 | MCV Test 8.0 |Train Dataset |\n|---------|----------------------|--------|---------------|---------------|------------|-----------|-------|------|-----|-------|---|\n| 1.10.0 | SentencePiece Unigram | 128 | 4.5 | 2.1 | 1.7 | 2.7 | 6.7 | 6.6 | 8.2 | 9.4 | NeMo ASRSET 3.0 |\n| 1.6.0 | SentencePiece Unigram | 128 | 4.3 | 2.2 | 2.0 | 2.9 | 7.0 | 7.2 | 8.0 | 9.4 | NeMo ASRSET 2.0 |\n| 1.0.0 | SentencePiece Unigram | 128 | 5.4 | 2.5 | 2.1 | 3.0 | 7.9 | - | - | - | NeMo ASRSET 1.4.1 |\n| rc1.0.0 | WordPiece | 128 | 6.3 | 2.7 | - | - | - | - | - | - | LibriSpeech |\n\n\nYou may use language models to improve the accuracy of the models. The WER(%) of the latest model with different language modeling techniques are reported in the follwoing table.\n\n| Language Modeling | Training Dataset | LS test-other | LS test-clean | Comment |\n|-------------------------------------|-------------------------|---------------|---------------|---------------------------------------------------------|\n|N-gram LM | LS Train + LS LM Corpus | 3.6 | 1.8 | N=10, beam_width=128, n_gram_alpha=1.0, n_gram_beta=1.0 |\n|Neural Rescorer(Transformer) | LS Train + LS LM Corpus | 3.5 | 1.7 | N=10, beam_width=128 |\n|N-gram + Neural Rescorer(Transformer)| LS Train + LS LM Corpus | 3.3 | 1.7 | N=10, beam_width=128, n_gram_alpha=1.0, n_gram_beta=1.0 |\n\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_en_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_en_conformer_ctc_large\" \\\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\n## References\n\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\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).","STT En Conformer-CTC Large",["D",1680638117637],"/api/image/nvidia/nemo/models/stt_en_conformer_ctc_large","https://assets.nvidiagrid.net/ngc/logos/Nemo.png",486696960,"bias","explainability","privacy","safetyAndSecurity",[],[320,321,322,323],[],[],[],{"_89":330,"_137":330,"_138":331,"_140":141},{"_89":328,"_137":328,"_138":329,"_140":141},{"_89":326,"_137":326,"_138":327,"_140":141},{"_89":324,"_137":324,"_138":325,"_140":141},"SST",{"_142":141,"_137":324},"PyTorchLightning",{"_142":141,"_137":326},"En",{"_142":141,"_137":328},"Conformer-CTC",{"_142":141,"_137":330},{"_202":1307,"_204":1308},"ngcApps",[1260],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1241":1242,"_1243":1244,"_1245":1246,"_1247":1248,"_1249":343,"_233":369,"_1250":1251,"_1252":51,"_1253":182,"_1254":182,"_1255":1256,"_1257":182,"_1258":1259},"amplitude",{"_1238":1239,"_883":-7,"_1240":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1232":1233,"_1234":1235,"_1236":1237},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787938122820,"1.12.0","defaultSearchSuggestions",{"_1123":262,"_1124":1125,"_1126":1127,"_1128":1129},"announcements",[],"consentState",{"_408":1113,"_1114":182,"_1115":26,"_1116":26,"_1117":1118},"consentConfig",{"_390":391,"_392":393,"_394":395,"_396":397,"_398":399,"_400":401,"_402":403,"_404":405,"_406":407,"_408":409},"showContactAdmin",1787938122821,[],[],[383,384,385,386,387,388,389],{"_40":55,"_42":65,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":63,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":62,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":61,"_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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These cookies may store a unique ID so that our system will remember you when you return. 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