Model Overview

This collection contains large size versions of Conformer-CTC (around 120M parameters) trained on NeMo ASRSet with around 500 hours of Italian speech.

It utilizes a Google SentencePiece [1] tokenizer with vocabulary size 128, and transcribes speech in lowercase Italian alphabet along with spaces and apostrophes.

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 487 hours of Italian speech:

Mozilla Common Voice 11.0 (Italian) - 220 hours after data cleaning
Multilingual LibriSpeech (Italian) - 214 hours after data cleaning
VoxPopuli transcribed subset (Italian) - 53 hours after data cleaning

Tokenizer Construction

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

We build a Google Sentencepiece Tokenizer [1] with the following script :

" \\\n --vocab_size=128 \\\n --tokenizer="spe" \\\n --spe_type="unigram" \\\n --spe_character_coverage=1.0 \\\n --no_lower_case \\\n --log\n\">

python [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \\\n  --manifest=\"train_manifest.json\" \\\n  --data_root=\"<OUTPUT DIRECTORY FOR TOKENIZER>\" \\\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 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

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

Version	Tokenizer	Vocabulary Size	MCV 11.0 Dev	MCV 11.0 Test	MLS Dev	MLS Test	VoxPopuli Dev	VoxPopuli Test	Train Dataset
1.13.0	SentencePiece Unigram	128	6.65	7.20	14.77	11.76	14.77	18.06	NeMo ASRSET It 2.0

You can combine this model with external language models to further improve WER. The table below shows results of beam search decoding with 5-gram KenLM model (included with this NGC model). To train 5-gram language model only the transcriptions from the training dataset were used.

\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	MCV 11.0 Dev	MCV 11.0 Test	MLS Dev	MLS Test	VoxPopuli Dev	VoxPopuli Test	Comment
N-gram LM	5.38	5.92	13.16	10.62	13.43	16.75	N=5, beam_width=128, n_gram_alpha=2.0, n_gram_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_it_conformer_ctc_large\")\n

Transcribing text with this model

"\n\">

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

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

[3] NVIDIA NeMo Toolkit

Licence

License to use this model is covered by the CC-BY-4 License 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 CC-BY-4 License.

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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 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\n\nAll the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of 487 hours of Italian speech:\n\n- Mozilla Common Voice 11.0 (Italian) - 220 hours after data cleaning\n- Multilingual LibriSpeech (Italian) - 214 hours after data cleaning\n- VoxPopuli transcribed subset (Italian) - 53 hours after data cleaning\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 [1] with the following script :\n\n```bash\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\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 | MCV 11.0 Dev | MCV 11.0 Test | MLS Dev | MLS Test | VoxPopuli Dev | VoxPopuli Test | Train Dataset |\n|---------|-----------------------|-----------------|--------------|---------------|---------|----------|---------------|----------------|--------------------|\n| 1.13.0 | SentencePiece Unigram | 128 | 6.65 | 7.20 | 14.77 | 11.76 | 14.77 | 18.06 | NeMo ASRSET It 2.0 |\n\nYou can combine this model with external language models to further improve WER. The table below shows results of beam search decoding with 5-gram KenLM model (included with this NGC model). To train 5-gram language model only the transcriptions from the training dataset were used.\n\n| Language Modeling | MCV 11.0 Dev | MCV 11.0 Test | MLS Dev | MLS Test | VoxPopuli Dev | VoxPopuli Test | Comment |\n|-------------------|--------------|---------------|---------|----------|---------------|----------------|--------------------------------------------------------|\n| N-gram LM | 5.38 | 5.92 | 13.16 | 10.62 | 13.43 | 16.75 | N=5, beam_width=128, n_gram_alpha=2.0, n_gram_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_it_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_es_conformer_ctc_large\" \\\n audio_dir=\"\"\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 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] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)\n\n[2] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)\n\n[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n### Licence\n\nLicense to use this model is covered by the [CC-BY-4 License](https://creativecommons.org/licenses/by/4.0/legalcode) 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 [CC-BY-4 License](https://creativecommons.org/licenses/by/4.0/legalcode).","displayName","STT It Conformer CTC Large",["D",1680635732810],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/stt_it_conformer_ctc_large","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[278,279,280,281,282,283],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_247":300,"_110":301,"_285":302,"_287":288},{"_247":115,"_110":115,"_285":299,"_287":288},{"_247":295,"_110":296,"_285":297,"_287":288},{"_247":293,"_110":293,"_285":294,"_287":288},{"_247":290,"_110":290,"_285":291,"_287":288},{"_247":284,"_110":284,"_285":286,"_287":288},"PytorchLightning","filter",{"_289":288,"_110":284},"category","label","field","NeMo",{"_289":292,"_110":290},"framework","It",{"_289":288,"_110":293},"DL","soln_dl",{"_289":298,"_110":296},"technology",{"_289":288,"_110":115},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_289":303,"_110":301},"application",{"_158":1279,"_160":1280},"ngcApps",[1232],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1213":1214,"_1215":1216,"_1217":1218,"_1219":1220,"_1221":315,"_189":341,"_1222":1223,"_1224":59,"_1225":137,"_1226":137,"_1227":1228,"_1229":137,"_1230":1231},"amplitude",{"_1210":1211,"_852":-7,"_1212":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1204":1205,"_1206":1207,"_1208":1209},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787843183434,"1.12.0","defaultSearchSuggestions",{"_1092":220,"_1093":1094,"_1095":1096,"_1097":1098},"announcements",[],"consentState",{"_378":1082,"_1083":137,"_1084":34,"_1085":34,"_1086":1087},"consentConfig",{"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379},"showContactAdmin",1787843183435,[],[],[355,356,357,358,359],{"_48":63,"_50":74,"_52":153,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":153,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":153,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":153,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":153,"_54":-5,"_56":-5,"_58":59},"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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Key	Value
Architecture	Conformer CTC
Outputs	Transcribed speech
Inputs	16 kHz Mono-channel