Model Overview

This collection contains medium size versions of Conformer-CTC (around 30M parameters) trained on ULCA Hindi Corpus with around ~1900 hours of hindi speech. The model transcribes speech in hindi characters along with spaces.

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 ULCA Hindi Labelled Dataset (~1900 hrs)

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 --log\n

Performance\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 and 6-Gram KenLM trained on AI4Bharat Corpus.

KenLM with 128 beam size with n_gram_alpha=1.5, n_gram_beta=2.0.

12.90%/4.50% WER/CER on Interspeech MUCS 2021 Blind Test Set.
14.48%/6.38% WER/CER on Interspeech MUCS 2021 Validation Set.
14.82%/7.17% WER/CER on IIT-Madras Speech Challenge 2020 Evaluation Set.
14.92%/7.28% WER/CER on IIT-Madras Speech Challenge 2020 Dev Set.
16.50%/7.24% WER/CER on Common Voice 8 (without special characters and punctuation)
15.69%/6.11% WER/CER on Common Voice 7 (without special characters and punctuation)
17.17%/5.39% WER/CER on Common Voice 6 (without special characeters and punctuation)

Greedy Decoding Scores:

15.03%/5.06% WER/CER on Interspeech MUCS 2021 Blind Test Set.
16.80%/7.07% WER/CER on Interspeech MUCS 2021 Validation Set.
17.62%/7.45% WER/CER on IIT-Madras Speech Challenge 2020 Evaluation Set.
17.58%/7.48% WER/CER on IIT-Madras Speech Challenge 2020 Dev Set.
20.01%/8.17% WER/CER on Common Voice 8 (without special characters and punctuation)
18.81%/6.93% WER/CER on Common Voice 7 (without special characters and punctuation)
19.61%/5.76% WER/CER on Common Voice 6 (without special characters and punctuation)

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_hi_conformer_ctc_medium\")\n

Transcribing text with this model

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n    pretrained_name=\"stt_hi_conformer_ctc_medium \\\n    audio_dir=\"\" \\\n    dataset_manifest=\"\" \\\n    output_filename=\"\" \\\n    batch_size=32 \\\n    cuda=0 \\\n

Transcribing text using buffered/chunked streaming with this model

python [NEMO_GIT_FOLDER]/examples/asr/asr_chunked_inference/ctc/speech_to_text_buffered_infer_ctc.py \\\n --asr_model=\"stt_hi_conformer_ctc_medium\" \\\n --test_manifest=\"\" \\\n --output_path=\"\" \\\n --model_stride=4\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.

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 hindi characters along with spaces.\n\n# Model Architecture\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\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\nThe 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\n\n### Datasets\n\nAll the models in this collection are trained on ULCA Hindi Labelled Dataset (~1900 hrs)\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 --log\n ```\n\nPerformance\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 and 6-Gram KenLM trained on AI4Bharat Corpus.\n\nKenLM with 128 beam size with n_gram_alpha=1.5, n_gram_beta=2.0. \n- 12.90%/4.50% WER/CER on Interspeech MUCS 2021 Blind Test Set.\n- 14.48%/6.38% WER/CER on Interspeech MUCS 2021 Validation Set.\n- 14.82%/7.17% WER/CER on IIT-Madras Speech Challenge 2020 Evaluation Set.\n- 14.92%/7.28% WER/CER on IIT-Madras Speech Challenge 2020 Dev Set.\n- 16.50%/7.24% WER/CER on Common Voice 8 (without special characters and punctuation)\n- 15.69%/6.11% WER/CER on Common Voice 7 (without special characters and punctuation)\n- 17.17%/5.39% WER/CER on Common Voice 6 (without special characeters and punctuation)\n\nGreedy Decoding Scores:\n- 15.03%/5.06% WER/CER on Interspeech MUCS 2021 Blind Test Set.\n- 16.80%/7.07% WER/CER on Interspeech MUCS 2021 Validation Set.\n- 17.62%/7.45% WER/CER on IIT-Madras Speech Challenge 2020 Evaluation Set.\n- 17.58%/7.48% WER/CER on IIT-Madras Speech Challenge 2020 Dev Set.\n- 20.01%/8.17% WER/CER on Common Voice 8 (without special characters and punctuation)\n- 18.81%/6.93% WER/CER on Common Voice 7 (without special characters and punctuation)\n- 19.61%/5.76% WER/CER on Common Voice 6 (without special characters and punctuation)\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_hi_conformer_ctc_medium\")\n```\n\n### Transcribing text with this model\n```\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_hi_conformer_ctc_medium \\\n audio_dir=\"\" \\\n dataset_manifest=\"\" \\\n output_filename=\"\" \\\n batch_size=32 \\\n cuda=0 \\\n ```\n \n ### Transcribing text using buffered/chunked streaming with this model\n```\npython [NEMO_GIT_FOLDER]/examples/asr/asr_chunked_inference/ctc/speech_to_text_buffered_infer_ctc.py \\\n --asr_model=\"stt_hi_conformer_ctc_medium\" \\\n --test_manifest=\"\" \\\n --output_path=\"\" \\\n --model_stride=4\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## 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 Hi Conformer-CTC Medium",["D",1680635480713],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/stt_hi_conformer_ctc_medium","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes",6980157062,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[242,243,244,245,246],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_210":262,"_248":262,"_249":263,"_251":252},{"_210":258,"_248":259,"_249":260,"_251":252},{"_210":256,"_248":256,"_249":257,"_251":252},{"_210":254,"_248":254,"_249":255,"_251":252},{"_210":247,"_248":247,"_249":250,"_251":252},"STT","value","filter",{"_253":252,"_248":247},"category","label","field","PyTorchLightning",{"_253":252,"_248":254},"Hi",{"_253":252,"_248":256},"Conversational AI","soln_conversational_ai",{"_253":261,"_248":259},"technology","Conformer-CTC",{"_253":252,"_248":262},{"_123":1241,"_125":1242},"ngcApps",[1194],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1175":1176,"_1177":1178,"_1179":1180,"_1181":1182,"_1183":275,"_154":301,"_1184":1185,"_1186":59,"_1187":102,"_1188":102,"_1189":1190,"_1191":102,"_1192":1193},"amplitude",{"_1172":1173,"_814":-7,"_1174":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1166":1167,"_1168":1169,"_1170":1171},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787368436052,"1.12.0","defaultSearchSuggestions",{"_1054":382,"_1055":1056,"_1057":1058,"_1059":1060},"announcements",[],"consentState",{"_339":1044,"_1045":102,"_1046":34,"_1047":34,"_1048":1049},"consentConfig",{"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340},"showContactAdmin",1787368436053,[],[],[315,316,317,318,319,320],{"_48":63,"_50":74,"_52":118,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":118,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":68,"_52":118,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":118,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":118,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":118,"_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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