Model Overview

This collection contains ContextNet-1024 (around 140M parameters) trained on German NeMo ASRSet with over 2000 hours of German speech.

It utilizes a Google SentencePiece [1] tokenizer with vocabulary size 1024, and transcribes text in lower case German alphabet along with spaces without any punctuation.

Model Architecture

ContextNet [2] model is an autoregressive, transducer based Automatic Speech Recognition model. You may find more info on the detail of this model here: ContextNet 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] (https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/contextnet_rnnt/contextnet_rnnt.yaml).

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 over two thousand hours of cleaned German speech:

MCV7.0 567 hours
MLS 1524 hours
VoxPopuli 214 hours

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 :

python [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \\\n --manifest=\"train_manifest.json\" \\\n --data_root=\"\" \\\n --vocab_size=1024 \\\n --tokenizer=\"spe\" \\\n --spe_type=\"unigram\" \\\n --spe_character_coverage=1.0 \\\n --no_lower_case \\\n --log\n

Performance

The performance of Automatic Speech Recognition models is measuring using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.

The latest model obtains the following greedy scores on the following evaluation datasets -

4.76 % on MCV7.0 dev
5.5 % on MCV7.0 test
3.53 % on MLS dev
4.2 % on MLS test
11.32 % on Vox Populi dev
9.4 % on Vox Populi test

Note that these evaluation datasets have been filtered and preprocessed to only contain German alphabet characters and are removed of 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.EncDecRNNTBPEModel.from_pretrained(model_name=\"stt_de_contextnet_1024\")\n

Transcribing text with this model

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_de_contextnet_1024\" \\\n audio_dir=\"\"\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] Google Sentencepiece Tokenizer

[2] ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

[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: [ContextNet Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html).\n\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/speech_to_text_rnnt_bpe.py) and this [base config] (https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/contextnet_rnnt/contextnet_rnnt.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 over two thousand hours of cleaned German speech:\n\n- MCV7.0 567 hours\n- MLS 1524 hours\n- VoxPopuli 214 hours\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=1024 \\\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 performance of Automatic Speech Recognition models is measuring using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.\n\nThe latest model obtains the following greedy scores on the following evaluation datasets -\n\n- 4.76 % on `MCV7.0 dev`\n- 5.5 % on `MCV7.0 test`\n- 3.53 % on `MLS dev`\n- 4.2 % on `MLS test`\n- 11.32 % on `Vox Populi dev`\n- 9.4 % on `Vox Populi test`\n\nNote that these evaluation datasets have been filtered and preprocessed to only contain German alphabet characters and are removed of punctuation.\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.EncDecRNNTBPEModel.from_pretrained(model_name=\"stt_de_contextnet_1024\")\n```\n\n## Transcribing text with this model\n\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \\\n pretrained_name=\"stt_de_contextnet_1024\" \\\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] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)\n\n[2] [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://arxiv.org/abs/2005.03191)\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 De ContextNet 1024",["D",1680634857357],"/api/image/nvidia/nemo/models/stt_de_contextnet_1024","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","bias","explainability","privacy","safetyAndSecurity",[],[310,311,312,313],[],[],[],{"_91":324,"_139":325,"_140":326,"_142":143},{"_91":321,"_139":322,"_140":323,"_142":143},{"_91":318,"_139":319,"_140":320,"_142":143},{"_91":314,"_139":315,"_140":316,"_142":143},"DL","soln_dl",{"_144":317,"_139":315},"technology","Conversational AI","soln_conversational_ai",{"_144":317,"_139":319},"Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_144":150,"_139":322},"AI","soln_ai",{"_144":317,"_139":325},{"_205":1498,"_207":1499},"ngcApps",[1451],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1432":1433,"_1434":1435,"_1436":1437,"_1438":1439,"_1440":338,"_236":362,"_1441":1442,"_1443":51,"_1444":184,"_1445":184,"_1446":1447,"_1448":184,"_1449":1450},"amplitude",{"_1429":1430,"_1035":-7,"_1431":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1423":1424,"_1425":1426,"_1427":1428},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1310":443,"_1311":1312,"_1313":1314,"_1315":1316},"announcements",[],"consentState",{"_400":1300,"_1301":184,"_1302":26,"_1303":26,"_1304":1305},"consentConfig",{"_382":383,"_384":385,"_386":387,"_388":389,"_390":391,"_392":393,"_394":395,"_396":397,"_398":399,"_400":401},"showContactAdmin",1784000172500,[],[],[376,377,378,379,380],{"_40":55,"_42":66,"_44":200,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":200,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":200,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":200,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":381,"_50":51},7,"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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