{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nemo-a-toolkit-for-building-ai-applications","title":"NeMo: a toolkit for building AI applications using Neural Modules","arxiv_id":"1909.09577","date":"2019-09-14","proceeding":null,"authors":["Oleksii Kuchaiev","Jason Li","Huyen Nguyen","Oleksii Hrinchuk","Ryan Leary","Boris Ginsburg","Samuel Kriman","Stanislav Beliaev","Vitaly Lavrukhin","Jack Cook","Patrice Castonguay","Mariya Popova","Jocelyn Huang","Jonathan M. Cohen"],"abstract":"NeMo (Neural Modules) is a Python framework-agnostic toolkit for creating AI applications through re-usability, abstraction, and composition. NeMo is built around neural modules, conceptual blocks of neural networks that take typed inputs and produce typed outputs. Such modules typically represent data layers, encoders, decoders, language models, loss functions, or methods of combining activations. NeMo makes it easy to combine and re-use these building blocks while providing a level of semantic correctness checking via its neural type system. The toolkit comes with extendable collections of pre-built modules for automatic speech recognition and natural language processing. Furthermore, NeMo provides built-in support for distributed training and mixed precision on latest NVIDIA GPUs. NeMo is open-source https://github.com/NVIDIA/NeMo","url_abs":"https://arxiv.org/abs/1909.09577v1","url_pdf":"https://arxiv.org/pdf/1909.09577v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"nemo-a-toolkit-for-building-ai-applications","repo_url":"https://github.com/NVIDIA/NeMo","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"automatic-speech-recognition-2","task_name":"Automatic Speech Recognition"},{"task_slug":"automatic-speech-recognition","task_name":"Automatic Speech Recognition (ASR)"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/speech-recognition-on-common-voice-french","task":"Speech Recognition","dataset":"Common Voice French","model":"ConformerCTC-L (4-gram)","rank_in_archive_order":2,"of":8,"metrics":{"Test WER":"9.16%"},"uses_additional_data":true},{"leaderboard":"/sota/speech-recognition-on-common-voice-french","task":"Speech Recognition","dataset":"Common Voice French","model":"ConformerCTC-L (no-LM)","rank_in_archive_order":4,"of":8,"metrics":{"Test WER":"9.63%"},"uses_additional_data":true},{"leaderboard":"/sota/speech-recognition-on-common-voice-german","task":"Speech Recognition","dataset":"Common Voice German","model":"ConformerCTC-L (4-gram)","rank_in_archive_order":5,"of":14,"metrics":{"Test WER":"6.03%"},"uses_additional_data":true},{"leaderboard":"/sota/speech-recognition-on-common-voice-german","task":"Speech Recognition","dataset":"Common Voice German","model":"ConformerCTC-L (no LM)","rank_in_archive_order":9,"of":14,"metrics":{"Test WER":"6.68%"},"uses_additional_data":true},{"leaderboard":"/sota/speech-recognition-on-common-voice-spanish","task":"Speech Recognition","dataset":"Common Voice Spanish","model":"ConformerCTC-L (4-gram)","rank_in_archive_order":1,"of":8,"metrics":{"Test WER":"5.5%"},"uses_additional_data":true},{"leaderboard":"/sota/speech-recognition-on-common-voice-spanish","task":"Speech Recognition","dataset":"Common Voice Spanish","model":"ConformerCTC-L (no LM)","rank_in_archive_order":4,"of":8,"metrics":{"Test WER":"6.9%"},"uses_additional_data":true}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1909.09577","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}