mlsanityData to 7 Oct 2026

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

arXiv 1902.08295 ↗First posted 21 Feb 2019cs.LG stat.MLcited by 86

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Abstract (arXiv)

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models are composed of modular building blocks that are flexible and easily extensible, and experiment configurations are centralized and highly customizable. Distributed training and quantized inference are supported directly within the framework, and it contains existing implementations of a large number of utilities, helper functions, and the newest research ideas. Lingvo has been used in collaboration by dozens of researchers in more than 20 papers over the last two years. This document outlines the underlying design of Lingvo and serves as an introduction to the various pieces of the framework, while also offering examples of advanced features that showcase the capabilities of the framework.

Key facts. Cited by 86 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Feb 2019.

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Citing papers per year: 2020: 1; 2021: 29; 2022: 33; 2023: 16; 2024: 6; 2025: 1.

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  1. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,601
  2. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,287
  3. 2022CoCa: Contrastive Captioners are Image-Text Foundation Modelscited by 1,265
  4. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  5. 2021GLaM: Efficient Scaling of Language Models with Mixture-of-Expertscited by 890
  6. 2021SimVLM: Simple Visual Language Model Pretraining with Weak Supervisioncited by 645
  7. 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 404
  8. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
  9. 2022Scaling Up Models and Data with textttt5x and textttseqiocited by 187
  10. 2021Primer: Searching for Efficient Transformers for Language Modelingcited by 162
  11. 2022Self-supervised Learning with Random-projection Quantizer for Speech Recognitioncited by 158
  12. 2021Recent Advances in End-to-End Automatic Speech Recognitioncited by 138
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  1. 2023End-to-End Speech Recognition: A Surveycited by 127
  2. 2021High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metricscited by 104
  3. 2022CVSS Corpus and Massively Multilingual Speech-to-Speech Translationcited by 71
  4. 2021Translatotron 2: High-quality direct speech-to-speech translation with voice preservationcited by 69
  5. 2022Building Machine Translation Systems for the Next Thousand Languagescited by 69
  6. 2023Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inferencecited by 69
  7. 2021Scaling End-to-End Models for Large-Scale Multilingual ASRcited by 61
  8. 2022When does dough become a bagel? Analyzing the remaining mistakes on ImageNetcited by 60
  9. 2022VeLO: Training Versatile Learned Optimizers by Scaling Upcited by 57
  10. 2021Scalable Scene Flow from Point Clouds in the Real Worldcited by 49
  11. 2021WaveGuard: Understanding and Mitigating Audio Adversarial Examplescited by 39
  12. 2021Tied & Reduced RNN-T Decodercited by 35
  13. 2023MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methodscited by 33
  14. 2021Transformer Based Deliberation for Two-Pass Speech Recognitioncited by 29
  15. 2023From English to More Languages: Parameter-Efficient Model Reprogramming for Cross-Lingual Speech Recognitioncited by 23
  16. 2021Learning Word-Level Confidence For Subword End-to-End ASRcited by 22
  17. 20224-bit Conformer with Native Quantization Aware Training for Speech Recognitioncited by 22
  18. 2021Effective Sequence-to-Sequence Dialogue State Trackingcited by 20
  19. 2022Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translationcited by 20
  20. 2023Defending against Adversarial Audio via Diffusion Modelcited by 20
  21. 2021PyGlove: Symbolic Programming for Automated Machine Learningcited by 19
  22. 2022Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learningcited by 19
  23. 2022Textless Direct Speech-to-Speech Translation with Discrete Speech Representationcited by 19
  24. 2022ESB: A Benchmark For Multi-Domain End-to-End Speech Recognitioncited by 18
  25. 2021Revealing and Protecting Labels in Distributed Trainingcited by 17
  26. 2022E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASRcited by 17
  27. 2023Efficient Domain Adaptation for Speech Foundation Modelscited by 13
  28. 2021Self-supervised and Supervised Joint Training for Resource-rich Machine Translationcited by 12
  29. 2022Improving the fusion of acoustic and text representations in RNN-Tcited by 11
  30. 2021Exploring Targeted Universal Adversarial Perturbations to End-to-end ASR Modelscited by 10
  31. 2021Input Length Matters: Improving RNN-T and MWER Training for Long-form Telephony Speech Recognitioncited by 10
  32. 2022Localising In-Domain Adaptation of Transformer-Based Biomedical Language Modelscited by 10
  33. 2023ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkitcited by 10
  34. 2021Lookup-Table Recurrent Language Models for Long Tail Speech Recognitioncited by 9
  35. 2022Internal Language Model Estimation Through Explicit Context Vector Learning for Attention-based Encoder-decoder ASRcited by 9
  36. 2022A Language Agnostic Multilingual Streaming On-Device ASR Systemcited by 9
  37. 2021Assessing Reference-Free Peer Evaluation for Machine Translationcited by 8
  38. 2021Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Predictioncited by 8

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