# NeurST: Neural Speech Translation Toolkit

arXiv: https://arxiv.org/abs/2012.10018 · HTML: https://mlsanity.com/paper/2012.10018/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 23 of 23)

- [Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis](https://mlsanity.com/paper/2304.04675/) (2023)
- [Cross-modal Contrastive Learning for Speech Translation](https://mlsanity.com/paper/2205.02444/) (2022)
- [End-to-end Speech Translation via Cross-modal Progressive Training](https://mlsanity.com/paper/2104.10380/) (2021)
- [Learning When to Translate for Streaming Speech](https://mlsanity.com/paper/2109.07368/) (2021)
- [Revisiting End-to-End Speech-to-Text Translation From Scratch](https://mlsanity.com/paper/2206.04571/) (2022)
- [Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks](https://mlsanity.com/paper/2105.00573/) (2021)
- [CMOT: Cross-modal Mixup via Optimal Transport for Speech Translation](https://mlsanity.com/paper/2305.14635/) (2023)
- [GigaST: A 10,000-hour Pseudo Speech Translation Corpus](https://mlsanity.com/paper/2204.03939/) (2022)
- [DUB: Discrete Unit Back-translation for Speech Translation](https://mlsanity.com/paper/2305.11411/) (2023)
- [ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit](https://mlsanity.com/paper/2304.04596/) (2023)
- [Non-autoregressive End-to-end Speech Translation with Parallel Autoregressive Rescoring](https://mlsanity.com/paper/2109.04411/) (2021)
- [Fast-MD: Fast Multi-Decoder End-to-End Speech Translation with Non-Autoregressive Hidden Intermediates](https://mlsanity.com/paper/2109.12804/) (2021)
- [CTC-based Non-autoregressive Speech Translation](https://mlsanity.com/paper/2305.17358/) (2023)
- [End-to-End Speech-to-Text Translation: A Survey](https://mlsanity.com/paper/2312.01053/) (2023)
- [Controlling Styles in Neural Machine Translation with Activation Prompt](https://mlsanity.com/paper/2212.08909/) (2022)
- [The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights](https://mlsanity.com/paper/2405.01345/) (2024)
- [On the Impact of Noises in Crowd-Sourced Data for Speech Translation](https://mlsanity.com/paper/2206.13756/) (2022)
- [The Volctrans Neural Speech Translation System for IWSLT 2021](https://mlsanity.com/paper/2105.07319/) (2021)
- [Visual Information Matters for ASR Error Correction](https://mlsanity.com/paper/2303.10160/) (2023)
- [Efficient CTC Regularization via Coarse Labels for End-to-End Speech Translation](https://mlsanity.com/paper/2302.10871/) (2023)
- [Conditioning LLMs with Emotion in Neural Machine Translation](https://mlsanity.com/paper/2408.03150/) (2024)
- [The IWSLT 2021 BUT Speech Translation Systems](https://mlsanity.com/paper/2107.06155/) (2021)
- [JoeyS2T: Minimalistic Speech-to-Text Modeling with JoeyNMT](https://mlsanity.com/paper/2210.02545/) (2022)

## Abstract (arXiv)

NeurST is an open-source toolkit for neural speech translation. The toolkit mainly focuses on end-to-end speech translation, which is easy to use, modify, and extend to advanced speech translation research and products. NeurST aims at facilitating the speech translation research for NLP researchers and building reliable benchmarks for this field. It provides step-by-step recipes for feature extraction, data preprocessing, distributed training, and evaluation. In this paper, we will introduce the framework design of NeurST and show experimental results for different benchmark datasets, which can be regarded as reliable baselines for future research. The toolkit is publicly available at https://github.com/bytedance/neurst/ and we will continuously update the performance of NeurST with other counterparts and studies at https://st-benchmark.github.io/.