# EURO: ESPnet Unsupervised ASR Open-source Toolkit

arXiv: https://arxiv.org/abs/2211.17196 · HTML: https://mlsanity.com/paper/2211.17196/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 41 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://mlsanity.com/paper/1912.07875/) (2019)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech](https://mlsanity.com/paper/2007.06028/) (2020)
- [Unsupervised Speech Recognition](https://mlsanity.com/paper/2105.11084/) (2021)
- [Recent Developments on ESPnet Toolkit Boosted by Conformer](https://mlsanity.com/paper/2010.13956/) (2020)
- [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://mlsanity.com/paper/2005.03191/) (2020)
- [Iterative Pseudo-Labeling for Speech Recognition](https://mlsanity.com/paper/2005.09267/) (2020)
- [Flashlight: Enabling Innovation in Tools for Machine Learning](https://mlsanity.com/paper/2201.12465/) (2022)
- [Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching](https://mlsanity.com/paper/1812.09323/) (2018)
- [Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yolox'ochitl Mixtec](https://mlsanity.com/paper/2101.10877/) (2021)
- [Learning pronunciation from a foreign language in speech synthesis networks](https://mlsanity.com/paper/1811.09364/) (2018)
- [A Parallelizable Lattice Rescoring Strategy with Neural Language Models](https://mlsanity.com/paper/2103.05081/) (2021)

## Built on by (most cited first, 5 of 5)

- [Learning from Flawed Data: Weakly Supervised Automatic Speech Recognition](https://mlsanity.com/paper/2309.15796/) (2023)
- [REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR](https://mlsanity.com/paper/2402.03988/) (2024)
- [Towards Unsupervised Speech Recognition Without Pronunciation Models](https://mlsanity.com/paper/2406.08380/) (2024)
- [What happens to diffusion model likelihood when your model is conditional?](https://mlsanity.com/paper/2409.06364/) (2024)
- [ESPnet-EZ: Python-only ESPnet for Easy Fine-tuning and Integration](https://mlsanity.com/paper/2409.09506/) (2024)

## Abstract (arXiv)

This paper describes the ESPnet Unsupervised ASR Open-source Toolkit (EURO), an end-to-end open-source toolkit for unsupervised automatic speech recognition (UASR). EURO adopts the state-of-the-art UASR learning method introduced by the Wav2vec-U, originally implemented at FAIRSEQ, which leverages self-supervised speech representations and adversarial training. In addition to wav2vec2, EURO extends the functionality and promotes reproducibility for UASR tasks by integrating S3PRL and k2, resulting in flexible frontends from 27 self-supervised models and various graph-based decoding strategies. EURO is implemented in ESPnet and follows its unified pipeline to provide UASR recipes with a complete setup. This improves the pipeline's efficiency and allows EURO to be easily applied to existing datasets in ESPnet. Extensive experiments on three mainstream self-supervised models demonstrate the toolkit's effectiveness and achieve state-of-the-art UASR performance on TIMIT and LibriSpeech datasets. EURO will be publicly available at https://github.com/espnet/espnet, aiming to promote this exciting and emerging research area based on UASR through open-source activity.