# GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement

arXiv: https://arxiv.org/abs/2406.11546 · HTML: https://mlsanity.com/paper/2406.11546/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 57 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 30 of 46)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Scaling Vision Transformers to 22 Billion Parameters](https://mlsanity.com/paper/2302.05442/) (2023)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning](https://mlsanity.com/paper/1609.06773/) (2016)
- [Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages](https://mlsanity.com/paper/2303.01037/) (2023)
- [WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition](https://mlsanity.com/paper/2110.03370/) (2021)
- [Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2010.10504/) (2020)
- [Improved Noisy Student Training for Automatic Speech Recognition](https://mlsanity.com/paper/2005.09629/) (2020)
- [AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale](https://mlsanity.com/paper/1808.10583/) (2018)
- [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020)
- [Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters](https://mlsanity.com/paper/2007.03001/) (2020)
- [The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage](https://mlsanity.com/paper/2111.09344/) (2021)

## Built on by (most cited first, 30 of 34)

- [OWSM v4: Improving Open Whisper-Style Speech Models via Data Scaling and Cleaning](https://mlsanity.com/paper/2506.00338/) (2025)
- [OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models](https://mlsanity.com/paper/2604.00688/) (2026)
- [WAXAL: A Large-Scale Multilingual African Language Speech Corpus](https://mlsanity.com/paper/2602.02734/) (2026)
- [Pseudo-Autoregressive Neural Codec Language Models for Efficient Zero-Shot Text-to-Speech Synthesis](https://mlsanity.com/paper/2504.10352/) (2025)
- [Fish Audio S2 Technical Report](https://mlsanity.com/paper/2603.08823/) (2026)
- [Dolphin: A Large-Scale Automatic Speech Recognition Model for Eastern Languages](https://mlsanity.com/paper/2503.20212/) (2025)
- [SeaLLMs-Audio: Large Audio-Language Models for Southeast Asia](https://mlsanity.com/paper/2511.01670/) (2025)
- [SLAM-LLM: A Modular, Open-Source Multimodal Large Language Model Framework and Best Practice for Speech, Language, Audio and Music Processing](https://mlsanity.com/paper/2601.09385/) (2026)
- [Uni-ASR: Unified LLM-Based Architecture for Non-Streaming and Streaming Automatic Speech Recognition](https://mlsanity.com/paper/2603.11123/) (2026)
- [EuroSpeech: A Multilingual Speech Corpus](https://mlsanity.com/paper/2510.00514/) (2025)
- [X-Voice: Enabling Everyone to Speak 30 Languages via Zero-Shot Cross-Lingual Voice Cloning](https://mlsanity.com/paper/2605.05611/) (2026)
- [VietASR: Achieving Industry-level Vietnamese ASR with 50-hour labeled data and Large-Scale Speech Pretraining](https://mlsanity.com/paper/2505.21527/) (2025)
- [UDDETTS: Unifying Discrete and Dimensional Emotions for Controllable Emotional Text-to-Speech](https://mlsanity.com/paper/2505.10599/) (2025)
- [Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices](https://mlsanity.com/paper/2509.02523/) (2025)
- [Enhancing Multilingual LLM-based ASR with Mixture of Experts and Dynamic Downsampling](https://mlsanity.com/paper/2606.10439/) (2026)
- [A Comparative Study of LLM-based ASR and Whisper in Low Resource and Code Switching Scenario](https://mlsanity.com/paper/2412.00721/) (2024)
- [ILT-Iterative LoRA Training through Focus-Feedback-Fix for Multilingual Speech Recognition](https://mlsanity.com/paper/2507.08477/) (2025)
- [Triple X: A LLM-Based Multilingual Speech Recognition System for the INTERSPEECH2025 MLC-SLM Challenge](https://mlsanity.com/paper/2507.17288/) (2025)
- [JaiTTS: A Thai Voice Cloning Model](https://mlsanity.com/paper/2604.27607/) (2026)
- [WorldSpeech: A Multilingual Speech Corpus from Around the World](https://mlsanity.com/paper/2605.09167/) (2026)
- [PolySpeech-100: A Large-Scale Benchmark for Speech Understanding Across 100+ Languages and Dialects](https://mlsanity.com/paper/2606.01016/) (2026)
- [Building and Evaluating Fixed-Voice Thai TTS from Synthetic Speech](https://mlsanity.com/paper/2609.03502/) (2026)
- [Empowering Global Voices: A Data-Efficient, Phoneme-Tone Adaptive Approach to High-Fidelity Speech Synthesis](https://mlsanity.com/paper/2504.07858/) (2025)
- [NTU Speechlab LLM-Based Multilingual ASR System for Interspeech MLC-SLM Challenge 2025](https://mlsanity.com/paper/2506.13339/) (2025)
- [Transsion Multilingual Speech Recognition System for MLC-SLM 2025 Challenge](https://mlsanity.com/paper/2508.14916/) (2025)
- [VietNormalizer: An Open-Source, Dependency-Free Python Library for Vietnamese Text Normalization in TTS and NLP Applications](https://mlsanity.com/paper/2603.04145/) (2026)
- [Sommelier: Scalable Open Multi-turn Audio Pre-processing for Full-duplex Speech Language Models](https://mlsanity.com/paper/2603.25750/) (2026)
- [AfriVox-v2: A Domain-Verticalized Benchmark for In-the-Wild African Speech Recognition](https://mlsanity.com/paper/2605.03590/) (2026)
- [Optimality of FSQ Tokens for Continuous Diffusion for Categorical Data with Application to Text-to-Speech](https://mlsanity.com/paper/2606.09962/) (2026)
- [GigaAM Multilingual: Foundation Model for Underrepresented Languages](https://mlsanity.com/paper/2607.10371/) (2026)

## Abstract (arXiv)

The evolution of speech technology has been spurred by the rapid increase in dataset sizes. Traditional speech models generally depend on a large amount of labeled training data, which is scarce for low-resource languages. This paper presents GigaSpeech 2, a large-scale, multi-domain, multilingual speech recognition corpus. It is designed for low-resource languages and does not rely on paired speech and text data. GigaSpeech 2 comprises about 30,000 hours of automatically transcribed speech, including Thai, Indonesian, and Vietnamese, gathered from unlabeled YouTube videos. We also introduce an automated pipeline for data crawling, transcription, and label refinement. Specifically, this pipeline involves Whisper for initial transcription, MMS for forced alignment, and multi-dimensional filtering for data quality assurance. A modified Noisy Student Training is developed to further refine flawed pseudo labels iteratively, thereby enhancing model performance. Experimental results on our manually transcribed evaluation set and two public test sets from Common Voice and FLEURS confirm our corpus's high quality and broad applicability. Notably, ASR models trained on GigaSpeech 2 can reduce the word error rate for Thai, Indonesian, and Vietnamese on our challenging and realistic YouTube test set by 25% to 40% compared to Whisper large-v3, with merely 10% model parameters. Furthermore, our ASR models trained on GigaSpeech 2 yield superior performance compared to commercial services. We hope that our newly introduced corpus and pipeline will open a new avenue for low-resource speech recognition and significantly facilitate research in this area.