# HiFiTTS-2: A Large-Scale High Bandwidth Speech Dataset

arXiv: https://arxiv.org/abs/2506.04152 · HTML: https://mlsanity.com/paper/2506.04152/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2025.

## Built on by (most cited first, 10 of 10)

- [VoXtream: Full-Stream Text-to-Speech with Extremely Low Latency](https://mlsanity.com/paper/2509.15969/) (2025)
- [VoXtream2: Full-stream TTS with dynamic speaking rate control](https://mlsanity.com/paper/2603.13518/) (2026)
- [VoiceChat-TTS: A Low-Latency Continuous Speech Synthesis Model for Interactive Agents](https://mlsanity.com/paper/2608.13831/) (2026)
- [High-Fidelity Speech Enhancement via Discrete Audio Tokens](https://mlsanity.com/paper/2510.02187/) (2025)
- [HoliTok: A Continuous Holistic Tokenization with Robust Dual Capabilities of Speech Generation and Understanding](https://mlsanity.com/paper/2605.29948/) (2026)
- [Post-Training Speech Enhancement Language Models with Perceptual Rewards](https://mlsanity.com/paper/2606.21458/) (2026)
- [VoiceDesigner: Text-to-Voice Generation and Editing via Unified Diffusion Modeling and Data Augmentation](https://mlsanity.com/paper/2608.13613/) (2026)
- [ZipL-Dialog: Memory-Efficient Long-Form Spoken Dialog Synthesis via Latent Flow Matching](https://mlsanity.com/paper/2607.12496/) (2026)
- [LILAC: An Idempotent Neural Speech Codec](https://mlsanity.com/paper/2608.05727/) (2026)
- [Scalable Direction-Following TTS via Voice Impression-Guided Pseudo Triplet Construction](https://mlsanity.com/paper/2609.02623/) (2026)

## Abstract (arXiv)

This paper introduces HiFiTTS-2, a large-scale speech dataset designed for high-bandwidth speech synthesis. The dataset is derived from LibriVox audiobooks, and contains approximately 36.7k hours of English speech for 22.05 kHz training, and 31.7k hours for 44.1 kHz training. We present our data processing pipeline, including bandwidth estimation, segmentation, text preprocessing, and multi-speaker detection. The dataset is accompanied by detailed utterance and audiobook metadata generated by our pipeline, enabling researchers to apply data quality filters to adapt the dataset to various use cases. Experimental results demonstrate that our data pipeline and resulting dataset can facilitate the training of high-quality, zero-shot text-to-speech (TTS) models at high bandwidths.