# Hi-Fi Multi-Speaker English TTS Dataset

arXiv: https://arxiv.org/abs/2104.01497 · HTML: https://mlsanity.com/paper/2104.01497/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2021.

## Built on by (most cited first, 30 of 60)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance](https://mlsanity.com/paper/2111.11755/) (2021)
- [Better speech synthesis through scaling](https://mlsanity.com/paper/2305.07243/) (2023)
- [Koel-TTS: Enhancing LLM based Speech Generation with Preference Alignment and Classifier Free Guidance](https://mlsanity.com/paper/2502.05236/) (2025)
- [AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement](https://mlsanity.com/paper/2501.15417/) (2025)
- [Improving Robustness of LLM-based Speech Synthesis by Learning Monotonic Alignment](https://mlsanity.com/paper/2406.17957/) (2024)
- [WavThruVec: Latent speech representation as intermediate features for neural speech synthesis](https://mlsanity.com/paper/2203.16930/) (2022)
- [Low-Resource Multilingual and Zero-Shot Multispeaker TTS](https://mlsanity.com/paper/2210.12223/) (2022)
- [NanoCodec: Towards High-Quality Ultra Fast Speech LLM Inference](https://mlsanity.com/paper/2508.05835/) (2025)
- [ClArTTS: An Open-Source Classical Arabic Text-to-Speech Corpus](https://mlsanity.com/paper/2303.00069/) (2023)
- [Exact Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech](https://mlsanity.com/paper/2206.12229/) (2022)
- [Adapting TTS models For New Speakers using Transfer Learning](https://mlsanity.com/paper/2110.05798/) (2021)
- [Low Frame-rate Speech Codec: a Codec Designed for Fast High-quality Speech LLM Training and Inference](https://mlsanity.com/paper/2409.12117/) (2024)
- [Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers](https://mlsanity.com/paper/2211.00585/) (2022)
- [RFWave: Multi-band Rectified Flow for Audio Waveform Reconstruction](https://mlsanity.com/paper/2403.05010/) (2024)
- [Meta Learning Text-to-Speech Synthesis in over 7000 Languages](https://mlsanity.com/paper/2406.06403/) (2024)
- [UniSS: Unified Expressive Speech-to-Speech Translation with Your Voice](https://mlsanity.com/paper/2509.21144/) (2025)
- [mdctGAN: Taming transformer-based GAN for speech super-resolution with Modified DCT spectra](https://mlsanity.com/paper/2305.11104/) (2023)
- [The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning](https://mlsanity.com/paper/2603.17837/) (2026)
- [A Toolbox for Construction and Analysis of Speech Datasets](https://mlsanity.com/paper/2104.04896/) (2021)
- [RefineGAN: Universally Generating Waveform Better than Ground Truth with Highly Accurate Pitch and Intensity Responses](https://mlsanity.com/paper/2111.00962/) (2021)
- [Distribution augmentation for low-resource expressive text-to-speech](https://mlsanity.com/paper/2202.06409/) (2022)
- [PoeticTTS -- Controllable Poetry Reading for Literary Studies](https://mlsanity.com/paper/2207.05549/) (2022)
- [Revisiting IPA-based Cross-lingual Text-to-speech](https://mlsanity.com/paper/2110.07187/) (2021)
- [Voice Cloning: Comprehensive Survey](https://mlsanity.com/paper/2505.00579/) (2025)
- [StableToken: A Noise-Robust Semantic Speech Tokenizer for Resilient SpeechLLMs](https://mlsanity.com/paper/2509.22220/) (2025)
- [VoiceBridge: General Speech Restoration with One-step Latent Bridge Models](https://mlsanity.com/paper/2509.25275/) (2025)
- [Large-scale unsupervised audio pre-training for video-to-speech synthesis](https://mlsanity.com/paper/2306.15464/) (2023)
- [Sound Check: Auditing Audio Datasets](https://mlsanity.com/paper/2410.13114/) (2024)
- [VoxServe: Streaming-Centric Serving System for Speech Language Models](https://mlsanity.com/paper/2602.00269/) (2026)

## Abstract (arXiv)

This paper introduces a new multi-speaker English dataset for training text-to-speech models. The dataset is based on LibriVox audiobooks and Project Gutenberg texts, both in the public domain. The new dataset contains about 292 hours of speech from 10 speakers with at least 17 hours per speaker sampled at 44.1 kHz. To select speech samples with high quality, we considered audio recordings with a signal bandwidth of at least 13 kHz and a signal-to-noise ratio (SNR) of at least 32 dB. The dataset is publicly released at http://www.openslr.org/109/ .