# ESPnet2-TTS: Extending the Edge of TTS Research

arXiv: https://arxiv.org/abs/2110.07840 · HTML: https://mlsanity.com/paper/2110.07840/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 51 references that are in the map.
- First posted on arXiv 15 Oct 2021.

## Builds on (most cited first, 30 of 32)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://mlsanity.com/paper/1910.11480/) (2019)
- [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://mlsanity.com/paper/1806.04558/) (2018)
- [Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://mlsanity.com/paper/1803.09017/) (2018)
- [NeMo: a toolkit for building AI applications using Neural Modules](https://mlsanity.com/paper/1909.09577/) (2019)
- [FastPitch: Parallel Text-to-speech with Pitch Prediction](https://mlsanity.com/paper/2006.06873/) (2020)
- [Recent Developments on ESPnet Toolkit Boosted by Conformer](https://mlsanity.com/paper/2010.13956/) (2020)
- [High Fidelity Speech Synthesis with Adversarial Networks](https://mlsanity.com/paper/1909.11646/) (2019)
- [Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion](https://mlsanity.com/paper/2008.12527/) (2020)
- [WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis](https://mlsanity.com/paper/2106.09660/) (2021)
- [Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech](https://mlsanity.com/paper/2005.05106/) (2020)
- [ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit](https://mlsanity.com/paper/1910.10909/) (2019)
- [JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis](https://mlsanity.com/paper/1711.00354/) (2017)
- [Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention](https://mlsanity.com/paper/1710.08969/) (2017)
- [Asteroid: the PyTorch-based audio source separation toolkit for researchers](https://mlsanity.com/paper/2005.04132/) (2020)
- [MultiSpeech: Multi-Speaker Text to Speech with Transformer](https://mlsanity.com/paper/2006.04664/) (2020)
- [Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis](https://mlsanity.com/paper/2002.03785/) (2020)
- [Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining](https://mlsanity.com/paper/1912.06813/) (2019)
- [StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization](https://mlsanity.com/paper/2011.01557/) (2020)
- [fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit](https://mlsanity.com/paper/2109.06912/) (2021)
- [Chainer: A Deep Learning Framework for Accelerating the Research Cycle](https://mlsanity.com/paper/1908.00213/) (2019)

## Built on by (most cited first, 22 of 22)

- [StyleTTS: A Style-Based Generative Model for Natural and Diverse Text-to-Speech Synthesis](https://mlsanity.com/paper/2205.15439/) (2022)
- [JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech](https://mlsanity.com/paper/2203.16852/) (2022)
- [A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech](https://mlsanity.com/paper/2302.04215/) (2023)
- [ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models](https://mlsanity.com/paper/2401.17230/) (2024)
- [Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features](https://mlsanity.com/paper/2203.03191/) (2022)
- [Exploring the Capability of Mamba in Speech Applications](https://mlsanity.com/paper/2406.16808/) (2024)
- [Low-Resource Multilingual and Zero-Shot Multispeaker TTS](https://mlsanity.com/paper/2210.12223/) (2022)
- [Exact Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech](https://mlsanity.com/paper/2206.12229/) (2022)
- [DiffVoice: Text-to-Speech with Latent Diffusion](https://mlsanity.com/paper/2304.11750/) (2023)
- [Structured State Space Decoder for Speech Recognition and Synthesis](https://mlsanity.com/paper/2210.17098/) (2022)
- [Speaker Anonymization with Phonetic Intermediate Representations](https://mlsanity.com/paper/2207.04834/) (2022)
- [Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining](https://mlsanity.com/paper/2301.12596/) (2023)
- [ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit](https://mlsanity.com/paper/2304.04596/) (2023)
- [Improving Cascaded Unsupervised Speech Translation with Denoising Back-translation](https://mlsanity.com/paper/2305.07455/) (2023)
- [Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness](https://mlsanity.com/paper/2404.06714/) (2024)
- [Lightweight End-to-end Text-to-speech Synthesis for low resource on-device applications](https://mlsanity.com/paper/2505.07701/) (2025)
- [Do Neural Codecs Generalize? A Controlled Study Across Unseen Languages and Non-Speech Tasks](https://mlsanity.com/paper/2601.12205/) (2026)
- [Empirical Study Incorporating Linguistic Knowledge on Filled Pauses for Personalized Spontaneous Speech Synthesis](https://mlsanity.com/paper/2210.07559/) (2022)
- [Enhancing Speech-to-Speech Translation with Multiple TTS Targets](https://mlsanity.com/paper/2304.04618/) (2023)
- [ESPnet-EZ: Python-only ESPnet for Easy Fine-tuning and Integration](https://mlsanity.com/paper/2409.09506/) (2024)
- [Voice Mapping of Text-to-Speech Systems: A Metric-Based Approach for Voice Quality Assessment](https://mlsanity.com/paper/2605.00861/) (2026)
- [TRILOGUE: A Trilingual Spoken Dialogue Fact-Checking Benchmark with Evidence and Paired Audio](https://mlsanity.com/paper/2609.04452/) (2026)

## Abstract (arXiv)

This paper describes ESPnet2-TTS, an end-to-end text-to-speech (E2E-TTS) toolkit. ESPnet2-TTS extends our earlier version, ESPnet-TTS, by adding many new features, including: on-the-fly flexible pre-processing, joint training with neural vocoders, and state-of-the-art TTS models with extensions like full-band E2E text-to-waveform modeling, which simplify the training pipeline and further enhance TTS performance. The unified design of our recipes enables users to quickly reproduce state-of-the-art E2E-TTS results. We also provide many pre-trained models in a unified Python interface for inference, offering a quick means for users to generate baseline samples and build demos. Experimental evaluations with English and Japanese corpora demonstrate that our provided models synthesize utterances comparable to ground-truth ones, achieving state-of-the-art TTS performance. The toolkit is available online at https://github.com/espnet/espnet.