# PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit

arXiv: https://arxiv.org/abs/2205.12007 · HTML: https://mlsanity.com/paper/2205.12007/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2022.

## Built on by (most cited first, 9 of 9)

- [FunASR: A Fundamental End-to-End Speech Recognition Toolkit](https://mlsanity.com/paper/2305.11013/) (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)
- [VoiceMark: Zero-Shot Voice Cloning-Resistant Watermarking Approach Leveraging Speaker-Specific Latents](https://mlsanity.com/paper/2505.21568/) (2025)
- [SpeeChain: A Speech Toolkit for Large-Scale Machine Speech Chain](https://mlsanity.com/paper/2301.02966/) (2023)
- [Transcribing Educational Videos Using Whisper: A preliminary study on using AI for transcribing educational videos](https://mlsanity.com/paper/2307.03200/) (2023)
- [Looking and Listening: Audio Guided Text Recognition](https://mlsanity.com/paper/2306.03482/) (2023)
- [Improving the Generalization Ability in Essay Coherence Evaluation through Monotonic Constraints](https://mlsanity.com/paper/2308.02506/) (2023)
- [FreeFlow: A Comprehensive Understanding on Diffusion Probabilistic Models via Optimal Transport](https://mlsanity.com/paper/2312.05486/) (2023)
- [AudioFace: Language-Assisted Speech-Driven Facial Animation with Multimodal Language Models](https://mlsanity.com/paper/2605.07478/) (2026)

## Abstract (arXiv)

PaddleSpeech is an open-source all-in-one speech toolkit. It aims at facilitating the development and research of speech processing technologies by providing an easy-to-use command-line interface and a simple code structure. This paper describes the design philosophy and core architecture of PaddleSpeech to support several essential speech-to-text and text-to-speech tasks. PaddleSpeech achieves competitive or state-of-the-art performance on various speech datasets and implements the most popular methods. It also provides recipes and pretrained models to quickly reproduce the experimental results in this paper. PaddleSpeech is publicly avaiable at https://github.com/PaddlePaddle/PaddleSpeech.