SpeechBrain: A General-Purpose Speech Toolkit

By Mirco Ravanelli, Titouan Parcollet, Peter William VanHarn Plantinga, Aku Rouhe, Samuele Cornell, Loren Lugosch, Cem Subakan, Nauman Dawalatabad, A. Heba, Jianyuan Zhong and 11 more. 2021

999 citations recorded by the source corpus.

SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the research and development of neural speech processing technologies by being simple, flexible, user-friendly, and well-documented. This paper describes the core architecture designed to support several tasks of common interest, allowing users to naturally conceive, compare and share novel speech processing pipelines. SpeechBrain achieves competitive or state-of-the-art performance in a wide range of speech benchmarks. It also provides training recipes, pretrained models, and inference scripts for popular speech datasets, as well as tutorials which allow anyone with basic Python proficiency to familiarize themselves with speech technologies.

Subfields: Artificial Intelligence, Speech & Audio, ML Systems & Hardware, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Speech & Audio

In ML Systems & Hardware

In Signal Processing

Citations in the corpus

Powerset multi-class cross entropy loss for neural speaker diarization (2023, methodology): “All experiments reported in this paper rely on the pretrained ECAPA-TDNN model [19] from SpeechBrain [20] available at hf.”
Scaling Speech Technology to 1, 000+ Languages (2023, methodology): “11 Figure 4 shows that the forced alignment implementation scales much better to longer sequences than CPU alternatives such as ctc-segmentation [Kürzinger et al., 2020], a popular segmentation library used in ESPNet [Watanabe et al., 2018], SpeechBrain [Ravanelli et al., 2021] and Flashlight [Kahn ”
The SpeakIn System for VoxCeleb Speaker Recognition Challange 2021 (2021, methodology): “Recent researches [6, 7] mentioned a new method that augments data on the fly (online augmentation).”
Pyannote.audio 2.1 Speaker Diarization Pipeline: Principle, Benchmark, and Recipe (2023, methodology): “In particular, all experiments reported in Table 1 relies on the implementation of ECAPA-TDNN [19] available in SpeechBrain [20] because it was found to outperform three open-source alternatives.”
Recent Advances in End-to-End Automatic Speech Recognition (2021, background): “First of all, great toolkits [56, 227, 252, 297, 299, 310] are indispensable to the fast development of E2E ASR”
Wespeaker: A Research and Production Oriented Speaker Embedding Learning Toolkit (2022, background): “The key features/advantages of the Wespeaker toolkit are as follows, • Competitive results : Compared with other open-source implementations [9, 16], we achieve very competitive performance in all the recipes, including the VoxCeleb, CNCeleb, and VoxConverse.”