Deep learning models and datasets are becoming increasingly large for a variety of tasks. It is critical to train models in a timely manner, especially in business, where we want to experiment swiftly. Furthermore, for both economic and environmental reasons, maximizing the usage of available technology during training is critical. In this article, we will look at various approaches for shortening learning time and making better use of computing resources. In particular OVHcloud AI Training provides a GPU cluster platform.

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.

SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch. The goal is to create a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech technologies, including systems for speech recognition, speaker recognition, speech enhancement, multi-microphone signal processing and many others. SpeechBrain is currently in beta. The recipes released with speechbrain implement speech processing systems with competitive or state-of-the-art performance. For more details, take a look into the corresponding implementation in recipes/dataset/.

"Anytime you're listening to high-quality audio, you're likely using Dolby," declared Vivek Kumar, who heads the AI team for Dolby Labs. Speaking at the PyTorch DevCon event late last year, Kumar briefly spoke about how PyTorch has become the go-to tool for deep learning-based audio research. According to Kumar, there are nearly 11 billion devices that use Dolby services. Let us take a look at how PyTorch became the pick of tools for such an ambitious, yet personal service like audio. The main advantage that is often accredited to PyTorch is its flexibility.

We are happy to announce the SpeechBrain project, that aims to develop an open-source and all-in-one toolkit based on PyTorch. The goal is to develop a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech systems for speech recognition (both end-to-end and HMM-DNN), speaker recognition, speech separation, multi-microphone signal processing (e.g, beamforming), self-supervised learning, and many others. The project will be led by Mila (Montréal) and is sponsored by Samsung, Nvidia, and Dolby. SpeechBrain will also benefit from the collaboration and expertise of other partners such as Avignon Université, Facebook/PyTorch, IBM Research, and Fluent.ai. Reddit is an awesome place to discuss, so please, let us know what you would like to see implemented for the speech community!