Here at Speechmatics, audio is the lifeblood of everything we do, from training our models right through to crafting effective demos of our technology. One of the best examples of this is our Portal translation demo, which allows the user to see their speech translated into a number of languages in realtime. However, accessing media devices through the browser isn't straightforward. Browsers require the user to explicitly permit access to the media device, and to make things even more complicated, each browser engine has its own quirks that have to be handled. In this article, I'll walk through how we were able to provide a consistent and straightforward microphone access experience for our demos across all the major browsers and devices.

Ever since the release of the HuggingFace🤗 Transformers library, it has been incredibly simple to train, finetune and run state-of-the-art Transformer-based translation models. This has also accelerated the development of our recently launched Translation feature. However, deploying these models in a production setting on GPU servers is still not straightforward, so I want to share how we at Speechmatics were able to deploy a performant real-time translation service for more than 30 languages and open-sourced part of our solution in the process.

As Transformer models increase in size, the computational cost of running inference also grows. Many organisations now face the challenge of deploying state-of-the-art models in a cost-effective way.

If we know anything of machine learning in 2023, it is this: bigger is better. Give your model more data, parameters, and compute and success is (somewhat) guaranteed (Hoffmann et al., 2022).