This paper presents a new open-source web application for simultaneous speech-to-text translation. The system translates live Estonian speech into English, Russian, and Ukrainian text, and also supports English-to-Estonian translation. Our solution uses a cascaded architecture that combines streaming speech recognition with a recently proposed LLM-based simultaneous translation model. The LLM treats translation as a conversation, processing input in small five-word chunks. Our streaming speech recognition achieves a word error rate of 10.2% and a BLEU score of 26.1 for Estonian-to-English, significantly outperforming existing streaming solutions. The application is designed for real-world use, featuring a latency of only 3 - 6 seconds. The application is available at https://est2eng.vercel.app.

Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to limitations in parallelization and scalability. We propose a novel model architecture, Receptance Weighted Key Value (RWKV), that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, thus parallelizing computations during training and maintains constant computational and memory complexity during inference. We scale our models as large as 14 billion parameters, by far the largest dense RNN ever trained, and find RWKV performs on par with similarly sized Transformers, suggesting future work can leverage this architecture to create more efficient models. This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.