#Language Modeling

xLSTM is an extended Recurrent Neural Network architecture that addresses the limitations of traditional LSTMs. Utilizing Exponential Gating and a Matrix Memory design, it offers enhanced performance for language modeling and serves as a notable alternative to both Transformers and State Space Models. Compatible with PyTorch, xLSTM is straightforward to install and configure for diverse applications, with features like xLSTMBlockStack and xLSTMLMModel adaptable for various use cases in language processing without exaggeration in claims.

Discover advancements in language modeling using the pre-trained Big VAE model, aimed at effective sentence organization within a latent space. This model supports sentence interpolation, analogy, and includes updates like dataset fine-tuning and language generation guidance. Uncover low-resource language understanding potential, backed by a detailed codebase and comprehensive documentation. Experience interactive demos and in-depth result analysis to understand this model's impact.

This repository provides a curated collection of key research papers in speech recognition and synthesis, covering areas like Text-to-Audio, Automatic Speech Recognition (ASR), Speaker Verification, Voice Conversion (VC), and Speech Synthesis (TTS). It also delves into specialized topics including Language Modelling, Confidence Estimates, and Music Modelling. The compilation features foundational works and recent advancements, offering valuable insights for researchers and practitioners in the field of audio processing. This serves as an extensive knowledge base for understanding the evolution of techniques and applications influencing today's speech and audio processing developments.

LongRoPE extends the context window of large language models past 2 million tokens using non-uniform positional embeddings and a 256k fine-tuning strategy. This method sustains performance across various context lengths, supporting in-context learning and long document summarization.

The Mamba project provides an innovative state space model architecture designed for efficient handling of information-dense data such as language models, overcoming the shortcomings of earlier subquadratic models. Its architecture, focusing on hardware efficiency similar to FlashAttention, utilizes selective state space modeling for scalable solutions. Pretrained models are available on Hugging Face, and the `lm-evaluation-harness` library enables evaluations. Comprehensive resources include installation guides, usage instructions, and benchmarking scripts to support seamless integration and performance optimization.