Icefall

Icefall is a core component of the Next-gen Kaldi project, serving as a comprehensive collection of recipes for speech-related tasks including Automatic Speech Recognition (ASR), Speaker Identification, and Keyword Spotting. Built atop the k2 framework and PyTorch, Icefall transitions the traditional Finite State Transducer (FST) approach of Kaldi into a modern, differentiable framework. Its technical architecture focuses on efficiency through the 'pruned RNN-T' and 'Zipformer' models, which significantly reduce computational overhead while maintaining state-of-the-art accuracy. By 2026, Icefall has positioned itself as the industry standard for researchers and engineers who require high-performance, customizable speech models that can be deployed on both cloud infrastructure and edge devices via its companion inference engine, Sherpa. It bridges the gap between academic research and production-grade deployments by providing reproducible scripts for massive datasets like LibriSpeech, Gigaspeech, and WenetSpeech, supporting both streaming and non-streaming applications with low-latency requirements.