Managing the HCLG Graph: Optimizing WFSTs in Kaldi
Learn how Kaldi uses WFST composition to build HCLG graphs, the role of determinization and minimization, and how to manage the memory trade-offs of large language models.
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kaldiHMM Graph Compatibility Transition to End-to-End Neural Architectures
Kaldi maintains a distinct boundary between legacy HMM-based models and modern end-to-end neural architectures. Each approach requires a specific graph-processing pipeline to ensure that the feature extractor and the decoder are aligned. When transitioning a deployment from HMM-based decoding to neural architectures, the configuration of the scf file and the