The local speech-recognition models shipped with **SayIt**, an open-source voice typing app for Windows: press a shortcut, speak, and AI-cleaned text lands at your cursor. These seven files are **byte-identical re-uploads** of the handy-computer GGUF conversions, mirrored here so SayIt's model downloader does not depend on a third-party repository staying online. No requantization, no retraining, no modification of any kind. SayIt's entire local speech-recognition stack rests on two things they built and gave away: transcribe.cpp, the ggml-based C/C++ inference library (MIT), and the handy-computer model org, which converted these ASR models to GGUF and keeps a much larger catalog than the seven files here. Without those conversions there would be no local mode in SayIt at all — running SenseVoice, Fun-ASR, Qwen3-ASR, Parakeet and Nemotron on consumer hardware through one runtime is the difficult part, and it was already solved.

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DeepSeek-R1-Distill-Llama-8B-Q4_K_M-GGUF

ct-2

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wav2vec2-large-xlsr-japlmthufielta-ipa1000-ns

ctaguchi

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Geneformer

ctheodoris

Geneformer is a foundational transformer model pretrained on a large-scale corpus of human single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology. Geneformer is a foundational transformer model pretrained on a large-scale corpus of single cell transcriptomes representing a broad range of human tissues. Geneformer V1 was originally pretrained in June 2021 on Genecorpus-30M, a corpus comprised of ~30 million human single cell transcriptomes. We excluded cells with high mutational burdens (e.g. malignant cells and immortalized cell lines) that could lead to substantial network rewiring without companion genome sequencing to facilitate interpretation. The current updated Geneformer V2 is pretrained on ~104 million human single cell transcriptomes (non-cancer) from Genecorpus-104M. The cancer continual learning V2 variant was continually pretrained on ~14 million cancer transcriptomes to yield a cancer domain-tuned model. Each single cell’s transcriptome is presented to the model as a rank value encoding where genes are ranked by their expression in that cell scaled by their expression across the entire Genecorpus (~30M for V1, ~104M for V2). The rank value encoding provides a nonparametric representation of that cell’s transcriptome and takes advantage of the many observations of each gene’s expression across the pretraining corpus to prioritize genes that distinguish cell state. Specifically, this method will deprioritize ubiquitously highly-expressed housekeeping genes by scaling them to a lower rank. Conversely, genes such as transcription factors that may be lowly expressed when they are expressed but highly distinguish cell state will move to a higher rank within the encoding. Furthermore, this rank-based approach may be more robust against technical artifacts that may systematically bias the absolute transcript counts value while the overall relative ranking of genes within each cell remains more stable.

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