E2e Keyword Spotting v3

--- library_name: pytorch tags: - keyword-spotting - speech-commands - audio-classification - apple-silicon - pytorch license: mit --- # E2E Keyword Spotting v3 Assets This repository contains the public v3 assets for https://github.com/bozliu/E2E-Keyword-Spotting. ## What is included - `best_kws12_v2_cpu_baseline.pt`: our own detector checkpoint for the stable CPU baseline - `keyword_calibration_v2_cpu_baseline.json`: keyword calibration for that checkpoint - `release_summary_v3.json`: cleaned release summary used by the public README - `self_benchmark_v3.json`: self-benchmark comparison table used by the public README ## Important note about v3 The strongest `v3` path in the public release is an **accuracy-first composed runtime** on Apple MPS. It combines: - our detector gate/runtime packaging from this repository - upstream Hugging Face models `superb/wav2vec2-base-superb-ks` and `MIT/ast-finetuned-speech-commands-v2` - our local mapping, calibration, and realtime validation logic This means the best public `v3` result is **not** represented by one newly-trained monolithic checkpoint in this repo. Instead, this HF repo publishes: - our own reusable baseline checkpoint - the cleaned release metrics - the metadata needed to understand the v3 public result ## Result snapshot On the current local KWS12 protocol, the public release summary reports: - offline worst-class precision: 95.44% - offline worst-class recall: 95.38% - unknown-to-target rate: 0.43% See `release_summary_v3.json` for details and comparability notes.

Source context: 0 downloads · 0 likes · Pipeline audio-classification · Library pytorch · Repo bozliu/e2e-keyword-spotting-v3