Model files
The server runs three models on the local CPU (onnxruntime, no GPU needed). Their files live in MODELS_DIR (by default DATA_DIR/models, data/models in Docker).
| File | Model | Use | License | Downloaded from |
|---|---|---|---|---|
fireredvad-stream-vad.onnx, fireredvad-cmvn.ark | FireRedVAD | Speech detection | Apache-2.0 | FireRedVAD's upstream repository |
redimnet2-b6.onnx | ReDimNet2 B6 | Voiceprints | MIT (training data below) | This repository's models-v1 release |
yamnet.onnx | YAMNet | Hearing music: songs played aloud get no voiceprint (MUSIC_DETECT) | Apache-2.0 | This repository's models-v1 release |
With ENGINE=local there is a fourth one: sherpa-onnx's X-ASR zh-en model (x-asr-zipformer-transducer-zh-en-punct-int8-2026-06-03/). At its first use, the server downloads the model tarball, about 136 MB, from the k2-fsa/sherpa-onnx GitHub Release (or LOCAL_ASR_URL) and unpacks it into the same directory.
Download and verification
- At startup the server downloads the missing files, about 70 MB at the first start.
- The sha256 of each file is pinned in
server/alldayrec/infer.pyandserver/alldayrec/local_asr.py, and the server accepts only matching files. When a file doesn't match or is missing, the server refuses to start. - A file that is already there is never downloaded again.
server/alldayrec/export_onnx.pyis how maintainers make new release files from the upstream weights. ReDimNet2's export differs byte for byte between machines, so the server refuses a file you export yourself.
The voiceprint model's training data
ReDimNet2's code and weights are MIT, but its training data includes VoxBlink2 (CC BY-NC-SA 4.0). See THIRD_PARTY_NOTICES.md and decide for yourself whether that fits your use.
The LLM isn't here: the project doesn't bundle or run an LLM, and uses the external service that LLM_BASE_URL points at.