metadata
license: apache-2.0
library_name: vokra
tags:
- vokra
- gguf
ten-vad (Vokra GGUF)
Converted to the Vokra GGUF format for Vokra, a zero-dependency speech-AI inference runtime.
This is a conversion, not a new model. The weights are the upstream ones; Vokra re-packages them so its runtime can memory-map them directly. Credit for the model belongs upstream — see Source below.
Files
| File | Size | SHA-256 |
|---|---|---|
ten-vad.gguf |
0.3 MB | a0c5f7a89569dca5fa06de4050768cddf6cd456ddca785a3eb241699528c3f04 |
Usage
# Download (any HTTP client works — the file is a plain GGUF)
curl -L -o ten-vad.gguf \
https://huggingface.co/vokra/ten-vad/resolve/main/ten-vad.gguf
vokra-cli run --model ten-vad.gguf --input input.wav
Provenance
| Field | Value |
|---|---|
| Architecture | ten_vad |
| Tensors | 19 |
| Upstream source | github.com/TEN-framework/ten-vad (compact ~306 KB LSTM/GRU VAD + LPCNet-derived DSP front-end, Apache-2.0 main + BSD-3-Clause front-end — NOTICE attribution required for LPCNet copyright when redistributing binaries embedding the front-end) |
| Upstream licence | apache-2.0 |
| Licence class | permissive |
| Registry model id | ten_vad |
| Vokra GGUF schema | 1 |
| Converted by | vokra-core 0.1.0-alpha.0 |
Every row above is read out of this file's own vokra.* metadata, so the card cannot claim something the artifact does not carry.
Licence
The weights are distributed under apache-2.0, unchanged from upstream. Conversion does not alter the licence, and your obligations run to the upstream author.
Verifying this file
shasum -a 256 ten-vad.gguf
# expect: a0c5f7a89569dca5fa06de4050768cddf6cd456ddca785a3eb241699528c3f04