Hi Silero team,
We're using the pre-trained silero_vad.onnx model (via the android-vad wrapper) in our project and would like to properly document/attribute the model internally.
We've reviewed:
src/silero_vad/model.py — confirms this loads a pre-compiled .jit/.onnx artifact, but doesn't define the architecture.
The Quality-Metrics wiki page — lists evaluation/benchmark datasets (ESC-50, AliMeeting, Earnings21, MSDWild, AISHELL-4, VoxConverse, Libriparty, etc.), but these appear to be test/eval sets rather than the training data.
Could you help clarify:
What dataset(s) were used to train the released VAD model (as opposed to the evaluation sets listed on the Quality-Metrics page)?
Is the model architecture (layer structure/network design) documented anywhere publicly, or is it considered proprietary?
Hi Silero team,
We're using the pre-trained silero_vad.onnx model (via the android-vad wrapper) in our project and would like to properly document/attribute the model internally.
We've reviewed:
src/silero_vad/model.py — confirms this loads a pre-compiled .jit/.onnx artifact, but doesn't define the architecture.
The Quality-Metrics wiki page — lists evaluation/benchmark datasets (ESC-50, AliMeeting, Earnings21, MSDWild, AISHELL-4, VoxConverse, Libriparty, etc.), but these appear to be test/eval sets rather than the training data.
Could you help clarify:
What dataset(s) were used to train the released VAD model (as opposed to the evaluation sets listed on the Quality-Metrics page)?
Is the model architecture (layer structure/network design) documented anywhere publicly, or is it considered proprietary?