This model can be used for Voice Activity Detection (VAD), and serves as the first step for Automatic Speech Recognition (ASR). Silero VAD works with 8 kHz and 16 kHz sample rates, with fixed 256 and 512 sample windows respectively. It supports more than 6,000 languages.
This model is ready for commercial use.
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see Silero Voice Activity Detector | PyTorch.
This model is governed by the NVIDIA RIVA License Agreement.
Disclaimer: AI models generate responses and outputs based on complex algorithms and machine learning techniques, and those responses or outputs may be inaccurate or offensive. By downloading a model, you assume the risk of any harm caused by any response or output of the model.
By using this software or model, you are agreeing to the terms and conditions of the license, acceptable use policy and Silero VAD’s privacy policy. Silero VAD is released under the MIT license.
Silero VAD website Silero VAD citation
@misc{Silero VAD,
author = {Silero Team},
title = {Silero VAD: pre-trained enterprise-grade Voice Activity Detector (VAD), Number Detector and Language Classifier},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/snakers4/silero-vad}},
commit = {insert_some_commit_here},
email = {hello@silero.ai}
}
Architecture Type: Unknown Network Architecture: Silero VAD
Input Type(s): Audio Input Format(s): Linear PCM 16-bit 1 channel (Audio) Input Parameters: One-Dimensional (1D)
Output Type(s): Probabilities of speech Output Format: Float Output Parameters: 1D
Supported Hardware Microarchitecture Compatibility:
Supported Operating System(s):
Bible.is Data Collection Method: Unknown Labeling Method: Unknown
globalrecordings.net Data Collection Method : Unknown Labeling Method: Unknown
VoxLingua107 Data Collection Method : Unknown Labeling Method: Unknown
Common Voice Data Collection Method : Human Labeling Method: Human
MLS Data Collection Method : Human Labeling Method: Human
Inference: Engine: Onnxruntime, Triton
Test Hardware:
For more detail on model usage, evaluation, training dataset and implications, please refer to Silero VAD github.
## Ethical Considerations:
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