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- **High accuracy**
Silero VAD shows an [excellent result](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics#vs-other-available-solutions) for speech detection in streaming tasks.
Silero VAD has [excellent results](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics#vs-other-available-solutions) on speech detection tasks.
- **Fast**
One audio chunk (30+ ms) [takes](https://github.com/snakers4/silero-vad/wiki/Performance-Metrics#silero-vad-performance-metrics) **1ms** to be processed on a single CPU thread. Using batching and/or GPU one can greatly speed up inference time in production tasks.
One audio chunk (30+ ms) [takes](https://github.com/snakers4/silero-vad/wiki/Performance-Metrics#silero-vad-performance-metrics) around **1ms** to be processed on a single CPU thread. Using batching or GPU can also improve performance considerably.
- **Lightweight**
JIT model size is less than one megabyte.
JIT model is less than one megabyte in size.
- **Generalized**
- **General**
Silero VAD was trained on a big corpora that included over **100** languages and performs well on audio of varying backgorund noise levels.
Silero VAD was trained on huge corpora that include over **100** languages and it performs well on audios from different domains with various background noise and quality levels.
- **Variable sampling rate**
- **Flexible sampling rate**
Silero VAD [supports](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics#sample-rate-comparison) **8000** and **16000** [sampling rate](https://en.wikipedia.org/wiki/Sampling_(signal_processing)#Sampling_rate)
Silero VAD [supports](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics#sample-rate-comparison) **8000 Hz** and **16000 Hz** [sampling rates](https://en.wikipedia.org/wiki/Sampling_(signal_processing)#Sampling_rate).
- **Variable chunk size**
- **Flexible chunk size**
Model was trained on audio chunks of variable lengths. Chunks of length **30 ms**, **60 ms** and **100 ms** are supported directly, other may perform well too.
Model was trained on audio chunks of different lengths. **30 ms**, **60 ms** and **100 ms** long chunks are supported directly, others may work as well.
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<h2 align="center">Typical Use Cases</h2>