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VAD v4

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adamnsandle
2022-10-26 16:10:20 +00:00
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README.md
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@@ -15,7 +15,7 @@ This repository also includes Number Detector and Language classifier [models](h
<br/>
<p align="center">
<img src="https://user-images.githubusercontent.com/36505480/145563071-681b57e3-06b5-4cd0-bdee-e2ade3d50a60.png" />
<img src="https://user-images.githubusercontent.com/36505480/198026365-8da383e0-5398-4a12-b7f8-22c2c0059512.png" />
</p>
<details>
@@ -35,11 +35,11 @@ https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-
- **Fast**
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. Under certain conditions ONNX may even run up to 2-3x faster.
One audio chunk (30+ ms) [takes](https://github.com/snakers4/silero-vad/wiki/Performance-Metrics#silero-vad-performance-metrics) less than **1ms** to be processed on a single CPU thread. Using batching or GPU can also improve performance considerably. Under certain conditions ONNX may even run up to 4-5x faster.
- **Lightweight**
JIT model is less than one megabyte in size.
JIT model is around one megabyte in size.
- **General**
@@ -47,11 +47,11 @@ https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-
- **Flexible sampling rate**
Silero VAD [supports](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics#sample-rate-comparison) **8000 Hz** and **16000 Hz** (PyTorch JIT) and **16000 Hz** (ONNX) [sampling rates](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).
- **Flexible chunk size**
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.
Model was trained on **30 ms**. Longer chunks are supported directly, others may work as well.
- **Highly Portable**