| --- |
| pipeline_tag: voice-activity-detection |
| license: bsd-2-clause |
| tags: |
| - speech-processing |
| - semantic-vad |
| - multilingual |
| datasets: |
| - pipecat-ai/smart-turn-data-v3.1-train |
| - pipecat-ai/smart-turn-data-v3.1-test |
| --- |
| |
| # Smart Turn v3.x |
|
|
| **Smart Turn** is an open‑source semantic Voice Activity Detection (VAD) model that tells you whether a speaker has finished their turn by analysing the raw waveform, not the transcript. |
|
|
| ## Links |
|
|
| * [Blog post: Smart Turn v3](https://www.daily.co/blog/announcing-smart-turn-v3-with-cpu-inference-in-just-12ms/) |
| * [GitHub repo](https://github.com/pipecat-ai/smart-turn) with training and inference code, and more information |
| * [Datasets](https://huggingface.co/pipecat-ai/datasets) |
|
|
|
|
| ## Model architecture |
|
|
| * Backbone: Whisper Tiny encoder |
| * Head: shallow linear classifier |
| * Params: 8M |
| * Checkpoint: 8 MB ONNX (int8 quantized), 32MB ONNX (unquantized) |
|
|
|
|
| ## How to use |
|
|
| Please see the blog post and GitHub repo for more information on using the model, either standalone or with Pipecat. |
|
|
|
|
| ## Thanks |
|
|
| Thank you to the following organisations for contributing audio datasets: |
|
|
| - [Liva AI](https://www.theliva.ai/) |
| - [Midcentury](https://www.midcentury.xyz/) |
| - [MundoAI](https://mundoai.world/) |
|
|
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|
