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--- |
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license: mit |
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tags: |
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- audio |
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- voice-activity-detection |
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- coreml |
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- silero |
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- speech |
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- ios |
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- macos |
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- swift |
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library_name: coreml |
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pipeline_tag: voice-activity-detection |
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datasets: |
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- alexwengg/musan_mini50 |
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- alexwengg/musan_mini100 |
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metrics: |
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- accuracy |
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- f1 |
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language: |
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- en |
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base_model: |
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- onnx-community/silero-vad |
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--- |
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# **<span style="color:#5DAF8D">🧃 CoreML Silero VAD </span>** |
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[](https://discord.gg/WNsvaCtmDe) |
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[](https://github.com/FluidInference/FluidAudio) |
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A CoreML implementation of the Silero Voice Activity |
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Detection (VAD) model, optimized for Apple platforms |
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(iOS/macOS). This repository contains pre-converted |
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CoreML models ready for use in Swift applications. |
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See FluidAudio Repo link at the top for more information |
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## Model Description |
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**Developed by:** Silero Team (original), converted by |
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FluidAudio |
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**Model type:** Voice Activity Detection |
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**License:** MIT |
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**Parent Model:** |
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[silero-vad](https://github.com/snakers4/silero-vad) |
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This is how the model performs against the silero-vad v6.0.0 basline Pytorch JIT version |
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Note that we tested the quantized versions, as the model is already tiny, theres no performance imporvement at all. |
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This is how the different models compare in terms of speed, the 256s takes in 8 chunks of 32ms and processes it in batches so its much faster |
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Conversion code is available here: [FluidInference/mobius](https://github.com/FluidInference/mobius) |
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## Intended Use |
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### Primary Use Cases |
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- Real-time voice activity detection in iOS/macOS |
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applications |
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- Speech preprocessing for ASR systems |
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- Audio segmentation and filtering |
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## How to Use |
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Citation |
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@misc{silero-vad-coreml, |
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title={CoreML Silero VAD}, |
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author={FluidAudio Team}, |
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year={2024}, |
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url={https://huggingface.co/alexwengg/coreml-silero-vad} |
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} |
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@misc{silero-vad, |
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title={Silero VAD}, |
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author={Silero Team}, |
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year={2021}, |
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url={https://github.com/snakers4/silero-vad} |
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} |
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- GitHub: https://github.com/FluidAudio/FluidAudioSwift |
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