Frame_VAD_Multilingual_MarbleNet_v2.0

Model overview

Frame-VAD Multilingual MarbleNet v2.0 is a convolutional neural network for voice activity detection (VAD) that serves as the first step for Speech Recognition and Speaker Diarization. It is a frame-based model that outputs a speech probability for each 20 millisecond frame of the input audio. The model has 91.5K parameters, making it lightweight and efficient for real-time applications. To reduce false positive errors — cases where the model incorrectly detects speech when none is present — the model was trained with white noise and real-word noise perturbations. During training, the volume of audios was also varied. Additionally, the training data includes non-speech audio samples to help the model distinguish between speech and non-speech sounds (such as coughing, laughter, and breathing, etc.) **Key Features** - Lightweight model with only 91.5K parameters - Robust against false positive errors - Outputs speech probability for each 20 ms audio frame - Multilingual support: Chinese, English, French, German, Russian, and Spanish Developers, speech processing engineers, and AI researchers will use it as the first step for other speech processing models.

Key features

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Limitations & considerations

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Source prices & cost analysis

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Public benchmarks & run configuration

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Frequently asked questions

Sources & traceability

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