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The metering of gas-liquid flows is difficult due to the non-linear relationship between flow regimes and fluid properties, flow orientation, channel geometry, etc. In fact, a majority of commercial multiphase flow meters have a low accuracy, limited range of operation or require a physical separation of the phases. We introduce the inference of gas-liquid flowrates using a neural network model that is trained by wire-mesh sensor (WMS) experimental data. The WMS is an experimental tool that records high-resolution high-frequency 3D void fraction distributions in gas-liquid flows. The experimental database utilized spans over two orders of superficial velocity magnitude and multiple flow regimes for a vertical small-diameter pipe. Our findings indicate that a single network can provide accurate and precise inference with below a 7.5% MAP error across all flow regimes. The best performing networks have a combination of a 3D-Convolution head, and an LSTM tail. The finding indicates that the spatiotemporal features observed in gas-liquid flows can be systematically decomposed and used for inferring phase-wise flowrate. Our method does not involve any complex pre-processing of the void fraction matrices, resulting in an evaluation time that is negligible when contrasted to the input time-span. The efficiency of the model manifests in a response time two orders of magnitude lower than the current state-of-the-art.