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We introduce Sound of Traffic, the largest publicly available dataset for traffic identification and counting to date. With over 415 hours of multichannel acoustic traffic data recorded in six different locations, it encompasses varying levels of traffic density and environmental conditions. In this work, we discuss strategies for automatic collection and alignment of large amount of labeled data, leveraging existing asynchronous urban sensors such as radar, cameras, and inductive coils. In addition to the dataset, we propose a simple baseline system for vehicle counting divided by type of the vehicle (passenger vs. commercial vehicle) and direction of travel (right-to-left and left-to-right), a fundamental task for traffic analysis. The dataset and baseline system serve as a starting point for researchers to develop more advanced algorithms and models in this field. The dataset can be accessed at https://zenodo.org/records/10700792 and https://zenodo.org/records/11209838.