Cache-timing attacks are serious security threats that exploit cache memories to steal secret information. We believe that the identification of a sequence of function calls from cache-timing data measurements is not a trivial step when building an attack. We present a recurrent neural network model able to automatically retrieve a sequence of operations from cache-timings. Inspired from natural language processing, our model is able to learn on partially labelled data. We use the model to unfold an end-to-end automated attack on OpenSSL ECDSA on the secp256k1 curve. Our attack is able to extract the 256 bits of the secret key by auto- matic analysis of about 2400 traces without any human processing.