Phoneme-to-audio alignment is the task of synchronizing voice recordings and their related phonetic transcripts. In this work, we introduce a new system to forced phonetic alignment with Recurrent Neural Networks (RNN). With the Connectionist Temporal Classification (CTC) loss as training objective, and an additional reconstruction cost, we learn to infer relevant perframe phoneme probabilities from which alignment is derived. The core of the neural architecture is a context-aware attention mechanism between mel-spectrograms and side information. We investigate two contexts given by either phoneme sequences (model PHATT) or spectrograms themselves (model SPATT). Evaluations show that these models produce precise alignments for both speaking and singing voice. Best results are obtained with the model PHATT, which outperforms baseline reference with an average imprecision of 16.3ms and 29.8ms on speech and singing, respectively. The model SPATT also appears as an interesting alternative, capable of aligning longer audio files without requiring phoneme sequences on small audio segments.