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Flamenco singing is characterized by pitch instability, micro-tonal ornamentations, large vibrato ranges, and a high degree of melodic variability. These musical features make the automatic identification of flamenco singers a difficult computational task. In this article we present an end-to-end pipeline for flamenco singer identification based on acoustic motif embeddings. In the approach taken, the fundamental frequency obtained directly from the raw audio signal is approximated. This approximation reduces the high variability of the audio signal and allows for small melodic patterns to be discovered using a sequential pattern mining technique, thus creating a dictionary of motifs. Several acoustic features are then used to extract fixed length embeddings of variable length motifs by using convolutional architectures. We test the quality of the embeddings in a flamenco singer identification task, comparing our approach with previous deep learning architectures, and study the effect of motivic patterns and acoustic features in the identification task. Results indicate that motivic patterns play a crucial role in identifying flamenco singers by minimizing the size of the signal to be learned, discarding information that is not relevant in the identification task. The deep learning architecture presented outperforms denser models used in large-scale audio classification problems.