Domestic canaries produce complex vocal patterns embed- ded in various levels of abstraction. Studying such temporal organization is of particular relevance to understand how animal brains represent and process vocal inputs such as language. However, this requires a large amount of annotated data. We propose a fast and easy-to-train trans- ducer model based on RNN architectures to automate parts of the anno- tation process. This is similar to a speech recognition task. We demon- strate that RNN architectures can be efficiently applied on spectral fea- tures (MFCC) to annotate songs at time frame level and at phrase level. We achieved around 95% accuracy at frame level on particularly complex canary songs, and ESNs achieved around 5% of word error rate (WER) at phrase level. Moreover, we are able to build this model using only around 13 to 20 minutes of annotated songs. Training time takes only 35 seconds using 2 hours and 40 minutes of data for the ESN, allowing to quickly run experiments without the need of powerful hardware.