Dear colleagues, On behalf of my co-authors, I'm pleased to share our new publication in Methods in Ecology and Evolution: *A computational framework to characterize and compare the tonal repertoires of toothed whales.* Authors: Maia Austin, Julie N. Oswald, Manali Rege-Colt, Emma Gagne, Eric Angel Ramos, Joëlle De Weerdt, Nicola Ransome, Laura J. May-Collado Abstract: Toothed whales, despite being one of the most acoustically specialized lineages of vertebrates, lack detailed vocal repertoire characterizations comparable to those of songbirds and primates. Current descriptions often lack standardization, limiting interspecies and intraspecies comparisons critical for understanding the contribution of phylogenetic, social and environmental drivers of vocal repertoire evolution. This study reviews six bioacoustics software packages (Luscinia, Beluga, ARTwarp, DeepSqueak, PAMGuard and SASLab) using machine learning for toothed whale whistle detection, extraction of whistle fundamental frequency contours and categorization. We applied these tools to acoustic recordings from four dolphin species to create a primer with guidelines and scripts for those interested in replicating the analyses. The results show that manual (Luscinia) and semi‐automated (Beluga, DeepSqueak) detection and extraction of whistles yielded a greater number of usable contours than the fully automated approaches (PAMGuard and SASLab). The integration of hard and fuzzy unsupervised machine learning analyses provided a multifaceted characterization of whistle repertoires based on identified patterns and structures. The hard approaches from ARTwarp and DeepSqueak were used to categorize signals based on contour similarity. Both packages produced similar numbers of categories. DeepSqueak was faster in categorizing contours, but ARTwarp is more flexible in how similarity thresholds are set. We used these categories to demonstrate estimating relative whistle repertoire size using three methods—Coupon Collector, Capture–Recapture and Hill Numbers. While the methods differ in their assumptions, there was consensus in the ranking of species based on repertoire size. We favour the application of Hill numbers to measure repertoire size and compositional similarity, as this method addresses uneven sampling. The fuzzy approach used the frequency and temporal features extracted from the traced contours in Luscinia to evaluate repertoire similarity in acoustic structure using the package's built‐in multivariate tools. This primer equips researchers to generate more detailed toothed whale repertoire characterizations by integrating machine learning tools with mathematical and community ecology approaches. Supplementary material is provided to support researchers with varying levels of experience in implementing these techniques and indices in their studies. The full text is open access, available on http://dx.doi.org/10.1111/2041-210X.70065. Guides to the software used can be found on GitHub ( https://github.com/austinmaia/ml-framework-repertoire/wiki). Best, Maia Austin PhD Candidate University of Vermont