We present the first public, user-friendly system for Galician poetry scansion, a symbolic system derived from a well-performing mixed-meter Spanish scansion library. We adapted its resources to Galician and added a preprocessing module. The system achieves 88% per-line accuracy in exact stress-pattern match on data unseen during development, and has practical value: First, it helps create a large annotated corpus to train scansion systems. Second, its web interface can help engage a non-specialist public. Third, its current accuracy is helpful for annotating large volumes of poetry and studying metrical trends in Computational Literary Studies use cases.

We present the first neural systems for automatic metrical scansion of poetry in Galician, a Romance language close to Portuguese and Spanish. The task is threefold: First, identifying metrical syllables based on lexical ones; both syllable series may differ given metrical licenses modifying a line’s syllable structure to enable stress-related rhythms. Second, identifying stress patterns, and third identifying the metrical syllable count, based on stressed positions. We manually annotated a corpus of 4,287 examples, a first in Galician, and fine-tuned an 8B-parameter LLM specialized in Galician and Portuguese, and two encoder–decoder models: ByT5, a token-free byte-to-byte model, and the multilingual mT5, which includes Galician. We also tested our recent symbolic scansion system. Several fine-tuning setups reached exact per-line accuracy above 90% on our test-set at all three scansion subtasks, using orthographic syllables with explicit stress marks as input. Encoder–decoders performed better than the LLM. The token-free ByT5 was best, particularly when adding the two surrounding lines to the input. The symbolic system (89.9% acc.) managed rare metaplasms infrequent in training data better than the neural ones, and the approaches can be seen as complementary.