  Automatic Speech Recognition (ASR) can play a crucial role in enhancing the
accessibility of spoken languages worldwide. In this paper, we build a set of
ASR tools for Amharic, a language spoken by more than 50 million people
primarily in eastern Africa. Amharic is written in the Ge'ez script, a sequence
of graphemes with spacings denoting word boundaries. This makes computational
processing of Amharic challenging since the location of spacings can
significantly impact the meaning of formed sentences. We find that existing
benchmarks for Amharic ASR do not account for these spacings and only measure
individual grapheme error rates, leading to significantly inflated measurements
of in-the-wild performance. In this paper, we first release corrected
transcriptions of existing Amharic ASR test datasets, enabling the community to
accurately evaluate progress. Furthermore, we introduce a post-processing
approach using a transformer encoder-decoder architecture to organize raw ASR
outputs into a grammatically complete and semantically meaningful Amharic
sentence. Through experiments on the corrected test dataset, our model enhances
the semantic correctness of Amharic speech recognition systems, achieving a
Character Error Rate (CER) of 5.5\% and a Word Error Rate (WER) of 23.3\%.
