  We present SDS-200, a corpus of Swiss German dialectal speech with Standard
German text translations, annotated with dialect, age, and gender information
of the speakers. The dataset allows for training speech translation, dialect
recognition, and speech synthesis systems, among others. The data was collected
using a web recording tool that is open to the public. Each participant was
given a text in Standard German and asked to translate it to their Swiss German
dialect before recording it. To increase the corpus quality, recordings were
validated by other participants. The data consists of 200 hours of speech by
around 4000 different speakers and covers a large part of the Swiss-German
dialect landscape. We release SDS-200 alongside a baseline speech translation
model, which achieves a word error rate (WER) of 30.3 and a BLEU score of 53.1
on the SDS-200 test set. Furthermore, we use SDS-200 to fine-tune a pre-trained
XLS-R model, achieving 21.6 WER and 64.0 BLEU.
