o 2wi@sddlmZmZmZddlZddlmZddlmZddl m Z ej e dddej d ejddd d ej d ejddd d ej d ed ejddedddejddddejddddd e d e d e dededef ddZdS))OptionalSequenceUnionN)prepare)prepare_iwslt22_ta)PathlikeT) show_default)context_settings corpus_dir)existsdir_okay)typesplits output_dirz-jz --num-jobszBHow many threads to use (can give good speed-ups with slow disks).)r defaulthelpz--normalize-textFzpWhether to perform additional text cleaning and normalization from https://aclanthology.org/2022.iwslt-1.29.pdf.)rrz--langszNComma-separated list of language abbreviations for source and target languagesnormalize_textlangsnum_jobscCs"|d}t||||||ddS)a IWSLT_2022 data preparation.  This is conversational telephone speech collected as 8kHz-sampled data. The catalog number LDC2022E01 corresponds to the train, dev, and test1 splits of the iwslt2022 shared task. To obtaining this data your institution needs to have an LDC subscription. You also should download the predined splits with git clone https://github.com/kevinduh/iwslt22-dialect.git ,)rrcleanrN)splitr)r rrrrr langs_listr`/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/lhotse/bin/modes/recipes/iwslt22_ta.py iwslt22_ta s ' r)typingrrrclicklhotse.bin.modesrlhotse.recipes.iwslt22_tar lhotse.utilsrcommanddictargumentPathoptionintboolstrrrrrrsN