o 2wi@s&ddlmZddlZddlmZmZddlmZmZm Z ddl m Z dgZ ej eddd ejd ejddd d ejd ed ejddedddejddddejddedgdddd e d e deededef ddZej eddd ejd ed ejddedgdd!dd e deefd"dZej eddd ejd ed ejddedgdd!dd e deefd#d$Zej eddd ejd ejddd d ejd ed ejddedddejddddejddedgdddd e d e deededef d%d$ZdS)&)SequenceN)downloadprepare)download_librittsdownload_librittsrprepare_libritts)PathlikelibrittsT) show_default)context_settings corpus_dir)existsdir_okay)type output_dirz-jz --num-jobsz?How many jobs to use (can give good speed-ups with slow disks).)rdefaulthelpz1--link-previous-utterance/--no-previous-utteranceFa If true adds previous utterance id to supervisions. Useful for reconstructing chains of utterances as they were read from LibriVox books. If previous utterance was skipped from LibriTTS datasets previous_utt label is None. 66% of utterances have previous utterance.)rrz-pz--dataset-partsallz{List of dataset parts to prepare. To prepare multiple parts, pass each with `-p` Example: `-p train-clean-360 -p dev-other`)rrmultipler dataset_partsnum_jobslink_previous_utterancecCt|||||ddS)zLibriTTS data preparation.rrrlink_previous_uttNrr rrrrr^/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/lhotse/bin/modes/recipes/libritts.pyr % target_dirz|List of dataset parts to download. To prepare multiple parts, pass each with `-p` Example: `-p train-clean-360 -p dev-other`cCt||ddS)zLibriTTS data download.rN)rr!rrrrr :cCr")zLibriTTS-R data download.r#N)rr$rrr librittsrMr%r&cCr)zLibriTTS-R data preparation.rNrrrrrr&`r )typingrclicklhotse.bin.modesrrlhotse.recipesrrr lhotse.utilsr__all__commanddictargumentPathoptionintstrboolr r&rrrrs