o 2wi@sdZddlZddlZddlmZddlmZddlmZm Z m Z m Z ddl m Z ddl mZmZmZddlmZmZd Zd ed fd ed e ede edefddZ  ddede ede edeeeee eeffffddZdS)uw This script creates the BUT Reverb DB dataset, which is available at: https://speech.fit.vutbr.cz/software/but-speech-fit-reverb-database The following description is taken from the official website: This is the first release of BUT Speech@FIT Reverb Database. The database is being built with respect to collect a large number of various Room Impulse Responses, Room environmental noises (or "silences"), Retransmitted speech (for ASR and SID testing), and meta-data (positions of microphones, speakers etc.). The goal is to provide speech community with a dataset for data enhancement and distant microphone or microphone array experiments in ASR and SID. The BUT Speech@FIT Reverb Dataset consists of 9 rooms: Size [m x m x m] Volume [m^3] # RIRs Ret. Type In RIR-Only set In LibriSpeech-Only set Q301 10.7x6.9x2.6 192 31 x 3 1 Office Yes Yes L207 4.6x6.9x3.1 98 31 x 6 3 Office Yes Yes L212 7.5x4.6x3.1 107 31 x 5 2 Office Yes Yes L227 6.2x2.6x14.2 229 31 x 5 3 Stairs Yes Yes R112 4.4x2.8x2.6* ~40 31 x 5 0 Hotel room Yes No CR2 28.2x11.1x3.3 1033 31 x 4 0 Conf. room Yes No E112 11.5x20.1x4.8* ~900 31 x 2 0 Lect. room Yes No D105 17.2x22.8x6.9* ~2000 31 x 6 1 Lect. room Yes Yes C236 7.0x4.1x3.6 102 31 x 10 0 Meeting room Yes No We placed 31 microphones in both rooms. The source (a Hi-Fi loudspeaker) was placed on 5 positions in average. We measured RIRs (using exponential sine sweep method) for each speaker position. Next we recorded environmental noise (silence). There was a radio at background playing in one speaker position in the office. All microphone positions are measured and stored in meta-files. We pre-calculated positions of microphones and speakers in Cartesian and polar coordinates as absolute and relative (to the speaker). The corpus can be cited as follows: @ARTICLE{8717722, author={Szöke, Igor and Skácel, Miroslav and Mošner, Ladislav and Paliesek, Jakub and Černocký, Jan}, journal={IEEE Journal of Selected Topics in Signal Processing}, title={Building and evaluation of a real room impulse response dataset}, year={2019}, volume={13}, number={4}, pages={863-876}, doi={10.1109/JSTSP.2019.2917582}} N) defaultdict)Path)DictOptionalSequenceUnion)tqdm)CutSet Recording RecordingSet)Pathlikeresumable_downloadzGhttp://merlin.fit.vutbr.cz/ReverbDB/BUT_ReverbDB_rel_19_06_RIR-Only.tgz.F target_dirurlforce_downloadreturncCst|}|jdddd}||}|r |s td|dt|||d|d}|sTtd|d t|}|j|d Wd |S1sOwY|S) a' Download and untar the BUT Reverb DB dataset. :param target_dir: Pathlike, the path of the dir to store the dataset. :param url: str, the url that downloads file called BUT_ReverbDB.tgz. :param force_download: bool, if True, download the archive even if it already exists. Tparentsexist_okzBUT_ReverbDB.tgzz Skipping z because file exists.)r BUT_ReverbDBz Untarring r)pathN) rmkdirexistslogginginfor tarfileopen extractall)rrrtgz_nametgz_pathtgz_dirtarr#Y/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/lhotse/recipes/but_reverb_db.pydownload_but_reverb_db?s     r%silencerir corpus_dir output_dirpartsc Cs^t|}|sJd||stdt|tr|g}tt}t|dD]M}|j j }||vr5q(|j j j j j j }|j j j j j }|j j j j }|j j j } |j dd} |d|d|d| d| } tj|| d} ||| q(tt} |D] }t||| |d <q||d urt|}|jd d d |D]}| |d |d |dq| S)z Prepare the BUT Speech@FIT Reverb Database corpus. :param corpus_dir: Pathlike, the path of the dir to store the dataset. :param output_dir: Pathlike, the path of the dir to write the manifests. zNo such directory: z,No parts specified for manifest preparation.z*.wavr-z-v) recording_id recordingsNTrzbut-reverb-db_z_recordings.jsonl.gz)ris_dir ValueError isinstancestrrlistrrglobparentnamelowerstemsplitr from_fileappenddictr from_recordingsrto_file)r)r*r+r/wav_filepartroom_idmic_idspk_iduidversionr. recording manifestsr#r#r$prepare_but_reverb_db[s<       rI)Nr&)__doc__rr collectionsrpathlibrtypingrrrrrlhotser r r lhotse.utilsr r BUT_REVERB_DB_URLr3boolr%rIr#r#r#r$sD.