o Si@s|dZddlZddlZddlmZddlmZddlmZddl m Z ddl m Z m Z mZmZmZmZddlmZdd lmZmZdd lmZmZdd lmZmZdd lmZdd lm Z m!Z!ddl"m#Z#dZ$ed#ddZ% d$de#de#de&dee&deeee ff ddZ'  d%de&de#dee(de)deee!ff ddZ*  d%de#d ee#de)de e&e e&eee!ffffd!d"Z+dS)&a About the Bengali.AI Speech corpus The competition dataset comprises about 1200 hours of recordings of Bengali speech. Your goal is to transcribe recordings of speech that is out-of-distribution with respect to the training set. Note that this is a Code Competition, in which the actual test set is hidden. In this public version, we give some sample data in the correct format to help you author your solutions. The full test set contains about 20 hours of speech in almost 8000 MP3 audio files. All of the files in the test set are encoded at a sample rate of 32k, a bit rate of 48k, in one channel. It is covered in more detail at https://arxiv.org/abs/2305.09688 Please download manually by kaggle competitions download -c bengaliai-speech N) defaultdict)ProcessPoolExecutor)contextmanager)Path)DictListOptionalSequenceTupleUnion)tqdm"get_ffmpeg_torchaudio_info_enabled"set_ffmpeg_torchaudio_info_enabled) Recording RecordingSet) fix_manifests$validate_recordings_and_supervisions)manifests_exist)SupervisionSegmentSupervisionSet)Pathlike)trainvalidtestreturnccs0t}tdz dVWt|dSt|w)NFr )enabledrS/home/ubuntu/.local/lib/python3.10/site-packages/lhotse/recipes/bengaliai_speech.pydisable_ffmpeg_torchaudio_info)s r corpus_dir audio_pathaudio_idtextc CsR|}|std|dStj||d}t|||d|jddd}||fS)NzNo such file: )path recording_idgrBengali)idr&r$startdurationchannellanguage)resolveis_fileloggingwarningr from_filerr*)r!r"r#r$ recordingsegmentrrr_parse_utterance3s$ r4subset audio_infonum_jobsc Csjt|}|dkr |d}n|d}t|d}tt|m}g}g}g} t|ddD].} tjt | d dd } |d urM| | vrHq-|| } nd } | | t|| | | q-t|d dD]} | }|d urmqb|\}}| || |qbt|}t| }Wd n1swYWd ||fSWd ||fS1swY||fS) z Returns the RecodingSet and SupervisionSet given a dataset part. :param subset: str, the name of the subset. :param corpus_dir: Pathlike, the path of the data dir. :return: the RecodingSet and SupervisionSet for train and valid. r test_mp3s train_mp3sz*.mp3zDistributing tasksdescr5z.mp3r N Processing)rlistrglobrrr osr%splitstrreplacekeysappendsubmitr4resultrfrom_recordingsr from_segments)r6r!r7r8 part_path audio_pathsexfutures recordings supervisionsr"r#r$futurerGr2r3 recording_setsupervision_setrrr_prepare_subsetPsJ           rS output_dirc Cst|}|sJd|tdt}|dur%t|}|jdddtt}t|d}| }Wdn1s@wYi}i}|ddD]/} d| vrh| dd  d d} | d|| d <qOd | vr~| d d  d d} | d|| d <qOt |d dD]_} td| t| |dddrtd| dqt| || dkr|n| dkr|nd|d\} } t| | \} } t| | |dur| |d| d| |d| d| | d|| <q|S)aO Returns the manifests which consist of the Recordings and Supervisions :param corpus_dir: Path to the Bengali.AI Speech dataset. :param output_dir: Pathlike, the path where to write the manifests. :return: a Dict whose key is the dataset part, and the value is Dicts with the keys 'recordings' and 'supervisions'. zNo such directory: zPreparing Bengali.AI Speech...NT)parentsexist_okz train.csvr5z,trainr ,rz,validz Dataset partsr;z%Processing Bengali.AI Speech subset: bengaliai_speechzjsonl.gz)partrTprefixsuffixzBengali.AI Speech subset: z already prepared - skipping.rr)r6r!r7r8bengaliai_speech_supervisions_z .jsonl.gzbengaliai_speech_recordings_)rNrO)ris_dirr/infoBENGALIAI_SPEECHmkdirrdictopenread splitlinesrCrAr rrSrrto_file) r!rTr8subsets manifestsfr7 train_info valid_infolinerYrQrRrrrprepare_bengaliai_speechsl     rm)rN)r )Nr5),__doc__r/r@ collectionsrconcurrent.futures.processr contextlibrpathlibrtypingrrrr r r tqdm.autor lhotserr lhotse.audiorr lhotse.qarrlhotse.recipes.utilsrlhotse.supervisionrr lhotse.utilsrr`rrBr4rbintrSrmrrrrsl              5