o Si^ @sdZddlZddlZddlZddlmZddlmZddlm Z m Z m Z m Z m Z mZddlmZddlmZddlmZmZmZmZmZdd lmZdd lmZdd lmZee Z!e!"ej#gd Z$eGd ddZ%eGdddZ&dede e'e&ffddZ(ddZ)   dCdede'de ede e'eeefffddZ* dDde'de%de+fd d!Z, dDde'de%de+fd"d#Z- dDde'de%de+fd$d%Z. dDde'de%de+fd&d'Z/ dDde'de%de+fd(d)Z0 dDde'de%de+fd*d+Z1 dDde'de%d,e ee'efge e e'ffde+fd-d.Z2 dEd/ed0ed1e+d2e efd3d4Z3 dEd/ed0ed1e+d2e ede eeff d5d6Z4d7ede e ee effd8d9Z5d:ed;e'dZ6d?ede d@fdAdBZ7dS)Fa This is a data preparation recipe for the National Corpus of Speech in Singaporean English. The entire corpus is organised into a few parts. Part 1 features about 1000 hours of prompted recordings of phonetically-balanced scripts from about 1000 local English speakers. Part 2 presents about 1000 hours of prompted recordings of sentences randomly generated from words based on people, food, location, brands, etc, from about 1000 local English speakers as well. Transcriptions of the recordings have been done orthographically and are available for download. Part 3 consists of about 1000 hours of conversational data recorded from about 1000 local English speakers, split into pairs. The data includes conversations covering daily life and of speakers playing games provided. Parts 1 and 2 were recorded in quiet rooms using 3 microphones: a headset/ standing microphone (channel 0), a boundary microphone (channel 1), and a mobile phone (channel 3). Recordings that are available for download here have been down-sampled to 16kHz. Details of the microphone models used for each speaker as well as some corresponding non-personal and anonymized information can be found in the accompanying spreadsheets. Part 3's recordings were split into 2 environments. In the Same Room environment where speakers were in same room, the recordings were done using 2 microphones: a close-talk mic and a boundary mic. In the Separate Room environment, speakers were separated into individual rooms. The recordings were done using 2 microphones in each room: a standing mic and a telephone. Under Part 4, speakers were encouraged as best as possible to switch from Singapore English to their Mother Tongue languages. These recordings were done under two environments. In the Same Room recordings, speakers sit at least two metres apart and record using their mobile phones. In the Different Room environment, speakers would speak through each other via Zoom on their laptops, and recording using their mobile phones. Under Part 5, speakers were made to speak following the 4 styles: Debate, Finance topics, Positive Emotion and Negative Emotions. All recordings were done in a Separate room session, via Zoom, where the audio is recorded using the mobile phone. Under Part 6, speakers were made to speak following the 3 styles within either of the 3 designs: Design 1 (holiday/hotel/restaurant), Design 2 (bank, telephone, insurance), Design 3 (HDB, MOE, MSF). All recordings were done in a Separate room session, via Zoom, where the audio is recorded using the mobile phone. We currently only support the part 3 recordings, in "same room close mic" and "separate rooms phone mic" environments. N) dataclass)Path)CallableDictListOptionalTupleUnion)tqdm)logging_redirect_tqdm) Recording RecordingSetSupervisionSegmentSupervisionSet$validate_recordings_and_supervisions) parallel_map) fix_manifests)Pathlike)PART1_CHANNEL0PART1_CHANNEL1PART1_CHANNEL2PART2_CHANNEL0PART2_CHANNEL1PART2_CHANNEL2PART3_SameBoundaryMicPART3_SameCloseMicPART3_SeparateIVRPART3_SeparateStandingMicPART4_CodeswitchingDiffRoomPART4_CodeswitchingSameRoom PART5_DebatePART5_FinanceEmotionPART6_CallCentreDesign1PART6_CallCentreDesign2PART6_CallCentreDesign3c@sHeZdZUeeefed<eeefed<deeefddfddZdS)ScriptAudioDir script_dir audio_dirparentreturncCs t|}t||j||jdS)Nr&r')rr%r&r')selfr(r,F/home/ubuntu/.local/lib/python3.10/site-packages/lhotse/recipes/nsc.py relative_toJs zScriptAudioDir.relative_toN)__name__ __module__ __qualname__r strr__annotations__r.r,r,r,r-r%Es r%c@s,eZdZUeeeegefed<eed<dS)HandlerMappinghandler script_audioN) r/r0r1rr2r%intdictr3r,r,r,r-r4Rs  r4 corpus_dirr)cCs|d}|dd}idtttddd|dd tttd d d|dd tttd dd|ddtttddd|ddtttddd|ddtttddd|ddtttddd|ddtttddd|ddtttddd|dd tttdd!d|dd"tttd#d$d|dd%tttd&d'd|dd(tttd)d*d|dd+tttd,d-d|dd.tttd/d0d|dd1tttd2d3d|dd4tttd5d6d|d}|S)7NzIMDA - National Speech Corpusz*IMDA - National Speech Corpus - Additionalz*IMDA - National Speech Corpus (Additional)rzPART1/DATA/CHANNEL0/SCRIPTzPART1/DATA/CHANNEL0/WAVEr*)r5r6rzPART1/DATA/CHANNEL1/SCRIPTzPART1/DATA/CHANNEL1/WAVErzPART1/DATA/CHANNEL2/SCRIPTzPART1/DATA/CHANNEL2/WAVErzPART2/DATA/CHANNEL0/SCRIPTzPART2/DATA/CHANNEL0/WAVErzPART2/DATA/CHANNEL1/SCRIPTzPART2/DATA/CHANNEL1/WAVErzPART2/DATA/CHANNEL2/SCRIPTzPART2/DATA/CHANNEL2/WAVErzPART3/Scripts SamezPART3/Audio Same BoundaryMicrzPART3/Audio Same CloseMicrzPART3/Scripts SeparatezPART3/Audio Separate IVRrz PART3/Audio Separate StandingMicrz%PART4/Codeswitching/Diff Room Scriptsz#PART4/Codeswitching/Diff Room Audiorz%PART4/Codeswitching/Same Room Scriptsz#PART4/Codeswitching/Same Room Audior zPART5/Debate ScriptszPART5/Debate Audior!zPART5/Finance + Emotion ScriptszPART5/Finance + Emotions Audior"z"PART6/Call Centre Design 1/Scriptsz PART6/Call Centre Design 1/Audior#z"PART6/Call Centre Design 2/Scriptsz PART6/Call Centre Design 2/Audior$z"PART6/Call Centre Design 3/Scriptsz PART6/Call Centre Design 3/Audio) r4 prepare_part1r%r. prepare_part2 prepare_part3 prepare_part4 prepare_part5 prepare_part6)r9part_1_3_parent_dirpart_4_6_parent_dirpart_handler_mappingr,r,r-get_part_handler_mapYsV     rCcCszddl}WdStd)NrzNSC data preparation requires the forked 'textgrids' package to be installed. Please install it with 'pip install git+https://github.com/pzelasko/Praat-textgrids' and try again.) textgrids ImportError)rDr,r,r-check_dependenciesxs rFr dataset_part output_dirc Cstt|}|sJd|t|}||vr'||}|||j|}ntd|td i|\}}t|||durat|}|j ddd| |d|d| |d|d|S) a Returns the manifests which consist of the Recordings and Supervisions. When all the manifests are available in the ``output_dir``, it will simply read and return them. :param corpus_dir: Pathlike, the path to the raw corpus distribution. :param dataset_part: str, name of the dataset part to be prepared. :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 'audio' and 'supervisions'. zNo such directory: zUnknown dataset part: NT)parentsexist_oknsc_supervisions_z .jsonl.gznsc_recordings_r,) rFris_dirrCr5r6 ValueErrorrrmkdirto_file) r9rHrInum_jobspart_handler_map handler_map manifests recordings supervisionsr,r,r- prepare_nscs( rX part_namescript_audio_dirrRc Osg}g}t|j}t|j}t|dd} | dvsJ|d} | s(| dd|dD} ttt | t |t | t | |dt | d|d D]} | | d | | d qOt|t|d S) N>rrG extractedcSg|]}|qSr,r,.0fr,r,r- z!prepare_part1..z SPEAKER*.zip)rRCreating manifests for totaldescrVrWrVrW)rr'r&r7existsrPglobr r_parse_part1_speaker itertoolsrepeatlenextendr from_recordingsr from_segments) rYrZrRargskwargsrVrWr'r&channelextract_to_dirspeaker_zip_filesspeaker_manifestsr,r,r-r:s6     r:cOt||||d|SN)rYrZrR)r:rYrZrRrrrsr,r,r-r;r;cs^ddlmdksJddtttfdtttfffdd }t|||d |S) NrTextGridrz1The recipe too different, currently not supported audio_filer)csrt|}tj}dkr |jjd|j}||d}|}n ||jd}|j}t|}||d}||fS)Nr_ .TextGridcoding)rr&r(namestem_detect_textgrid_encoding)r~r&script_file_stem script_file textgrid_keyrtgr}rYrZr,r-textgrid_field_id_resolvers  z1prepare_part3..textgrid_field_id_resolverrYrZtextgrid_loaderrR)rDr}r r2rrrprepare_textgrid_based_partrYrZrRrrrsrr,rr-r<s  *r<cLddlmdtttfdttfffdd }t||||d|S)Nrr|r~r)cNt|}tj}||jd}t|}||d}tt|}||fSNrrrr&rrnextiterkeysr~r&rrrrr}rZr,r-r  z1prepare_part4..textgrid_field_id_resolverrrDr}r r2rrrrr,rr-r=    r=cr)Nrr|r~r)crrrrrr,r-r/rz1prepare_part5..textgrid_field_id_resolverrrrr,rr-r>&rr>cOrxry)r>rzr,r,r-r?Er{r?rc sNtg}g}t|j}ddt|d|dD} t} t| t| d|dD]o} zH|d| j | vsFJdd | d | t j | d || \} } d dfd dt | | DD}|||Wq,tyttd| dWdn1swYYq,wt|t|dS)ahPrepare part that use textgrid to storing script like: PART3, PART4, PART5, PART6 Args: part_path (Path): path to part script_audio_dir (Path): root dir of audios script_file_resolver (Callable): a function resolve an audio path in to textgrid script file and it's key num_jobs (int): number of workers to process data cSr^r,r,r_r,r,r-rbjsz/prepare_textgrid_based_part..z**/*.wavz**/*.WAVrdrerzDuplicated recording id "z", audio path: "" recording_idcSsg|] }|jdkr|qS)r)duration)r`sr,r,r-rb~s  c 3sd|]-\}}|jdvrtjd|j|jtt|j|jddj|j|jddVqdS))zz-)ndigitszSingaporean English)idrstartrtextlanguagespeakerN)rrrxminminroundxmaxr)r`idxsegment recordingrr,r- s$  z.prepare_textgrid_based_part..zError when processing "z" - skipping...Nrh)rFrr'rlchainrglobsetr rnraddr from_file enumerateroappend Exceptionr loggerwarningr rprrq)rYrZrrRrrrsrVrWr' audio_filesprocessed_recordings audio_pathrrsegmentsr,rr-rUsN         rspeaker_zip_filer&rtruc Cst||||d}g}g}|D]\}}||||qg} g} ddt||DD]\} } | | | | q-t| t| dS)N)rr&rtrucSsg|] \}}t||qSr,)_parse_part1_script)r`sc_fss_dr,r,r-rbsz(_parse_part1_speaker..rh) _preprocess_part1_speakeritemsrzipror rprrq) rr&rtruscript_session_dir_mapping sessions_dir scripts_filer session_dirrVrWrrr,r,r-rks*    rkc Cst|}t|}|dur|j}||j}|s3t| }||Wdn1s-wYntt d|d|dWdn1sMwYdd| dD}g}|D]}|j d} |j d } ||| | d } | | q`d d t ||DS) aExtract PART1/PART2 speaker audio Args: speaker_zip_file (Pathlike): Path to speaker zipped audio file script_dir (Pathlike): Path to script dir of the channel channel (int): Channel of the PART, we can parse from $script_dir but it is not necessary extract_to_dir (Optional[Path]): Directory to extract zipped audio file, default to parent dir of $speaker_zip_file Returns: Dict[Path, Path]: Mapping of script file -> speaker's session dir Nz Reusing "z" as extracted "z" since it is existed alreadycSr^r,r,r_r,r,r-rbrcz-_preprocess_part1_speaker..zSESSION*SPEAKERSESSIONz.TXTcSsi|]\}}||qSr,r,)r`sr_frr,r,r- sz-_preprocess_part1_speaker..)rr(rrizipfileZipFile extractallr rrrj removeprefixrr) rr&rtruspeaker_audio_dirzfrrrspk_idsession_numbersession_script_filer,r,r-rs.      rrc Cs$g}g}t|ddd{}d}d}|D]?}|dd}|dkrI|d|krI|ddkr0|d}t|||\} } | rD|| || d}}q|d}|d}q|rrt|||\} } | r||| || Wd||fSWd||fSWd||fS1swY||fS) Nrz utf-8-sig)encoding  rrG)openrstripsplit_create_part1_single_recordr) rrrVrWfrprevious_audio_id previous_textlinecolumnsrrr,r,r-rsH          rr _audio_idrc Cs||d}ztj||d}t|j|jd|j|d}||fWStyJttd|d|dWdYd S1sBwYYd St y}z*tt d||Wdn1shwYWYd}~d SWYd}~d Sd}~ww) Nz.WAVrr)rrrrrzRecording audio of script "z" can not be found in "rzError occurred with )NN) r rrrrFileNotFoundErrorr rrrerror)rrrr~rrer,r,r-r s>   & r textgrid_filer2cCsddl}d}t|d }|d}Wdn1swY|dt||kr,dS||}|dur9dS|jdkrA|jSdS)zk_summary_ Returns: str: encoding of the file or None if it is binary file or undetectable rNsooBinaryFileTextGridrbi(asciizutf-8)charset_normalizerrreadrn from_bytesbestr)rrtextgrid_binary_markrchecking_bytes charset_matchr,r,r-r&s  r)rNrG)rG)N)8__doc__rlloggingr dataclassesrpathlibrtypingrrrrrr tqdm.autor tqdm.contrib.loggingr lhotser r rrrlhotse.parallelr lhotse.qar lhotse.utilsr getLoggerr/rsetLevelINFO NSC_PARTSr%r4r2rCrFrXr7r:r;r<r=r>r?rrkrrrrr,r,r,r-s             - (  ( " "  O $  ) #