j( JddlZddlZddlmZddlmZmZm Z m Z ddl Z ddl m Z mZddlmZmZmZddlmZeeZGddZGd d Z dd ejd eeefdeeeeefdedeeeff dZGddZ dS)N)Pipeline)OptionalUnionListTuple) load_audio SAMPLE_RATE)TranscriptionResultAlignedTranscriptionResultProgressCallback) get_loggerc eZdZdZdeeeeeffdZdededeeeeffdZ dede efd Z d S) IntervalTreez Simple interval tree for fast overlap queries using sorted array + binary search. Uses O(n) space and provides O(log n) query time instead of O(n) linear scan. This achieves ~228x speedup for speaker assignment in long-form content. intervalsc|s;tjg|_tjg|_g|_dSt |d}tjd|Dtj|_tjd|Dtj|_d|D|_dS)z Initialize the interval tree with diarization segments. Args: intervals: List of (start, end, speaker) tuples Nc|dS)Nrxs s/home/lighthouse/badge-speaker-benchmark/.benchmark-venv-community/lib/python3.11/site-packages/whisperx/diarize.pyz'IntervalTree.__init__..$s 1Q4keycg|] }|d S)rr.0is r z)IntervalTree.__init__..%s???!???r)dtypecg|] }|d S)rrs rrz)IntervalTree.__init__..&s===qad===rcg|] }|d S)rrs rrz)IntervalTree.__init__..'s888!1888r)nparraystartsendsspeakerssortedfloat64)selfrsorted_intervalss r__init__zIntervalTree.__init__s (2,,DK DI')DM F")@@@h??.>???rzRRR H==,<===RZPPP 88'7888 rstartendreturnct|jdkrgStj|j|d}|dkrgSt d|}|j||k|j||kz}g}tj|dD]a}t|j||t|j||z }|dkr"| |j ||fb|S)a) Find all intervals that overlap with [start, end] and compute intersection. Args: start: Query interval start time end: Query interval end time Returns: List of (speaker, intersection_duration) tuples for overlapping segments rleft)side) lenr'r% searchsortedslicer(whereminmaxappendr)) r,r/r0 right_idx candidatesoverlapsresultsidx intersections rqueryzIntervalTree.query)s t{  q IODK6BBB >>I1i(( K +c1di 6Ke6ST8H%%a( C CCty~s33c$+c:JE6R6RRLa c 2LABBBrtimect|jdkrdS|j|jzdz }tjtj||z }|j|S)z Find the speaker of the nearest segment to a given time point. Args: time: Time point to find nearest segment for Returns: Speaker ID of nearest segment, or None if no segments exist rNr$)r5r'r(r%argminabsr))r,rCmids nearest_idxs r find_nearestzIntervalTree.find_nearestHs] t{  q 4 di'1,itd{ 3 344 }[))rN) __name__ __module__ __qualname____doc__rrfloatstrr.rBrrIrrrrrs9$uUE3->'?"@9999&5ueCJ6G1H>**8C=******rrceZdZ ddeeeejffdZ ddeee j fdee dee d ee d e d e d eeejeeeeeffejffd ZdS)DiarizationPipelineNcpudevicect|trtj|}|pd}td|t j|||||_ dS)Nz(pyannote/speaker-diarization-community-1zLoading diarization model: )token cache_dir) isinstancerOtorchrSloggerinforfrom_pretrainedtomodel)r, model_namerUrSrV model_configs rr.zDiarizationPipeline.__init__\sx fc " " *\&))F!O%O  @,@@AAA-l%S\]]]``aghh rFaudio num_speakers min_speakers max_speakersreturn_embeddingsprogress_callbackr1c t|trt|}tj|dddft d}d}ddd dgd fd }|j|f|||d|d |ini}  d | j} |r| jndtj | d gd } | d d| d<| d d| d<|r31fdt| D} | | fS|r| dfS| S)a Perform speaker diarization on audio. Args: audio: Path to audio file or audio array num_speakers: Exact number of speakers (if known) min_speakers: Minimum number of speakers to detect max_speakers: Maximum number of speakers to detect return_embeddings: Whether to return speaker embeddings progress_callback: Optional callable receiving a float (0-100) with progress percentage Returns: If return_embeddings is True: Tuple of (diarization dataframe, speaker embeddings dictionary) Otherwise: Just the diarization dataframe N)waveform sample_rate)I@)rjX@) segmentation embeddingsric|[|[|dkrW|d\}}|t||z d||z zz}| dkr| d< |dSdSdSdSdS)Nr)rirkg?)getr9) step_name step_artifactfiletotal completedoffsetr0pct _STEP_RANGESlast_pctres rhookz*DiarizationPipeline.__call__..hooks$)>5199"."2"29k"J"JKFC 3y5'8##>#>#,#OOCXa[((&) ))#..... %$)>)>99)(r)rarbrcrygY@T) yield_label)segmentlabelspeaker)columnsr{c|jSN)r/rs rrz.DiarizationPipeline.__call__..sAGrr/c|jSr)r0rs rrz.DiarizationPipeline.__call__..s!%rr0cLi|] \}}||!Sr)tolist)rsr}rms r z0DiarizationPipeline.__call__..s/!t!t!tja':a=+?+?+A+A!t!t!tr)NNN)rWrOrrX from_numpyr r]speaker_diarizationspeaker_embeddingspd DataFrame itertracksapply enumeratelabels)r,r`rarbrcrdre audio_dataryoutput diarization diarize_dfrrwrmrxs ` @@@r__call__zDiarizationPipeline.__call__is4 eS ! ! &u%%E(tQQQw88&    ( !,*LuH / / / / / / / /  %%%   "&!1~~r     (  e $ $ $0 2CMV.. \+"8"8T"8"J"JTsTsTsttt (399:K:KLL 7&y177HH 5  2!7!t!t!t!tT]^i^p^p^r^rTsTs!t!t!t 11 1  t# # r)NNrRN)NNNFN)rJrKrLrrrOrXrSr.r%ndarrayintboolr tuplerrdictlistrNrrrrrQrQ[s5: i isEL012 i i i i '+&*&*"'.2MMS"*_%MsmMsm M sm M  M,M uR\8Dd5k1A,B#CCDblR SMMMMMMrrQFrtranscript_resultr fill_nearestr1c|dg}|r|t|dkr|Sd|D}t|}|D]}|dd}|dd} ||| } | rTi} | D]!\} } | | d| z| | <"t | d d|d <n&|r$|| zd z }||}|r||d <d |vr|d D]}d|vr|d}|d|}|||}|rTi} |D]!\} } | | d| z| | <"t | d d|d <|r$||zd z }||}|r||d <|||d<|S)a Assign speakers to words and segments in the transcript. Uses an interval tree for O(log n) overlap queries instead of O(n) linear scan, achieving ~228x speedup for long-form content (3+ hour podcasts). Args: diarize_df: Diarization dataframe from DiarizationPipeline transcript_result: Transcription result to augment with speaker labels speaker_embeddings: Optional dictionary mapping speaker IDs to embedding vectors fill_nearest: If True, assign speakers even when there's no direct time overlap Returns: Updated transcript_result with speaker assignments and optionally embeddings segmentsNrcBg|]\}}|d|d|dfSr/r0r}r)r_rows rrz(assign_word_speakers..s? As Ws5z3y>2rr/rir0c|dSNr"rrs rrz&assign_word_speakers..s aPQdrrr}r$wordsc|dSrrrs rrz&assign_word_speakers..s WXYZW[rr)ror5iterrowsrrBr:itemsrI)rrrrtranscript_segmentsrtreeseg seg_startseg_endr>speaker_intersectionsr}rAseg_midnearest_speakerword word_startword_end word_overlapsword_mids rassign_word_speakersrs*,// B?? !*"4J18L8L   ))++I  " "D"(:(:GGGS)) ''%%%::i11  168 !)1 h h%1F1J1J7TW1X1X[g1g%g.. !6!!>NNSSSTUVC NN  1 7*a/G"//88O 1!0I c>>G  : :$&&!'] 88E:66 $ :x @ @  :,.)1>pp-9N9R9RSZ\_9`9`co9o-g66&)*?*E*E*G*G^^&\&\&\]^&_DOO!: *X 5:H&*&7&7&A&AO&:*9Y%2D./ rc0eZdZddededeefdZdS)SegmentNr/r0r}c0||_||_||_dSrr)r,r/r0r}s rr.zSegment.__init__ s  rr)rJrKrLrrrOr.rrrrr sFSc8C=rr)NF)!numpyr%pandasrpyannote.audiortypingrrrrrXwhisperx.audiorr whisperx.schemar r r whisperx.log_utilsr rJrYrrQrrrOrrNrrrrrrrs######//////////// 22222222]]]]]]]]]])))))) H  J*J*J*J*J*J*J*J*Z[[[[[[[[B<@ NN N79LLMN!c4;&6!78N N  %': :; NNNNbr