o 9wi&@sddlZddlmZmZmZddlmZddlZddlZddl m Z ddl m Z ddl mZddlmZddlmZmZdd lmZdd lmZdd lmZdd dZGdddZGdddeZGdddeZdS)N)CallableTextUnion)Optional)Model) AudioFile)VoiceActivityDetection) PipelineModel) AnnotationSlidingWindowFeature)Segment)Vad?Zd;?c Cstj}tjtjtjt}tj|dd|dur-tj |dd}tj|}ntj|}tj |s@t d|tj |rStj |sSt |dt|d}tj||d}||d d d } t|t|d } | | | S) NT)exist_okassetszpytorch_model.binzModel file not found at z! exists and is not a regular filerb)use_auth_tokeng?)onsetoffsetmin_duration_onmin_duration_off) segmentationdevice)torchhub_get_torch_homeospathdirnameabspath__file__makedirsjoinexistsFileNotFoundErrorisfile RuntimeErroropenreadrfrom_pretrainedVoiceActivitySegmentationr instantiate) r vad_onset vad_offsetrmodel_fp model_dirmain_dir model_bytes vad_modelhyperparameters vad_pipeliner6S/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/whisperx/vads/pyannote.pyload_vad_models*    r8csheZdZdZddddddedfdedeeded ed ed ed effd d ZdedefddZ Z S)BinarizeaBinarize detection scores using hysteresis thresholding, with min-cut operation to ensure not segments are longer than max_duration. Parameters ---------- onset : float, optional Onset threshold. Defaults to 0.5. offset : float, optional Offset threshold. Defaults to `onset`. min_duration_on : float, optional Remove active regions shorter than that many seconds. Defaults to 0s. min_duration_off : float, optional Fill inactive regions shorter than that many seconds. Defaults to 0s. pad_onset : float, optional Extend active regions by moving their start time by that many seconds. Defaults to 0s. pad_offset : float, optional Extend active regions by moving their end time by that many seconds. Defaults to 0s. max_duration: float The maximum length of an active segment, divides segment at timestamp with lowest score. Reference --------- Gregory Gelly and Jean-Luc Gauvain. "Minimum Word Error Training of RNN-based Voice Activity Detection", InterSpeech 2015. Modified by Max Bain to include WhisperX's min-cut operation https://arxiv.org/abs/2303.00747 Pyannote-audio rNinfrrrr pad_onset pad_offset max_durationcs<t||_|p ||_||_||_||_||_||_dSN) super__init__rrr<r=rrr>)selfrrrrr<r=r> __class__r6r7rASs   zBinarize.__init__scoresreturncsD|jj\}}|jfddt|D}t}t|jjD]\}}|jdur(|n|j|}|d} |d|jk} |dg} | g} | } t |dd|ddD]|\} }| r| | }||j krt | d}|t | |d}| |}t| |j||j}||||f<| |} | |dd} | |dd} n||jkrt| |j| |j}||||f<| } d} g} g} | || | qO||jkr| } d} qO| rt| |j| |j}||||f<q|jd ks|jd ks|jd kr|j td krtd |j|jd }|jdkr t|D]\}}|j|jkr|||f=q|S) zBinarize detection scores Parameters ---------- scores : SlidingWindowFeature Detection scores. Returns ------- active : Annotation Binarized scores. csg|]}|jqSr6)middle).0iframesr6r7 ysz%Binarize.__call__..NrFTr:r;z+This would break current max_duration param)collar)datashapesliding_windowranger enumerateTlabelsrzipr>lennpargminr r<r=rappendrfloatNotImplementedErrorsupportrlist itertracksduration)rBrE num_frames num_classes timestampsactivekk_scoreslabelstart is_active curr_scorescurr_timestampsty curr_duration search_aftermin_score_div_idx min_score_tregionsegmenttrackr6rJr7__call__ksd  "            zBinarize.__call__) __name__ __module__ __qualname____doc__r\rrAr r rv __classcell__r6r6rCr7r92s2"r9c sVeZdZ   ddededeedfffdd Zdd ed e e d e fd d Z Z S)r+pyannote/segmentationFNrfscorerc stjd|||d|dS)N)rr}rr6)r@rA)rBrr}rinference_kwargsrCr6r7rAsz"VoiceActivitySegmentation.__init__filehookrFcCsR|j||d}|jr"|j|vr||j}|S||}|||j<|S||}|S)aApply voice activity detection Parameters ---------- file : AudioFile Processed file. hook : callable, optional Hook called after each major step of the pipeline with the following signature: hook("step_name", step_artefact, file=file) Returns ------- speech : Annotation Speech regions. )r) setup_hooktrainingCACHED_SEGMENTATION _segmentation)rBrr segmentationsr6r6r7applys    zVoiceActivitySegmentation.apply)r|FNr?)rwrxryr boolrrrArrrr rr{r6r6rCr7r+s $ r+csXeZdZdfdd ZdefddZeddZe dd ed e efd d Z Z S)PyannoteNc s,tdt|dt|||d|_dS)Nz7>>Performing voice activity detection using Pyannote...r-)rr/)printr@rAr8r5)rBrrr/kwargsrCr6r7rAszPyannote.__init__audiocKs ||Sr?)r5)rBrrr6r6r7rvs zPyannote.__call__cCst|dS)Nr)r from_numpy unsqueeze)rr6r6r7preprocess_audioszPyannote.preprocess_audiorrrcCs~|dksJt|||d}||}g}|D] }|t|j|jdqt|dkr1tdgS|s7Jdt ||||S)Nr)r>rrUNKNOWNzNo active speech found in audiozsegments_list is empty.) r9 get_timeliner[SegmentXriendrXrr merge_chunks)segments chunk_sizerrbinarize segments_list speech_turnr6r6r7rs    zPyannote.merge_chunks)NN)rN) rwrxryrArrv staticmethodrr\rrr{r6r6rCr7rs r)rrNN)rtypingrrrrnumpyrYrpyannote.audiorpyannote.audio.core.iorpyannote.audio.pipelinesrpyannote.audio.pipelines.utilsr pyannote.corer r r whisperx.diarizerwhisperx.vads.vadr r8r9r+rr6r6r6r7s$           -