pic |ddlZddlZddlmZmZddlZddlmZgdZGddZ GddZ d5d e d e d ejfd Zd5d e dejd e fdZej dfde fdZdZej&dd dedddddddddddfdejded e d e d!ed"e d#e d$ed%e d&ed'eegdfd(ed)e d*e d+efd,ZGd-d.Z d6d/eed0ejd1ed e d ejf d2Z d6d/eed0ejd1ed e d ejf d3Zd/eed e d eefd4Zy)7N)CallableList)version)ruendeesc@eZdZddZdefdZd dZdefdZdefdZy) OnnxWrappercTddladdl}|j}d|_d|_|r-d|j vr|j|dg||_n|j|||_|jd|vrtjddg|_ yd dg|_ y) NrCPUExecutionProvider) providers sess_options)r16kz,This model support only 16000 sampling rate!>@) numpynp onnxruntimeSessionOptionsinter_op_num_threadsintra_op_num_threadsget_available_providersInferenceSessionsession reset_stateswarningswarn sample_rates)selfpathforce_onnx_cpuroptss V/opt/services/ai/voice_agent/venv/lib/python3.12/site-packages/silero_vad/utils_vad.py__init__zOnnxWrapper.__init__ s))+$%!$%! 4 8[8[8]]&77I_H`os7tDL&7747PDL  D= MMH I!&D !%u D src~|jdk(r|jd}|jdkDrtd|j|dk7r|dzdk(r|dz}|dddd|f}d}||jvrtd|jd||jdz dkDr td ||fS) Nr rz*Too many dimensions for input audio chunk rzSupported sampling rates: z (or multiply of 16000)g@?@zInput audio chunk is too short)dim unsqueeze ValueErrorr shape)r!xr(steps r%_validate_inputzOnnxWrapper._validate_input!s 557a< AA 557Q;I!%%'ST T ;BJ!O;D!FdF( AB T&& &9$:K:K9LLcde e  ?U "=> >"u r'ctjd|dfj|_tjd|_d|_d|_y)Nr*r)torchzerosfloat_state_context_last_sr_last_batch_size)r! batch_sizes r%rzOnnxWrapper.reset_states3s@kk1j#"67==?  A  !r'c|j||\}}|dk(rdnd}|jd|k7rtd|jdd|jd}|dk(rdnd }|js|j ||j r |j |k7r|j ||jr |j|k7r|j |t |jstj|||_tj|j|gd }|d vr}|j|jjtj|d d}|jj!d|}|\}} tj"| |_ n t|d| df|_||_||_tj"|}|S)NrzProvided number of samples is z< (Supported values: 256 for 8000 sample rate, 512 for 16000)r@ r r+rrint64)dtype)inputstater(.)r1r.r-r:rr9lenr8r4r5catrr7rarrayrrun from_numpy) r!r/r( num_samplesr; context_size ort_inputsort_outsoutrGs r%__call__zOnnxWrapper.__call__9s$$Q+25[cc 772;+ %=aggbk]KGHI IWWQZ 5[rb $$   j ) MM  3   j )  ! !(=(=(K   j )4==!!KK LADM IIt}}a(a 0  #$779t{{7H7H7JRTRZRZ[]elRZRmnJ||''j9H!JC**51DK, # }~-.  *s# r'cg}|j||\}}|j|dk(rdnd}|jd|zrD||jd|zz }tjj j |d|fdd}td|jd|D]3}|dd|||zf}|j||}|j|5tj|d } | jS) Nrr=r>r rconstantg)valuerB) r1rr.r4nn functionalpadrangerRappendrIcpu) r!r/r(outsrMpad_numi wavs_batch out_chunkstackeds r% audio_forwardzOnnxWrapper.audio_forward^s$$Q+2 5[cc 771: #!QWWQZ+%=>G##''Aw<3'OAq!''!*k2 #A1a+ o-.J j"5I KK " # ))Da({{}r'N)F)r ) __name__ __module__ __qualname__r&intr1rrRrbr'r%r r s1.*S$" #c#J3r'r c2eZdZdZdej fdZy) ValidatorcD|jdrdnd|_tjj |d|jrIddl}|r,d|j vr|jddg|_y|jd|_ytd|_y) Nz.onnxTFz inf.modelrr)r) model_path) endswithonnxr4hubdownload_url_to_filerrrmodelinit_jit_model)r!urlr#rs r%r&zValidator.__init__rsLL1Du  &&sK8 99 "8K<_<_B!B!!B+N)rcrdrer&r4rvrRrgr'r%ririqs @ u|| r'rirr" sampling_ratereturnc tjtj}|tjdkr7 ddgdt |gg}tj j ||\}}n tj|\}}|jd kDr'|j!dd kDr|j#dd }||k7r%tj$j'|||}|j)dS#tj|\}}YxYw# ddlm }||j}|j}|j}n-#t$r!tdtjd d zwxYwYxYw)N2.9channels1rate)effectsr) AudioDecodertorchaudio version z$ requires torchcodec for audio I/O. *Install torchcodec or pin torchaudio < 2.9r T)r+keepdim)rparse torchaudio __version__str sox_effectsapply_effects_fileloadtorchcodec.decodersrget_all_samplesdata sample_rate ImportError RuntimeErrorndimsizemean transformsResamplesqueeze)r"rwta_verr~wavr(rsampless r% read_audiorsg ]]:11 2F  e$$ ,"C(&#m2D)EFG ,,??g?VGC  ood+GC xx!| ahh1dh+ ]##,,R?D ;;q>/ , ood+GC  <&t,<<>ll(( ")**@*@)AAefBC s/5C=4D=DF4EF*E>>Ftensorc|jj}|jdk(r|jd}t j t j} t j|||dy#t$rk|t j dk\rQ ddl m }||d}|j|Yy#t$r!td t jd d zwxYwwxYw) Nr r)bits_per_samplerz) AudioEncoderr)rrz! requires torchcodec for saving. r)detachr[rr,rrrrsave Exceptiontorchcodec.encodersrto_filerr)r"rrwrrencoders r% save_audiors ]]_ "F {{a!!!$ ]]:11 2FfmRH   W]]5) ) <&v5A% ")**@*@)AAbcBC   s#A==!C1!C*C,,C1r[rkchtjj||}|j|S)N) map_location)r4jitreval)rkdevicerps r%rqrqs& IINN:FN ;E JJL Lr'c ddl}|jd|itt|Dcgc]}||z c}j ddddgdt||zgddd ycc}w) Nrprobs)index)rareag?secondszspeech probabilitytab20)figsizekindylimxlimxlabelylabelcolormap)pandas DataFramerYrHplot)rr0pdr/s r%make_visualizationrsoLL'5!*/E *;>Bd7At9As5zD7H3I!,! ?C?#r*rF333333?g{Gz?Tstartc |dS)Nr rg)r/s r%z'get_speech_timestamps..qs 1r')keyend)r4 is_tensorrv TypeErrorrHr.rYrr-rrrrVrWrXrfitemrZmax enumerateminroundr)-rrprrwrrrrrrrrrrrrr^r0min_speech_samplesspeech_pad_samplesmax_speech_samplesmin_silence_samples!min_silence_samples_at_max_speechaudio_length_samples speech_probscurrent_start_samplechunk speech_probprogressprogress_percent triggeredspeechescurrent_speechtemp_endprev_end next_start possible_ends cur_samplesil_durdur sil_dur_nowspeechsilence_durationaudio_length_seconds speech_dicts- r%get_speech_timestampsrs\ ??5 ! RLL'E 5;;!s5;;'( %AMM!$E % u{{ a qr ru-%"71"<% ff  XYM)qrr!.%!7#S &)??$F&6=&)>>ATTWX[mWmm'*AADH(58Q(QTX(X%u:L %a)=?R S 9*,@CV,VW u:+ +HH''++EAs;NQTUZQ[;[7\3]^EE=1668 K('*== * *+H$';;sB % &'7 8 9IHNI,d3 HHzM#L1J;(1,  9 $( 8+G::$$h%89HH$'  9 $iI&0N7 #  *~g'>>ASS-- #M~ F #(0u%/!#%^ : 55.8N7+ %I3444: " ,4N5)OON3%'N!H,$) 2<w/788H8zH$&M-7N5)OON3%'N788H8zH %I$&M - 'Y%$x/K1kDe6e#00(0u%"5)N7,CCGYYOON3!#3444:! " UJX/.2IIM__ 4u'x( _ 6 6!#a;M)M"NOF7O H ! !'!}W5u E !&8"88u %5%:!;; ),SHQqSM'4JM]abMb4b-c)d1 g& #C(`$a bu ),SHQqSM'4JM_4_-`)a1 g&$8&-J\:\ ]^F5M _3mC# wK#&u[-AM-QSb'cef#gK !$U;u+= +M%_au!vK  w # 'K  D (  $ &  '<)<})LM OG RPQ Qs T Tc jeZdZ d dedededefdZdZejd defdZ y ) VADIteratorrrwrrc||_||_||_|dvr td||zdz |_||zdz |_|j y)ab Class for stream imitation Parameters ---------- model: preloaded .jit/.onnx silero VAD model threshold: float (default - 0.5) Speech threshold. Silero VAD outputs speech probabilities for each audio chunk, probabilities ABOVE this value are considered as SPEECH. It is better to tune this parameter for each dataset separately, but "lazy" 0.5 is pretty good for most datasets. sampling_rate: int (default - 16000) Currently silero VAD models support 8000 and 16000 sample rates min_silence_duration_ms: int (default - 100 milliseconds) In the end of each speech chunk wait for min_silence_duration_ms before separating it speech_pad_ms: int (default - 30 milliseconds) Final speech chunks are padded by speech_pad_ms each side rCzDVADIterator does not support sampling rates other than [8000, 16000]rN)rprrwr-rrr)r!rprrwrrs r%r&zVADIterator.__init__s_: "*  -cd d#03J#JT#Q "/-"?$"F r'cb|jjd|_d|_d|_y)NFr)rprrrcurrent_sample)r!s r%rzVADIterator.reset_statess( ! r'rctj|s tj|}|j dk(rt |dn t |}|xj |z c_|j||jj}||jk\r|jrd|_ ||jk\rb|jsVd|_ td|j |jz |z }d|s t|iSt!||jz |iS||jdz kr|jr|js|j |_ |j |jz |j"kry|j|jz|z }d|_ d|_ d |s t|iSt!||jz |iSy#tdxYw) a= x: torch.Tensor audio chunk (see examples in repo) return_seconds: bool (default - False) whether return timestamps in seconds (default - samples) time_resolution: int (default - 1) time resolution of speech coordinates when requested as seconds rr*rTrrNFr)r4rrvrr+rHrrprwrrrrrrrfrr)r!r/rrrr speech_start speech_ends r%rRzVADIterator.__call__sq! VLLO,-557a 4>> )t}}DM 4>> )4>>!DNq$"5"58O8O"ORe"efLnS.E E%P\_c_q_qPqtCKDE E $..4/ /T^^== $ 3 3 ""T]]2T5M5MM!]]T-D-DDGZZ ! !&ns:CC%PZ]a]o]oPorAKBCC7 V TUUs G G-N)rrrr)Fr ) rcrdrer6rfr&rr4rurRrgr'r%rrsd%(&+03&( &!&!$&+. & !$ &P U]]_***r'rtssrrc|r |s tdt}|r t||n|}|D]}|j||d|dt j |S)aCollect audio chunks from a longer audio clip This method extracts audio chunks from an audio clip, using a list of provided coordinates, and concatenates them together. Coordinates can be passed either as sample numbers or in seconds, in which case the audio sampling rate is also needed. Parameters ---------- tss: List[dict] Coordinate list of the clips to collect from the audio. wav: torch.Tensor, one dimensional One dimensional float torch.Tensor, containing the audio to clip. seconds: bool (default - False) Whether input coordinates are passed as seconds or samples. sampling_rate: int (default - None) Input audio sampling rate. Required if seconds is True. Returns ------- torch.Tensor, one dimensional One dimensional float torch.Tensor of the concatenated clipped audio chunks. Raises ------ ValueError Raised if sampling_rate is not provided when seconds is True. 3sampling_rate must be provided when seconds is Truerrr-list_seconds_to_samples_tssrZr4rI)rrrrwchunks_tssr^s r%collect_chunksr(siD}NOO VF:A "3 6sD 0 c!G*QuX./0 99V r'c|r |s tdt}d}|r t||n|}|D]}|j|||d|d} |j||dt j |S)aDrop audio chunks from a longer audio clip This method extracts audio chunks from an audio clip, using a list of provided coordinates, and drops them. Coordinates can be passed either as sample numbers or in seconds, in which case the audio sampling rate is also needed. Parameters ---------- tss: List[dict] Coordinate list of the clips to drop from from the audio. wav: torch.Tensor, one dimensional One dimensional float torch.Tensor, containing the audio to clip. seconds: bool (default - False) Whether input coordinates are passed as seconds or samples. sampling_rate: int (default - None) Input audio sampling rate. Required if seconds is True. Returns ------- torch.Tensor, one dimensional One dimensional float torch.Tensor of the input audio minus the dropped chunks. Raises ------ ValueError Raised if sampling_rate is not provided when seconds is True. rrrrNr )rrrrwr  cur_startr r^s r% drop_chunksrVsD}NOO VFI:A "3 6sD  s9aj13eH  MM#ij/" 99V r'cn|Dcgc]%}t|d|zt|d|zd'c}Scc}w)zDConvert coordinates expressed in seconds to sample coordinates. rr)rr)r)rrwcrds r%r r sJ   s7|$}4SZ =0   s*2)r)FN)r4rtypingrrr packagingr languagesr rirrfrvrrrrqrrur6boolrrdictrrr rgr'r%rsE ! $ ddN2S@S%,,s2'%,,u-s#.1/48;9>u9