o %ݫi@sLdZddlZddlZddlmZddlmZddlmZGdddeZ dS)a\ Specifies the inference interfaces for Voice Activity Detection (VAD) modules. Authors: * Aku Rouhe 2021 * Peter Plantinga 2021 * Loren Lugosch 2020 * Mirco Ravanelli 2020 * Titouan Parcollet 2021 * Abdel Heba 2021 * Andreas Nautsch 2022, 2023 * Pooneh Mousavi 2023 * Sylvain de Langen 2023 * Adel Moumen 2023 * Pradnya Kandarkar 2023 N) Pretrained) split_path)fetchcseZdZdZgdZgdZfddZ   d5d d Zd d Zd6ddZ d7ddZ d8ddZ d9ddZ d9ddZ d:ddZ   d;d d!Zdd,d-Z .d?d/d0Z            d@d1d2Zd6d3d4ZZS)AVADaA ready-to-use class for Voice Activity Detection (VAD) using a pre-trained model. Arguments --------- *args : tuple **kwargs : dict Arguments are forwarded to ``Pretrained`` parent class. Example ------- >>> import torchaudio >>> from speechbrain.inference.VAD import VAD >>> # Model is downloaded from the speechbrain HuggingFace repo >>> tmpdir = getfixture("tmpdir") >>> VAD = VAD.from_hparams( ... source="speechbrain/vad-crdnn-libriparty", ... savedir=tmpdir, ... ) >>> # Perform VAD >>> boundaries = VAD.get_speech_segments("tests/samples/single-mic/example1.wav") ) sample_ratetime_resolutiondevice)compute_features mean_var_normmodelcs*tj|i||jj|_|jj|_dSN)super__init__hparamsrr)selfargskwargs __class__M/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/inference/VAD.pyr6s z VAD.__init__ FcCs||\}}||jkrtdt||}t||}|} |r$|d} t|| } g} d} d} | ||kr9d} tjt|| |d\}}||j}| sT|j d|krft j d||jd }t j ||gdd }t j jj|ddd|fd| fd }|ddd}||}|d d d dd d f}|r||}|ddd}t|j d||j}t||j}t| |j}t j jj|d|fd|fd|fd }|ddd}| || rn| |} q1t j | dd }t||j|}|d d d|d d f}|S)a_Outputs the frame-level speech probability of the input audio file using the neural model specified in the hparam file. To make this code both parallelizable and scalable to long sequences, it uses a double-windowing approach. First, we sequentially read non-overlapping large chunks of the input signal. We then split the large chunks into smaller chunks and we process them in parallel. Arguments --------- audio_file: path Path of the audio file containing the recording. The file is read with torchaudio. large_chunk_size: float Size (in seconds) of the large chunks that are read sequentially from the input audio file. small_chunk_size: float Size (in seconds) of the small chunks extracted from the large ones. The audio signal is processed in parallel within the small chunks. Note that large_chunk_size/small_chunk_size must be an integer. overlap_small_chunk: bool True, creates overlapped small chunks. The probabilities of the overlapped chunks are combined using hamming windows. Returns ------- prob_vad: torch.Tensor torch.Tensor containing the frame-level speech probabilities for the input audio file. FThe detected sample rate is different from that set in the hparam fileFrT frame_offset num_framesrdim) kernel_sizestrideN) output_sizer#r$)_get_audio_infor ValueErrorint torchaudioloadstrtorshapetorchzeroscatnn functionalunfold unsqueezesqueeze transposeget_speech_prob_chunk_manage_overlapped_chunkspermuterfoldappend)r audio_filelarge_chunk_sizesmall_chunk_sizeoverlap_small_chunkr audio_lenlong_chunk_lensmall_chunk_lensmall_chunk_stepsmall_chunk_len_step prob_chunks last_chunk begin_sample large_chunkfspadding small_chunkssmall_chunks_probout_len kernel_lenstep_lenprob_vad last_elemrrrget_speech_prob_file;s|%        DzVAD.get_speech_prob_filecCstj|jd|jd}t|jdd}|d|df||dd|d|df<|dd|f|d|d|dd|f<|dd|dd|dd<|S)zgThis support function manages overlapped the case in which the small chunks have a 50% overlap.rr rrNr)r/hamming_windowr.rr)r5)rrMrT half_pointrrrr9s(    zVAD._manage_overlapped_chunksNcCst|jdkr |d}|durtj|jd|jd}||j||j}}|}|j |}|j ||}|j |}| |jd|jd|jd|jd}|j |\}}|j|}t|}|S)a3Outputs the frame-level posterior probability for the input audio chunks Outputs close to zero refers to time steps with a low probability of speech activity, while outputs closer to one likely contain speech. Arguments --------- wavs : torch.Tensor Batch of waveforms [batch, time, channels] or [batch, time] depending on the model. Make sure the sample rate is fs=16000 Hz. wav_lens : torch.Tensor Lengths of the waveforms relative to the longest one in the batch, tensor of shape [batch]. The longest one should have relative length 1.0 and others len(waveform) / max_length. Used for ignoring padding. Returns ------- torch.Tensor The encoded batch rrNr r)lenr.r5r/onesrr-floatmodsr r cnnreshapernndnnsigmoid)rwavswav_lensfeatsoutputsh output_probrrrr8s$     zVAD.get_speech_prob_chunk??cCs||kd}||kd}td|jdD],}|dd|df|dd|ddfO<|dd|df|dd|dfM<q||jS)aScans the frame-level speech probabilities and applies a threshold on them. Speech starts when a value larger than activation_th is detected, while it ends when observing a value lower than the deactivation_th. Arguments --------- vad_prob: torch.Tensor Frame-level speech probabilities. activation_th: float Threshold for starting a speech segment. deactivation_th: float Threshold for ending a speech segment. Returns ------- vad_th: torch.BoolTensor torch.Tensor containing 1 for speech regions and 0 for non-speech regions. cpurN.)r-ranger.r)rvad_prob activation_thdeactivation_thframe_does_not_deactivatevad_thirrrapply_threshold s ,* zVAD.apply_thresholdsecondscCsNtj|ddd}d|dddddf<||}|dddddfdk|dddddf<|dddddfdk|dddddf<|dkjdddkrktj|tdgdd|j fdd}|dkdddf dd}|dddfd|dddf<||j  }|j |}|d kr|}|S|}|S) a Computes the time boundaries where speech activity is detected. It takes in input frame-level binary decisions (1 for speech, 0 for non-speech) and outputs the begin/end second (or sample) of each detected speech region. Arguments --------- prob_th: torch.Tensor Frame-level binary decisions (1 for speech frame, 0 for a non-speech one). The tensor can be obtained from apply_threshold. output_value: 'seconds' or 'samples' When the option 'seconds' is set, the returned boundaries are in seconds, otherwise, it reports them in samples. Returns ------- boundaries: torch.Tensor torch.Tensor containing the start second (or sample) of speech segments in even positions and their corresponding end in odd positions (e.g, [1.0, 1.5, 5,.0 6.0] means that we have two speech segment; one from 1.0 to 1.5 seconds and another from 5.0 to 6.0 seconds). r)dimsshiftsrNrr?r!rq)r/rollr)nonzeror.r1Tensorr5r-rr\rrYrround)rprob_th output_valueprob_th_shiftedindexesrqsamples boundariesrrrget_boundaries2s000  zVAD.get_boundariesc Csg}|jddkr |S|d}|d}td|jdD]&}||df}||}||kr6||df}q|||g|}||df}q|||gt||j}|S)aMerges segments that are shorter than the given threshold. Arguments --------- boundaries : str torch.Tensor containing the speech boundaries. It can be derived using the get_boundaries method. close_th: float If the distance between boundaries is smaller than close_th, the segments will be merged. Returns ------- new_boundaries The new boundaries with the merged segments. r)rr)rrr)r.rYrir<r/ FloatTensorr-r) rr~close_thnew_boundaries prev_beg_seg prev_end_segrobeg_segsegment_distancerrrmerge_close_segmentsps    zVAD.merge_close_segmentscCslg}t|jdD]!}||df||df}||kr*|||df||dfgq t||j}|S)aRemoves segments that are too short. Arguments --------- boundaries : torch.Tensor torch.Tensor containing the speech boundaries. It can be derived using the get_boundaries method. len_th: float If the length of the segment is smaller than close_th, the segments will be merged. Returns ------- new_boundaries The new boundaries without the short segments. rr)rir.r<r/rr-r)rr~len_thrroseg_lenrrrremove_short_segmentsszVAD.remove_short_segmentsTcCs|dur t|ddd}|dur||\}}||}|jtjkr#d}nd}d} d} t|jdD]b} || df} || df} | | kri| d} d ||d }|rYt|| | | f|duri||| | | fd | d} d ||d }|rt|| | | f|dur||| | | fd | } q0|dur| |kr| d} d ||d }|rt|| | |f|dur||| | |fd |dur| dSdS) aSaves the boundaries on a file (and/or prints them) in a readable format. Arguments --------- boundaries: torch.Tensor torch.Tensor containing the speech boundaries. It can be derived using the get_boundaries method. save_path: path When to store the text file containing the speech/non-speech intervals. print_boundaries: Bool Prints the speech/non-speech intervals in the standard outputs. audio_file: path Path of the audio file containing the recording. The file is read with torchaudio. It is used here to detect the length of the signal. Nwzutf-8)modeencodingz% iz% .2f rrz segment_%03d NON_SPEECH SPEECH) openr'dtyper/r)rir.printwriteclose)rr~ save_pathprint_boundariesr=frrA value_formatlast_endcnt_segro begin_value end_value print_strrrrsave_boundariessP    zVAD.save_boundariesư>cCsr||\}}||jkrtdt|j|}g} t|jdD]} t|| df|} t|| df|} | | } tj|| | d\}}| |j }|j |||d}| d|}|}||d|d}|dd}|j|||d }|j|d d }t|jdD]!}|| df||df}|| df||df}| ||gqq t|  |j } | S) aApplies energy-based VAD within the detected speech segments.The neural network VAD often creates longer segments and tends to merge segments that are close with each other. The energy VAD post-processes can be useful for having a fine-grained voice activity detection. The energy VAD computes the energy within the small chunks. The energy is normalized within the segment to have mean 0.5 and +-0.5 of std. This helps to set the energy threshold. Arguments --------- audio_file: path Path of the audio file containing the recording. The file is read with torchaudio. boundaries: torch.Tensor torch.Tensor containing the speech boundaries. It can be derived using the get_boundaries method. activation_th: float A new speech segment is started it the energy is above activation_th. deactivation_th: float The segment is considered ended when the energy is <= deactivation_th. eps: float Small constant for numerical stability. Returns ------- new_boundaries The new boundaries that are post-processed by the energy VAD. rrrr) chunk_size chunk_striderrrfrkrlrqrz)r'rr(r)rrir.r*r+r-r create_chunksabssumlogmeanstdr5rprr<r/r)rr=r~rkrlepsrrA chunk_lenrrorH end_samplersegment_segment_chunks energy_chunks energy_vadenergy_boundariesjstart_enend_endrrr energy_VAD sR)    zVAD.energy_VAD@cCs.|d||}||jd|jdd}|S)a Splits the input into smaller chunks of size chunk_size with an overlap chunk_stride. The chunks are concatenated over the batch axis. Arguments --------- x: torch.Tensor Signal to split into chunks. chunk_size : int The size of each chunk. chunk_stride: int The stride (hop) of each chunk. Returns ------- x: torch.Tensor A new tensors with the chunks derived from the input signal. rrr)r4r\r.)rxrrrrrrnszVAD.create_chunkscCs"tt|}|j}|j}||fS)z>Returns the sample rate and the length of the input audio file)r*infor,rr)rr=metadatarrArrrr'szVAD._get_audio_info{Gz?c Cs||\}}||jkrtdd}t||}|}d} tjd||jd} ||krF|d| df| d||f<| d} ||}||}||ks)| S)aUpsamples the output of the vad to help visualization. It creates a signal that is 1 when there is speech and 0 when there is no speech. The vad signal has the same resolution as the input one and can be opened with it (e.g, using audacity) to visually figure out VAD regions. Arguments --------- vad_out: torch.Tensor torch.Tensor containing 1 for each frame of speech and 0 for each non-speech frame. audio_file: path The original audio file used to compute vad_out time_resolution : float Time resolution of the vad_out signal. Returns ------- vad_signal The upsampled version of the vad_out tensor. rrrr )r'rr(r)r/r0r) rvad_outr=rrsig_lenbeg_samp step_sizeend_sampindex vad_signalrrr upsample_VADs"  zVAD.upsample_VADc Cs||\}}||jkrtdtjd||jd}t|jdD]}t||df|}t||df|}d|d||f<q |S)aBased on the input boundaries, this method creates a signal that is 1 when there is speech and 0 when there is no speech. The vad signal has the same resolution as the input one and can be opened with it (e.g, using audacity) to visually figure out VAD regions. Arguments --------- boundaries: torch.Tensor torch.Tensor containing the boundaries of the speech segments. audio_file: path The original audio file used to compute vad_out Returns ------- vad_signal The output vad signal with the same resolution of the input one. rrr rrt) r'rr(r/r0rrir.r)) rr~r=rrrro beg_samplerrrrupsample_boundariess zVAD.upsample_boundariescCs||\}}g}t|jdD]@}t||df|}t||df|} | |} tjt||| d\} } || } | |krP| ||df||dfgqt | |j }|S)aTakes in input the boundaries of the detected speech segments and double checks (using the neural VAD) that they actually contain speech. Arguments --------- boundaries: torch.Tensor torch.Tensor containing the boundaries of the speech segments. audio_file: path The original audio file used to compute vad_out. speech_th: float Threshold on the mean posterior probability over which speech is confirmed. Below that threshold, the segment is re-assigned to a non-speech region. Returns ------- new_boundaries The boundaries of the segments where speech activity is confirmed. rrr)r'rir.r)r*r+r,r8rr<r/rr-r)rr~r= speech_thrrrrorrlen_segrrJ speech_probrrrdouble_check_speech_segmentss   z VAD.double_check_speech_segments皙?cCs||\}}||jkrtdg}t|jdD];}||df|} ||df|} ttd| ||} tt|| ||} | | } tj || | d\} } | | q|S)aqReturns a list containing all the detected speech segments. Arguments --------- boundaries: torch.Tensor torch.Tensor containing the boundaries of the speech segments. audio_file: path The original audio file used to compute vad_out. before_margin: float Used to cut the segments samples a bit before the detected margin. after_margin: float Use to cut the segments samples a bit after the detected margin. Returns ------- segments: list List containing the detected speech segments rrrr) r'rr(rir.r)maxminr*r+r<)rr~r= before_margin after_marginrrsegmentsrorrr vad_segmentrJrrr get_segmentss&  zVAD.get_segmentscCst|\}}t||d}|j||||d}|j|| | d}|j|dd}|r1|j||| | d}|j||d}|j||d}|rI|j ||| d}|S) a'Detects speech segments within the input file. The input signal can be both a short or a long recording. The function computes the posterior probabilities on large chunks (e.g, 30 sec), that are read sequentially (to avoid storing big signals in memory). Each large chunk is, in turn, split into smaller chunks (e.g, 10 seconds) that are processed in parallel. The pipeline for detecting the speech segments is the following: 1- Compute posteriors probabilities at the frame level. 2- Apply a threshold on the posterior probability. 3- Derive candidate speech segments on top of that. 4- Apply energy VAD within each candidate segment (optional). 5- Merge segments that are too close. 6- Remove segments that are too short. 7- Double check speech segments (optional). Arguments --------- audio_file : str Path to audio file. large_chunk_size: float Size (in seconds) of the large chunks that are read sequentially from the input audio file. small_chunk_size: float Size (in seconds) of the small chunks extracted from the large ones. The audio signal is processed in parallel within the small chunks. Note that large_chunk_size/small_chunk_size must be an integer. overlap_small_chunk: bool If True, it creates overlapped small chunks (with 50% overlap). The probabilities of the overlapped chunks are combined using hamming windows. apply_energy_VAD: bool If True, a energy-based VAD is used on the detected speech segments. The neural network VAD often creates longer segments and tends to merge close segments together. The energy VAD post-processes can be useful for having a fine-grained voice activity detection. The energy thresholds is managed by activation_th and deactivation_th (see below). double_check: bool If True, double checks (using the neural VAD) that the candidate speech segments actually contain speech. A threshold on the mean posterior probabilities provided by the neural network is applied based on the speech_th parameter (see below). close_th: float If the distance between boundaries is smaller than close_th, the segments will be merged. len_th: float If the length of the segment is smaller than close_th, the segments will be merged. activation_th: float Threshold of the neural posteriors above which starting a speech segment. deactivation_th: float Threshold of the neural posteriors below which ending a speech segment. en_activation_th: float A new speech segment is started it the energy is above activation_th. This is active only if apply_energy_VAD is True. en_deactivation_th: float The segment is considered ended when the energy is <= deactivation_th. This is active only if apply_energy_VAD is True. speech_th: float Threshold on the mean posterior probability within the candidate speech segment. Below that threshold, the segment is re-assigned to a non-speech region. This is active only if double_check is True. Returns ------- boundaries: torch.Tensor torch.Tensor containing the start second of speech segments in even positions and their corresponding end in odd positions (e.g, [1.0, 1.5, 5,.0 6.0] means that we have two speech segment; one from 1.0 to 1.5 seconds and another from 5.0 to 6.0 seconds). )source)r>r?r@rrqr)r)r)r) rrrSrprYrrrrr)rr=r>r?r@apply_energy_VAD double_checkrrrkrlen_activation_then_deactivation_thrrflrFryr~rrrget_speech_segments<s< Y zVAD.get_speech_segmentscCs |||S)z,Gets frame-level speech-activity predictions)r8)rr`rarrrforwards z VAD.forward)rrFr )rfrg)rq)rg)NTN)rfrr)rr)r)rf)rr) rrFFTrgrgrfrgrfrrf)__name__ __module__ __qualname____doc__HPARAMS_NEEDEDMODULES_NEEDEDrrSr9r8rprrrrrrr'rrrrrr __classcell__rrrrrs\   3 % > / O e .& / 0 r) rr/r* speechbrain.inference.interfacesrspeechbrain.utils.data_utilsrspeechbrain.utils.fetchingrrrrrrs