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Used to implement [SpecAugment: A Simple Data Augmentation Method for ASR](https://huggingface.co/papers/1904.08779). Note that this method is not optimized to run on TPU and should be run on CPU as part of the preprocessing during training. Args: shape: The shape for which to compute masks. This should be of a tuple of size 2 where the first element is the batch size and the second element is the length of the axis to span. mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of independently generated mask spans of length `mask_length` is computed by `mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the actual percentage will be smaller. mask_length: size of the mask min_masks: minimum number of masked spans attention_mask: A (right-padded) attention mask which independently shortens the feature axis of each batch dimension. rz&`mask_length` has to be bigger than 0.zO`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: z and `sequence_length`: `ct|zz z}t|}|zkDrz}|dz z |krt|dz z d}|S)z;Given input length, compute how many spans should be maskedrr)rmax)r(num_masked_spanepsilonrDrCrEsequence_lengths r7compute_num_masked_spanz6_compute_mask_indices..compute_num_masked_spanAsoi,6DwNOoy9 [ (? :-mask_time_indicesrct|jdds|S|j\}}}|)|jj |j ||<n|jj dkDr|jrt||f|jj |jj||jj}tj||jtj}|jj |j ||<|jjdkDr|jrt||f|jj|jj |jj"}tj||jtj}|dddfj%d|d}d||<|S) z Masks extracted features along time axis and/or along feature axis according to [SpecAugment](https://huggingface.co/papers/1904.08779). apply_spec_augmentTNr)rCrDrrE)rr/)rCrDrEr;)getattrr4rrmrr/rjrurcmask_time_lengthmask_time_min_masksrrrrrkmask_feature_lengthmask_feature_min_masksexpand)r3r>rurr7rLrTmask_feature_indicess r7_mask_hidden_statesz&Data2VecAudioModel._mask_hidden_statesst{{$8$? 4A3E3E3G0 O[  (/3/E/E/H/HI\I\/]M+ , [[ ' '! + 5_-++44 KK88-++99 ! !& ->}G[G[chcmcm n /3/E/E/H/HI\I\/]M+ , ;; ( (1 ,#8[)++77 KK;;++<< $ $)<<0D]MaMainisis#t #74#@#G#GO]_#` 23M. /r8rvrrrrc H||n|jj}||n|jj}||n|jj}|j |}|j dd}|!|j |jd|d}|j|\} }|j| ||} |j| ||||} | d} |j|j| } |s | |f| ddzSt| || j| j S) a/ mask_time_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict masked extracted features in *config.proj_codevector_dim* space. NrrJFr.)rurrrrrr)rextract_featuresr>r)r4rrrrhr<r8rrirrnroData2VecAudioBaseModelOutputr>r) r3rvrrurrrrrr>encoder_outputss r7r?zData2VecAudioModel.forwardsb 2C1N-TXT_T_TqTq$8$D $++JjJj &1%uEN+/*A*ABR*S' '00 ->~1 ,, )/!5# ' (* << # LL7M!#34qr7JJ J++-)77&11   r8rNNNNN)rBrCrDrr%rtr FloatTensorrBrrrrrrr?rErFs@r7reres2"47;26 ,((,!,,t3,((4/ ,\/36:)-,0#'8 llT)8  t+8 !,,t3 8  $; 8 #Tk 8 D[8  - -8 8 r8rerJzu Data2VecAudio Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC). ) custom_introceZdZfdZdZe d dejdzdejdzdedzdedzdedzd ejdzd e e zfd Z xZ S) Data2VecAudioForCTCct||t||_t j |j |_|jtd|jdt|dr|jr |jn |j}t j||j|_|j#y)aZ config ([`Data2VecAudioForCTC`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. NzYou are trying to instantiate z with a configuration that does not define the vocabulary size of the language model head. Please instantiate the model as follows: `Data2VecAudioForCTC.from_pretrained(..., vocab_size=vocab_size)`. or define `vocab_size` of your model's configuration.r!)r$r%rerrr final_dropoutr vocab_sizerr6hasattrr!rrTrlm_headrp)r3r4rr6s r7r%zData2VecAudioForCTC.__init__"s  08zz&"6"67    $00@AHH *1)GFL^L^F % %djdvdv yy!3V5F5FG  r8cL|jjjyrrrrhrsrss r7rtz*Data2VecAudioForCTC.freeze_feature_encoder= --@@Br8Nrvrrrrlabelsrc ||n|jj}|I|j|jjk\r"t d|jj|j |||||}|d} |j | } |j| } d} |b||n$tj|tj}|j|jdjtj} |dk\} | jd}|j| }tj j#| dtj$j'dd}tj(j*j-d 5tj j/||| ||jj0|jj2|jj4 } ddd|s| f|t6dz}| | f|zS|St9| | |j:|j< S#1swYExYw) a labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*): Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size - 1]`. Nz$Label values must be <= vocab_size: rrrNr;)rr/rF)enabled)blank reduction zero_infinitylosslogitsr>r)r4rrIrrrrrr ones_liker1r+rRr masked_selectrr log_softmaxfloat32r<backendscudnnflagsctc_loss pad_token_idctc_loss_reductionctc_zero_infinity_HIDDEN_STATES_START_POSITIONrr>r)r3rvrrrrrrrr>rrr  labels_masktarget_lengthsflattened_targets log_probsoutputs r7r?zData2VecAudioForCTC.forwardDs)$&1% 1` a classification loss is computed (Cross-Entropy). NTrrrr;rrJrr)r4rrrrrstackrrrrrrRrmeanr8rrrrrrrr>r)r3rvrrrrrrrr> norm_weights pooled_output padding_maskexpand_padding_maskrrloss_fctrs r7r?z.Data2VecAudioForSequenceClassification.forwards!0&1%->r1a-HHMMRSMTM#AJM}5  !)..1.5MBB=CVCVWXCY[ijL"."8"8"<"C"CAq-J]J]^_J`"a 25M.. /)--!-4|7G7GA7G7N7S7STVXY7ZZM/  ')HFKKDKK,B,BCV[[QS_UDY)F)G!HHF)-)9TGf$ Ev E'!//))   r8r)rBrCrDr%rtrrrrrrrr?rErFs@r7rrs"C(/3)-,0#'&*C llT)C  t+C  $; C #Tk C D[ C  t#C  ) )C C r8rceZdZfdZdZdZe d dejdzdejdzdejdzde dzd e dzd e dzd e e zfd Z xZ S)(Data2VecAudioForAudioFrameClassificationct||t|dr|jr t dt ||_|jdz}|jr0tjtj||z |_ tj|j|j |_|j |_|j%y)Nr!zgAudio frame classification does not support the use of Data2VecAudio adapters (config.add_adapter=True)r)r$r%rr!rrerrrrrlrrWrrrTrrrprs r7r%z1Data2VecAudioForAudioFrameClassification.__init__s  6= )f.@.@y 18--1  ( (!#ejj.Dz.Q!RD ))F$6$68I8IJ ++ r8cL|jjjyrrrrss r7rtz?Data2VecAudioForAudioFrameClassification.freeze_feature_encoder rr8cP|jjD] }d|_ yrrrqs r7rz:Data2VecAudioForAudioFrameClassification.freeze_base_modelrr8Nrvrrrrrrc ||n|jj}|jjrdn|}|j|||||}|jjrr|t} t j | d} tjj|jd} | | jdddzjd} n|d} |j| } d} |\t} | | jd|jt j |jd|jd} |s| f|tdz}|St#| | |j$|j& S) rNTrrrr;r)axisr)r4rrrrrrrrrrrrRrrrargmaxrr>r)r3rvrrrrrrrr>rrrrrs r7r?z0Data2VecAudioForAudioFrameClassification.forwardsj0&1%->r1a-HHMMRSMTM#AJM/  ')HFKKDOOtuDY)F)G!HHFM$!//))   r8r)rBrCrDr%rtrrrrrrrr?rErFs@r7rrs C(/3&*)-,0#': llT):  t+:  t# :  $; : #Tk : D[:  & &: : r8rc&eZdZdfd ZdZxZS) AMSoftmaxLossct|||_||_||_t j tj||d|_ t j|_ y)NT)rp) r$r%scalemarginrrrlrrandnrrr)r3 input_dimrrrr6s r7r%zAMSoftmaxLoss.__init__[sQ   $ll5;;y*#EUYZ '') r8c|j}tjj|jd}tjj|d}t j ||}||jz }tjj||j}|jt j|j||z}|j||}|S)Nrrr)flattenrr normalizerrmmrone_hotrrwhererr) r3r>rr cos_thetapsionehotrrs r7r?zAMSoftmaxLoss.forwardcs!((!(< // 1/E HH]F3 $++%&&vt?ekk&++-iHHyy( r8)g>@g?rArFs@r7rrZs * r8rcXeZdZdfd Zdej dej fdZxZS) TDNNLayerct||dkDr|j|dz n|j||_|j||_|j ||_|j||_tj|j|j z|j|_ tj|_ y)Nrr)r$r%tdnn_dimr'r( tdnn_kernelr tdnn_dilationdilationrrkernelReLUr1r2s r7r%zTDNNLayer.__init__rs rc&trddlm}tr+t|jrt j d|jdd}|jjj|j|j|jjdd}tjj|||jj |j"}|jdd}|j%|}|S)Nr) LoraLayerzDetected LoRA on TDNNLayer. LoRA weights won't be applied due to optimization. You should exclude TDNNLayer from LoRA's target modules.rrJ)r)rpeft.tuners.lorarrrwarningswarnr<rrr(rr'rrconv1dr!rr1)r3r>rrs r7r?zTDNNLayer.forward|s   2  $++y1 O &//15 ##(():):D+BFD]D]!^))F$=$=v?X?XY&v'@'@&BSBST Rs>FcL|jjjyrrrrss r7rtz.Data2VecAudioForXVector.freeze_feature_encoderrr8cP|jjD] }d|_ yrrrqs r7rz)Data2VecAudioForXVector.freeze_base_modelrr8r cVd}|jjD] }|||d}|S)z? 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