cCi SrSSKrSSKJrJr SSKrSSKJr SSKJr SSK J r J r J r SSK Jr SS KJr SS KJr SS KJr SS KJrJr SS KJr SSKJrJrJr SSKJr SSK J!r! \RD"\#5r$Sr%Sr&Sr'/SQr(Sr)Sr*"SS\RV5r,"SS\RV5r-"SS\RV5r."SS\RV5r/"SS \RV5r0"S!S"\RV5r1"S#S$\RV5r2"S%S&\RV5r3"S'S(\5r4"S)S*\5r5S+r6S,r7"S-S.\55r8\ "S/\65"S0S1\555r9\ "S2\65"S3S4\555r:/S5Qr;g)6zPyTorch M-CTC-T model.N)OptionalUnion)nn)ACT2FN)add_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forward)is_deepspeed_zero3_enabled)is_fsdp_managed_module)_prepare_4d_attention_mask)GradientCheckpointingLayer)BaseModelOutputCausalLMOutput)PreTrainedModel)apply_chunking_to_forward find_pruneable_heads_and_indicesprune_linear_layer)logging) MCTCTConfigrzspeechbrain/m-ctc-t-large)rizY"Mr. Quilter is the apostle of the middle classes, and we're glad to welcome his gospel."gv@c2^\rSrSrSrU4SjrSrSrU=r$)MCTCTConv1dSubsampler3z Convolutional subsampler: a stack of 1D convolution (along temporal dimension) followed by non-linear activation via gated linear units (https://huggingface.co/papers/1911.08460) cZ>^[TT]5 UTlURTl[ R "UR5TlURTl URUR-Tl TRS:a*URc [S5eURTlOSTlUR"S-TlUR&TlUR*Tl[ R."U4Sj[1TR(555Tlg)NrzbNeed to specify `conv_channels` configuration in `MCTCTConfig` to use multiple convolution layers.c 3># UHwup[R"US:Xa TROTRUUTRS- :aTRUO TR UTR USS9v My g7f)rrvalid) kernel_sizestridepaddingN)rConv1d in_channels mid_channels num_layers out_channelsr!).0ikselfs m/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/deprecated/mctct/modeling_mctct.py 1MCTCTConv1dSubsampler.__init__..Usz ) 4 II$%F  0A0A!0D()DOOa,?(?!!!$TEVEV{{1~  4sA?B)super__init__config conv_glu_dimglu_dimrDropout conv_dropoutdropoutnum_conv_layersr&input_feat_per_channelinput_channelsr$ conv_channels ValueErrorr% hidden_sizer' conv_kernelr conv_strider! ModuleList enumerate conv_layersr+r1 __class__s` r,r0MCTCTConv1dSubsampler.__init__9s  ** zz&"5"56  00!886;P;PP ??Q ##+  !' 4 4D  $D "..2!--(( == ) "$"2"23 )  c[SUR55n[RRR USSX"4SS5nUR SS5R5nURHCnU"U5n[RRX0RS9nURU5nME UR SS5R5nU$)Nc3*# UH oS-v M g7f)rN)r(sizes r,r-0MCTCTConv1dSubsampler.forward..cs=,[TU]5 [R"URUR UR S9Ul[R"URUR 5Ul [R"URUR 5Ul [5Ul [R"UR5UlUR#S[$R&"UR5R)S5SS9 UR#S[$R*"UR,R/5[$R0UR,R2S9SS9 g)N) padding_idx position_ids)rF) persistenttoken_type_idsdtypedevice)r/r0r Embedding vocab_sizer< pad_token_idword_embeddingsmax_position_embeddingsposition_embeddingstype_vocab_sizetoken_type_embeddingsMCTCTLayerNorm LayerNormr4hidden_dropout_probr6register_bufferrOarangeexpandzerosrhrIlongrnrBs r,r0MCTCTEmbeddings.__init__ss !||F,=,=v?Q?Q_e_r_rs#%<<0N0NPVPbPb#c %'\\&2H2H&J\J\%]" ()zz&"<"<=   ELL)G)GHOOPWXej     KK))..0 4K\K\KcKc d  rEcUbUR5OUR5SSnUSnUcURSS2XWU-24nUcv[US5(a-URSS2SU24nUR USU5n U nO8[ R "U[ RURRS9nUcURU5nURU5n XJ-n URU 5n URU 5n U $)Nrirrkrrl) rIrhhasattrrkr|rOr}r~rnrrrvrxr6) r+rUrkrh inputs_embedspast_key_values_length input_shape seq_lengthbuffered_token_type_ids buffered_token_type_ids_expandedrv embeddingss r,rXMCTCTEmbeddings.forwards0>/In))+}OaOaOcdgegOh  ^  ,,Q0FVlIl0l-lmL  !t-..*.*=*=a*n*M'3J3Q3QR]^_R`bl3m0!A!&[ SWSdSdSkSk!l   00@M $ : :> J": ^^J/ \\*- rE)rxr6rtrvrr)NNNNrrZrbs@r,rdrdpsQ .wxrErdcJ^\rSrSrU4SjrSrSrSrSSjrSr U=r $) MCTCTSelfAttentionc^>[TU]5 URUR-S:wa7[ US5(d&[ SURSURS35eURUlUR UlURUR-Ul[R"URURSS9Ul [R"URURSS9Ul [R"URURSS9Ul [R"UR5UlUR"Ul[R$"SUR"-S - UR5UlUR(Ulg) Nrembedding_sizezThe hidden size (z6) is not a multiple of the number of attention heads ()Fbiasrr)r/r0r<num_attention_headsrr;attention_head_dimattention_head_size all_head_sizerLinearquerykeyvaluer4attention_probs_dropout_probr6rsrodistance_embedding is_decoderrBs r,r0MCTCTSelfAttention.__init__s[     : : :a ?PVXhHiHi#F$6$6#78 445Q8  $*#=#= #)#<#< !558P8PPYYv1143E3EER 99V//1C1C%PYYv1143E3EER zz&"E"EF '-'E'E$"$,,q63Q3Q/QTU/UW[WoWo"p ++rEcUR5SSURUR4-nUR"U6nUR SSSS5$)Nrirrr)rIrrviewpermute)r+x new_x_shapes r,transpose_for_scores'MCTCTSelfAttention.transpose_for_scoressLffhsmt'?'?AYAY&ZZ FFK yyAq!$$rEc [UR5S:a4UR"[[ [UR5556nUR "[U56R"[[ [U5556$)Nr)lenshaperreversedrangereshape)r+rrs r,reshape_fortran"MCTCTSelfAttention.reshape_fortrans^ qww bclerrrLrr) start_dim)rmathsqrtrrrrrOmatmulrRrweighteinsumrrrPsoftmaxr6rflatten)r+rVattention_mask head_maskoutput_attentionsmixed_query_layer key_layer value_layer query_layerattention_scorespositional_embeddingrelative_position_scoresattention_probs context_layeroutputss r,rXMCTCTSelfAttention.forwards|!JJ}5- $:R:R0SS--dhh}.EF // =0IJ //0AB !<< 5H5HR5PQ $66==#(<<0BDXZoZopqstZu#v #'#J#JKc#d +F  %/@ --//0@b/I,,7  -9O _B %--aAq9AABAO 6G=2O\M]rE) rrrr6rrrsrrrNNF) r[r\r]r^r0rrrrXr`rarbs@r,rrs-,.% Q *8 ..rErc.^\rSrSrU4SjrSrSrU=r$)rwic>[TU]5 [R"[R "S55Ul[R"[R"S55UlgNr) r/r0r ParameterrOonessingleton_weightr}singleton_bias)r+rCs r,r0MCTCTLayerNorm.__init__s@  " UZZ] ; ll5;;q>:rEc8XR-UR-$N)rrr+rVs r,rXMCTCTLayerNorm.forwards 5 559L9LLLrE)rrr[r\r]r^r0rXr`rarbs@r,rwrws; MMrErwc.^\rSrSrU4SjrSrSrU=r$)MCTCTSelfOutputi c2>[TU]5 Xl[R"UR UR SS9Ul[R"UR URS9Ul[R"UR5Ul gNFr)eps) r/r0r1rrr<denserxlayer_norm_epsr4ryr6rBs r,r0MCTCTSelfOutput.__init__!sg  YYv1163E3EER f&8&8f>S>STzz&"<"<= rEcpURU5nURU5nURX-5nU$rrr6rxr+rV input_tensors r,rXMCTCTSelfOutput.forward(5 =1  ]3 }'CD rE)rxr1rr6rrbs@r,rr s>rErc>^\rSrSrU4SjrSrSSjrSrU=r$)MCTCTAttentioni/c>[TU]5 [U5Ul[ U5Ul[ 5Ulgr)r/r0rr+routputset pruned_headsrBs r,r0MCTCTAttention.__init__0s0 &v. %f- ErEc6[U5S:Xag[XRRURRUR 5up[ URRU5URl[ URRU5URl[ URRU5URl [ URRUSS9URl URR[U5- URlURRURR-URl UR RU5Ulg)NrrrL)rrr+rrrrrrrrrrunion)r+rindexs r, prune_headsMCTCTAttention.prune_heads6s  u:? 7 9900$))2O2OQUQbQb  -TYY__eD *499==%@ ,TYY__eD .t{{/@/@%QO )- (E(EE (R %"&))"?"?$))B_B_"_  --33E:rEcjURUUUU5nURUSU5nU4USS-nU$)Nrr)r+r)r+rVrrr self_outputsattention_outputrs r,rXMCTCTAttention.forwardHsNyy       ;;|A F#% QR(88rE)rrr+r) r[r\r]r^r0rrXr`rarbs@r,rr/s"" ;* rErc.^\rSrSrU4SjrSrSrU=r$)MCTCTIntermediatei[c>[TU]5 [R"URUR SS9Ul[UR[5(a[URUl gURUl g)NFr) r/r0rrr<intermediate_sizer isinstance hidden_actstrrintermediate_act_fnrBs r,r0MCTCTIntermediate.__init__\sc YYv1163K3KRWX f'' - -'-f.?.?'@D $'-'8'8D $rEcJURU5nURU5nU$rrrrs r,rXMCTCTIntermediate.forwardds& =1 00? rErrrbs@r,rr[s9rErc.^\rSrSrU4SjrSrSrU=r$) MCTCTOutputijc&>[TU]5 [R"URUR SS9Ul[R"UR URS9Ul[R"UR5Ul gr) r/r0rrrr<rrxrr4ryr6rBs r,r0MCTCTOutput.__init__ksc YYv779K9KRWX f&8&8f>S>STzz&"<"<= rEcpURU5nURU5nURX-5nU$rrrs r,rXMCTCTOutput.forwardqrrE)rxrr6rrbs@r,r r js> rEr cF^\rSrSrS\4U4SjjrSSjrSrSrU=r $) MCTCTLayerixr1c>[TU]5 SUlURUl[ U5Ul[ U5UlURUl[U5Ul gr) r/r0 seq_len_dimchunk_size_feed_forwardr intermediater attentionrr rrBs r,r0MCTCTLayer.__init__ysV '-'E'E$-f5'/ ++!&) rEcURXX4S9nUSnUSSn[URURURU5nU4U-nU$)N)rrr)rrfeed_forward_chunkrr) r+rVrrrself_attention_outputsrr layer_outputs r,rXMCTCTLayer.forwardso"& 9"0" 2!4(,0  # #T%A%A4CSCSUe  /G+rEcJURU5nURX!5nU$r)rr)r+rintermediate_outputrs r,rMCTCTLayer.feed_forward_chunks)"//0@A{{#6I rE)rrrrrrr) r[r\r]r^rr0rXrr`rarbs@r,rrxs) *{ * *rErc^\rSrSr%Sr\\S'SrSrSr Sr S\ R4S jr S rS rg ) MCTCTPreTrainedModelizz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. r1mctctrUTcURRn[U[R5(aWUR R RSUS9 URb$URR R5 GOI[U[R5(acUR R RSUS9 URb1UR R URR5 O[U[R5(aJURR R5 UR R RS5 O^[U[5(aIURR RS5 UR R R5 [U[R[R"45(aWUR R RSUS9 URb%URR R5 ggg)zInitialize the weightsg)meanstdNg?)r1initializer_rangerrrrdatanormal_rzero_rorgrxfill_rwrrr#)r+moduler$s r, _init_weights"MCTCTPreTrainedModel._init_weightsskk++ fbii ( ( MM   & &CS & 9{{&   &&(  - - MM   & &CS & 9!!- ""6#5#56<<>  - - KK   " " $ MM   $ $S )  / /  # # ( ( . .s 3  ! ! & & , , . fryy"))4 5 5 MM   & &CS & 9{{&   &&(' 6rE input_lengthsc"Sn[[URR5URRURR 5H6up4nUS-nUSU--X$S- -- S- n[ R"XSS9S-nM8 U$)z8 Computes the output length of the convolutional layers rrtrunc) rounding_mode)ziprr1r7r=r>rOdiv)r+r-dilation_ kernel_szr!r"s r, _get_feat_extract_output_lengths5MCTCTPreTrainedModel._get_feat_extract_output_lengthss$' $++-- . 0G0GI`I`% A& 1nG)AK7(RSm:TTWXXM!IIm7SVWWM % rEc[UR5S:a USS2SS2S4nURURS55nUR 5Sn[ R "XA4URURS9nSU[ R"XBRS9US- 4'URS/5RS5RS/5R5nU$)Nrrirrlrr) rrr6rNrIrOr}rmrnr{flipcumsumr~)r+feature_vector_lengthrsubsampled_lengthsbszs r,"_get_feature_vector_attention_mask7MCTCTPreTrainedModel._get_feature_vector_attention_masks ~## $q (+Aq"H5N"BB>CUCUVXCYZ!!#A&  (0D0D^MbMb  ef S1F1FGI[^_I_`a',,bT299"=BBB4HMMOrErHN)r[r\r]r^r_r__annotations__base_model_prefixmain_input_namesupports_gradient_checkpointingr+rO LongTensorr6r>r`rHrEr,r r s< &O&*#)0 e>N>N rEr aH This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`MCTCTConfig`]): 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. a Args: input_features (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`Wav2Vec2CTCTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. c^\rSrSrS\4U4SjjrS S\RS\RS\RS\S\S \S \ \ \ 44S jjr S r U=r$) MCTCTEncoderir1c,>[TU]U5 URUl[5Ul[ U5Ul[R"[UR5Vs/sHn[U5PM sn5Ul SUl gs snf)NF)r/r0ryrw layer_normrrWrr?rnum_hidden_layersrlayersgradient_checkpointing)r+r1r4rCs r,r0MCTCTEncoder.__init__ss  #)#=#= (*)&1 mmvG_G_A`$aA`AZ%7A`$ab &+#%bs(BrUrrroutput_hidden_states return_dictreturncUbUOURRnUbUOURRnUbUOURRnUR U5nUR U5nUbUR URSU5n[RRXpRURS9nUb[X'R5nU(aSOSn U(aSOSn Ub`UR5S[!UR"5:wa6[%S[!UR"5SUR5SS35e['5=(d [)U5n [+UR"5HupU(aX4-n [,R."/5nUR=(a XRR0:nU(aU (a U "UUUS9nUSnU(aS nU(dMU WS4-n M U(aX4-n U(d[3S XU 455$[5XU S 9$) Nr)ptrainingrHrz&The head_mask should be specified for z layers, but it is for .)rVrr)NNc3.# UH ocMUv M g7frrH)r(vs r,r-'MCTCTEncoder.forward..`se$Sq$Ss last_hidden_staterV attentions)r1rrMuse_return_dictrHrWr>rrrPr6ryrRr rmrIrrJr;r r r@rOrand layerdroptupler)r+rUrrrrMrNrrVencoder_statesall_attentions synced_gpusidx encoder_layerdropout_probabilityskip_the_layer layer_outputss r,rXMCTCTEncoder.forwards,2C1N-TXT_T_TqTq$8$D $++JjJj &1%>Ne]N$See e+Vd  rE)rWrKryrHrJ)FFT)r[r\r]r^rr0rOTensorboolrr]rrXr`rarbs@r,rFrFs,{,#(%* I  I  I << I  I # I I  uo% &I I rErFzaThe bare M-CTC-T Model transformer outputting raw hidden-states without any specific head on top.c^\rSrSrU4Sjr\"\RS55\"\ \ \ S\ S9SS\ RS\\ RS\\ RS \\S \\S \\S \\\ 44S jj55rSrU=r$) MCTCTModelifcp>[TU]U5 Xl[U5UlUR 5 gr)r/r0r1rFencoder post_initrBs r,r0MCTCTModel.__init__ks-   #F+  rEzbatch_size, sequence_lengthaudio) checkpoint output_type config_classmodalityexpected_outputrUrrrrMrNrOc PUbUOURRnUbUOURRnUbUOURRnUc [ S5eUR UUUUUUS9nUSnU(d U4USS-$[ UURURS9$)Nz#You have to specify input_features.rrrrMrNrrrW) r1rrMrZr;rlrrVrY) r+rUrrrrMrNencoder_outputssequence_outputs r,rXMCTCTModel.forwardts"2C1N-TXT_T_TqTq$8$D $++JjJj &1% # 'tn # d^#  uo% &# i# rErjzcMCTCT Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).c^\rSrSrU4Sjr\"\5\"\\ \ \ \ S9SS\ RS\\ RS\\ RS\\S\\S \\S \\ R"S \\\ 44S jj55rS rU=r$) MCTCTForCTCic>[TU]U5 [U5UlURc[ SUR S35eURn[R"X!R5Ul UR5 g)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: `MCTCTForCTC.from_pretrained(..., vocab_size=vocab_size)`. or define `vocab_size` of your model's configuration.) r/r0rjr!rpr;rCr<rrctc_headrm)r+r1output_hidden_sizerCs r,r0MCTCTForCTC.__init__s  '    $00@AHH  $// "46G6GH  rE)rprqrrrt expected_lossrUrrrrMrNlabelsrOc UbJUR5URR:a"[SURR35eUbUOURRnUR UUUUUUS9nUSn UR U 5n Sn UGbeUbUO/[R"URSS[RS9nURURS55R[R5n US:n U RS5nURU 5n[R R#U S[R$S9R'SS5n[R(R*R-S S 9 [R R/UUU UURR0URR2URR4S 9n SSS5 U(dU 4U[6S-nU bU 4U-$U$[9XUR:UR<S 9$!,(df  NL=f) a labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*): Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size - 1]`. Nz$Label values must be <= vocab_size: rvrri)rm)rMrmrF)enabled)blank reduction zero_infinity)losslogitsrVrY)maxr1rpr;rZr!rrOrrr~r6rNto masked_selectrrP log_softmaxfloat32rRbackendscudnnflagsctc_lossrqctc_loss_reductionctc_zero_infinity_HIDDEN_STATES_START_POSITIONrrVrY)r+rUrrrrMrNrrrVrrr- labels_masktarget_lengthsflattened_targets log_probsrs r,rXMCTCTForCTC.forwards 2  &**,$++2H2H"HCDKKDZDZC[\] ]%0% E 'tn E d^E ))*E  un$ %E CE rEr)rrjr )rs " "rrA8C:@.mm,   H % !2't:BII:z7bii7tiiXMRYYM bii )RYY)X   ")) $+$NB?BJ @T 'T ng5 %5 5 pma &a a H @rE