+ jZ+tRt^RIt^RIHt^RIHt^RIHt]!RR7]!RR]444tR.t R#) zData2VecText configurationN)strict)PreTrainedConfig)auto_docstringz!facebook/data2vec-audio-base-960h) checkpointc aa]tRt^toRtRt^ tRt^ t^ t Rt Rt Rt Rt RtRtRtRtRtR tRtRtRtRtR t^t^t^tR t^ t^tRt^ t ^t!R t"R t#R t$Rt%Rt&Rt'Rt(R t)^t*^t+^t,R t-^t.^t/^t0Rt1V3Rlt2Rt3]4R4t5V3Rlt6Rt7Vt8V;t9#)Data2VecAudioConfiga feat_proj_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for output of the feature encoder. final_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the final projection layer of [`Data2VecAudioForCTC`]. feat_extract_activation (`str, `optional`, defaults to `"gelu"`): The non-linear activation function (function or string) in the 1D convolutional layers of the feature extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. conv_dim (`tuple[int]` or `list[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`): A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers. conv_stride (`tuple[int]` or `list[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`): A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*. conv_bias (`bool`, *optional*, defaults to `False`): Whether the 1D convolutional layers have a bias. num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16): Number of groups of 1D convolutional positional embeddings layer. conv_pos_kernel_size (`int`, *optional*, defaults to `19`): Kernel size of positional conv module. num_conv_pos_embeddings (`int`, *optional*, defaults to 128): Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional embeddings layer. mask_time_prob (`float`, *optional*, defaults to 0.05): Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking procedure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If reasoning from the probability of each feature vector to be chosen as the start of the vector span to be masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the mask_time_length (`int`, *optional*, defaults to 10): Length of vector span along the time axis. mask_time_min_masks (`int`, *optional*, defaults to 2),: The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step, irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length < mask_time_min_masks'' mask_feature_prob (`float`, *optional*, defaults to 0.0): Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The masking procedure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over the axis. If reasoning from the probability of each feature vector to be chosen as the start of the vector span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. mask_feature_length (`int`, *optional*, defaults to 10): Length of vector span along the feature axis. mask_feature_min_masks (`int`, *optional*, defaults to 0): The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time step, irrespectively of `mask_feature_prob`. Only relevant if ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks'' ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`): Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an instance of [`Data2VecAudioForCTC`]. ctc_zero_infinity (`bool`, *optional*, defaults to `False`): Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance of [`Data2VecAudioForCTC`]. use_weighted_layer_sum (`bool`, *optional*, defaults to `False`): Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an instance of [`Data2VecAudioForSequenceClassification`]. classifier_proj_size (`int`, *optional*, defaults to 256): Dimensionality of the projection before token mean-pooling for classification. tdnn_dim (`tuple[int]` or `list[int]`, *optional*, defaults to `(512, 512, 512, 512, 1500)`): A tuple of integers defining the number of output channels of each 1D convolutional layer in the *TDNN* module of the *XVector* model. The length of *tdnn_dim* defines the number of *TDNN* layers. tdnn_kernel (`tuple[int]` or `list[int]`, *optional*, defaults to `(5, 3, 3, 1, 1)`): A tuple of integers defining the kernel size of each 1D convolutional layer in the *TDNN* module of the *XVector* model. The length of *tdnn_kernel* has to match the length of *tdnn_dim*. tdnn_dilation (`tuple[int]` or `list[int]`, *optional*, defaults to `(1, 2, 3, 1, 1)`): A tuple of integers defining the dilation factor of each 1D convolutional layer in *TDNN* module of the *XVector* model. The length of *tdnn_dilation* has to match the length of *tdnn_dim*. xvector_output_dim (`int`, *optional*, defaults to 512): Dimensionality of the *XVector* embedding vectors. add_adapter (`bool`, *optional*, defaults to `False`): Whether a convolutional network should be stacked on top of the Data2VecAudio Encoder. Can be very useful for warm-starting Data2VecAudio for SpeechEncoderDecoder models. adapter_kernel_size (`int`, *optional*, defaults to 3): Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`. adapter_stride (`int`, *optional*, defaults to 2): Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`. num_adapter_layers (`int`, *optional*, defaults to 3): Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is True`. output_hidden_size (`int`, *optional*): Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant if `add_adapter is True`. Example: ```python >>> from transformers import Data2VecAudioConfig, Data2VecAudioModel >>> # Initializing a Data2VecAudio facebook/data2vec-audio-base-960h style configuration >>> configuration = Data2VecAudioConfig() >>> # Initializing a model (with random weights) from the facebook/data2vec-audio-base-960h style configuration >>> model = Data2VecAudioModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```zdata2vec-audioii gelug?gg{Gz?gh㈵>Fg?sumNc <VP;'g VPVn\VP4Vn\ SV`!R/VBR#)N)output_hidden_size hidden_sizelenconv_dimnum_feat_extract_layerssuper __post_init__)selfkwargs __class__s&,z/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/data2vec/configuration_data2vec_audio.pyr!Data2VecAudioConfig.__post_init__s@"&"9"9"M"MT=M=M'*4=='9$ ''c  z\VP4VP8wgI\VP4VP8wg%\VP4VP8wdO\ R\VP4 R\VP4 R\VP4 R24hR#)zOPart of `@strict`-powered validation. Validates the architecture of the config.zConfiguration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) = z`, `len(config.conv_stride) = z`, `len(config.conv_kernel) = z`.N)r conv_strider conv_kernelr ValueErrorrs&rvalidate_architecture)Data2VecAudioConfig.validate_architectures!! "d&B&B BD$$%)E)EEDMM"d&B&BB &''Ec$JZJZF[E\]//243C3C/D.ERI CrcB\P!VP4#)N)mathprodrrs&rinputs_to_logits_ratio*Data2VecAudioConfig.inputs_to_logits_ratiosyy))**rc:<V^8dQh/S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[S[,;R&S[S[,;R&S[S[,;R &S[S[,;R &S[S[,;R &S[S[,;R &S[;R &S[;R&S[;R&S[S[,S[S[R3,,;R&S[S[,S[S[R3,,;R&S[S[,S[S[R3,,;R&S[;R&S[;R&S[;R&S[;R&S[S[,;R&S[;R&S[;R&S[S[,;R&S[;R&S[;R&S[;R&S[;R&S[;R &S[;R!&S[S[,S[S[R3,,;R"&S[S[,S[S[R3,,;R#&S[S[,S[S[R3,,;R$&S[;R%&S[R&,;R'&S[R&,;R(&S[S[S[,,R&,;R)&S[;R*&S[;R+&S[;R,&S[;R-&S[R&,;R.&#)/ vocab_sizernum_hidden_layersnum_attention_headsintermediate_size hidden_acthidden_dropoutactivation_dropoutattention_dropoutfeat_proj_dropout final_dropout layerdropinitializer_rangelayer_norm_epsfeat_extract_activation.rrr conv_biasnum_conv_pos_embedding_groupsconv_pos_kernel_sizenum_conv_pos_embeddingsmask_time_probmask_time_lengthmask_time_min_masksmask_feature_probmask_feature_lengthmask_feature_min_masksctc_loss_reductionctc_zero_infinityuse_weighted_layer_sumclassifier_proj_sizetdnn_dim tdnn_kernel tdnn_dilationxvector_output_dimN pad_token_id bos_token_id eos_token_id add_adapteradapter_kernel_sizeadapter_stridenum_adapter_layersr)intstrfloatlisttuplebool)format __classdict__s"r __annotate__ Data2VecAudioConfig.__annotate__s8PQRSTUV!WX!YZ[\CK%]^ )_`s{(abs{(cd3;$efs{ gh#ij kl!)mn3i%S/)OopcU38_,DqrcU38_,Estuv$'+wx"yz!${|CK&}~@ ABs{(CD!EF #GH#IJ#KL!(MN#OP3i%S/)FQRcU38_,>ST9uS#X.@UV!WX* YZ* [\S /D(,]^_` abcdefd )gr)rr)r r r r r r r )r(r(r(r(r(r() r\r\r\r(r()r r r r i)rZr\r\r])r]r(r\r]r]):__name__ __module__ __qualname____firstlineno____doc__ model_typer)rr*r+r,r-r.r/r0r1r2r3r4r5r6rrrr7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrKrLrMrNrOrrr propertyr%__annotate_func____static_attributes____classdictcell__ __classcell__)rrWs@@rrrs-a F"JJK!!J"%N&)%(%(!$M I# N#),OH/DK/EKI)+! 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