+ j7|Rt^RIt^RIt^RIHt^RIHt^RIHt]!RR7]!RR]444t R.t R#) zWav2Vec2 model configurationN)strict)PreTrainedConfig)auto_docstringzfacebook/wav2vec2-base-960h) checkpointc8aa]tRt^toRtRt^ tRt^ t^ t Rt Rt Rt Rt RtRtRtRtRtRtR tR tRtRtRtRtR t^t^tR tR tRt^ t ^t!Rt"^ t#^t$Rt%^t&Rt'^dt(Rt)Rt*Rt+Rt,R t-R t.Rt/Rt0Rt1Rt2R t3^t4^t5^t6R t7^t8^t9^t:Rt;Rt]?R4t@V3RltARtBVtCV;tD#)Wav2Vec2Configa$ feat_proj_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for output of the feature encoder. feat_quantizer_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for the output of the feature encoder that's used by the quantizer. final_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the final projection layer of [`Wav2Vec2ForCTC`]. feat_extract_norm (`str`, *optional*, defaults to `"group"`): The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D convolutional layers. 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_kernel (`tuple[int]` or `list[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`): A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The length of *conv_kernel* 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_embeddings (`int`, *optional*, defaults to 128): Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional embeddings layer. num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16): Number of groups of 1D convolutional positional embeddings layer. do_stable_layer_norm (`bool`, *optional*, defaults to `False`): Whether to apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is True` corresponds to applying layer norm before the attention layer, whereas `do_stable_layer_norm is False` corresponds to applying layer norm after the attention layer. apply_spec_augment (`bool`, *optional*, defaults to `True`): Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://huggingface.co/papers/1904.08779). 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 actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. 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'' num_codevectors_per_group (`int`, *optional*, defaults to 320): Number of entries in each quantization codebook (group). num_codevectors_per_group (`int`, *optional*, defaults to 320): Number of entries in each quantization codebook (group). num_codevector_groups (`int`, *optional*, defaults to 2): Number of codevector groups for product codevector quantization. contrastive_logits_temperature (`float`, *optional*, defaults to 0.1): The temperature *kappa* in the contrastive loss. num_negatives (`int`, *optional*, defaults to 100): Number of negative samples for the contrastive loss. codevector_dim (`int`, *optional*, defaults to 256): Dimensionality of the quantized feature vectors. proj_codevector_dim (`int`, *optional*, defaults to 256): Dimensionality of the final projection of both the quantized and the transformer features. diversity_loss_weight (`int`, *optional*, defaults to 0.1): The weight of the codebook diversity loss component. 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 [`Wav2Vec2ForCTC`]. 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 [`Wav2Vec2ForCTC`]. 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 [`Wav2Vec2ForSequenceClassification`]. 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 Wav2Vec2 Encoder. Can be very useful for warm-starting Wav2Vec2 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`. adapter_attn_dim (`int`, *optional*): Dimension of the attention adapter weights to be used in each attention block. An example of a model using attention adapters is [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all). Example: ```python >>> from transformers import Wav2Vec2Config, Wav2Vec2Model >>> # Initializing a Wav2Vec2 facebook/wav2vec2-base-960h style configuration >>> configuration = Wav2Vec2Config() >>> # Initializing a model (with random weights) from the facebook/wav2vec2-base-960h style configuration >>> model = Wav2Vec2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```wav2vec2ii gelug?gg{Gz?gh㈵>groupFTg?i@sumNc <\VP4VnVP;'g VPVn\ SV`!R/VBR#)N)lenconv_dimnum_feat_extract_layersoutput_hidden_size hidden_sizesuper __post_init__)selfkwargs __class__s&,t/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/wav2vec2/configuration_wav2vec2.pyrWav2Vec2Config.__post_init__s@'*4=='9$"&"9"9"M"MT=M=M ''c  z\VP4VP8wgI\VP4VP8wg%\VP4VP8wdO\ R\VP4 R\VP4 R\VP4 R24hR#)zOPart of `@strict`-powered validation. Validates the architecture of the config.zConfiguration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) = z`, `len(config.conv_stride) = z`, `len(config.conv_kernel) = z`.N)r conv_strider conv_kernelr ValueErrorrs&rvalidate_architecture$Wav2Vec2Config.validate_architectures!! "d&B&B BD$$%)E)EEDMM"d&B&BB &''Ec$JZJZF[E\]//243C3C/D.ERI Crcb\P!\PVP^4#)) functoolsreduceoperatormulrr!s&rinputs_to_logits_ratio%Wav2Vec2Config.inputs_to_logits_ratios! d.>.>BBrc<V^8dQh/S[R,;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[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&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[;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[;R0&S[R,;R1&S[R,;R2&S[S[S[,,R,;R3&S[;R4&S[;R5&S[;R6&S[;R7&S[R,;R8&S[R,;R9&#):N vocab_sizernum_hidden_layersnum_attention_headsintermediate_size hidden_acthidden_dropoutactivation_dropoutattention_dropoutfeat_proj_dropoutfeat_quantizer_dropout final_dropout layerdropinitializer_rangelayer_norm_epsfeat_extract_normfeat_extract_activation.rrr conv_biasnum_conv_pos_embeddingsnum_conv_pos_embedding_groupsdo_stable_layer_normapply_spec_augmentmask_time_probmask_time_lengthmask_time_min_masksmask_feature_probmask_feature_lengthmask_feature_min_masksnum_codevectors_per_groupnum_codevector_groupscontrastive_logits_temperature num_negativescodevector_dimproj_codevector_dimdiversity_loss_weightctc_loss_reductionctc_zero_infinityuse_weighted_layer_sumclassifier_proj_sizetdnn_dim tdnn_kernel tdnn_dilationxvector_output_dim pad_token_id bos_token_id eos_token_id add_adapteradapter_kernel_sizeadapter_stridenum_adapter_layersradapter_attn_dim)intstrfloatlisttuplebool)format __classdict__s"r __annotate__Wav2Vec2Config.__annotate__sXd YZ[\]^!_`!abcdCK%ef )ghs{(ijs{(kl"CK-mn3;$ops{ qr#st uv$wx!)yz3i%S/)O{|cU38_,D}~cU38_,E@AB!&CD$'+EF&GH#IJCK&KLMN OPs{(QR!ST #UV #(WX"YZ%*/[\]^_`"ab!&cd#ef#gh!(ij#kl3i%S/)FmncU38_,>op9uS#X.@qr!st* uv* wxS /D(,yz{| }~@ABd )CDDj'Er)rr)r r r r r r r )r-r-r-r-r-r-) rlrlrlr-r-)r r r r i)rjrlrlr%r%)r%r-rlr%r%)E__name__ __module__ __qualname____firstlineno____doc__ model_typer.rr/r0r1r2r3r4r5r6r7r8r9r:r;r<r=rrrr>r?r@rArBrCrDrErFrGrHrIrJrKrLrMrNrOrPrQrRrSrTrUrVrWrXrYrZr[r\r]r^rr_rr"propertyr*__annotate_func____static_attributes____classdictcell__ __classcell__)rrgs@@rrrsoE NJJK!!J"%N&)%(%(*-!$M I# N$#),OH/DK/EKI#&)+!!&#"&N %(!"#%(!",/"MN"#&###( #,FH/>K1@M! 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