+ jp;Rt^RIt^RIt^RIHt^RIHt^RIHt]!RR7]!RR]444t ]!RR7]!R R ]444t RR .t R#) zSpeechT5 model configurationN)strict)PreTrainedConfig)auto_docstringzmicrosoft/speecht5_asr) checkpointcFaa]tRt^toRtRtRRRR/t^QtRt^ t ^ t Rt R t ^t Rt^ tR tR tR tR tR tR tR tR tR tRtRtR tRtRtR tR t^t ^t!Rt"Rt#^ t$^t%Rt&^ t'^t(^t)^t*^t+^t,^Pt-^t.Rt/Rt0Rt1^t2Rt3^t4Rt5^t6Rt7Rt8^t9Rt:^t;RtRt?Rt@V3RltARtBRtCV3RltDRtEVtFV;tG#)!SpeechT5Configa positional_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the text position encoding layers. feat_extract_norm (`str`, *optional*, defaults to `"group"`): The norm to be applied to 1D convolutional layers in the speech encoder pre-net. One of `"group"` for group normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D convolutional layers. feat_proj_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for output of the speech encoder pre-net. 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 speech encoder pre-net. 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 speech encoder pre-net. 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 speech encoder pre-net. 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. apply_spec_augment (`bool`, *optional*, defaults to `True`): Whether to apply *SpecAugment* data augmentation to the outputs of the speech encoder pre-net. 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_mel_bins (`int`, *optional*, defaults to 80): Number of mel features used per input features. Used by the speech decoder pre-net. Should correspond to the value used in the [`SpeechT5Processor`] class. speech_decoder_prenet_layers (`int`, *optional*, defaults to 2): Number of layers in the speech decoder pre-net. speech_decoder_prenet_units (`int`, *optional*, defaults to 256): Dimensionality of the layers in the speech decoder pre-net. speech_decoder_prenet_dropout (`float`, *optional*, defaults to 0.5): The dropout probability for the speech decoder pre-net layers. speaker_embedding_dim (`int`, *optional*, defaults to 512): Dimensionality of the *XVector* embedding vectors. speech_decoder_postnet_layers (`int`, *optional*, defaults to 5): Number of layers in the speech decoder post-net. speech_decoder_postnet_units (`int`, *optional*, defaults to 256): Dimensionality of the layers in the speech decoder post-net. speech_decoder_postnet_kernel (`int`, *optional*, defaults to 5): Number of convolutional filter channels in the speech decoder post-net. speech_decoder_postnet_dropout (`float`, *optional*, defaults to 0.5): The dropout probability for the speech decoder post-net layers. reduction_factor (`int`, *optional*, defaults to 2): Spectrogram length reduction factor for the speech decoder inputs. max_speech_positions (`int`, *optional*, defaults to 4000): The maximum sequence length of speech features that this model might ever be used with. max_text_positions (`int`, *optional*, defaults to 450): The maximum sequence length of text features that this model might ever be used with. encoder_max_relative_position (`int`, *optional*, defaults to 160): Maximum distance for relative position embedding in the encoder. use_guided_attention_loss (`bool`, *optional*, defaults to `True`): Whether to apply guided attention loss while training the TTS model. guided_attention_loss_num_heads (`int`, *optional*, defaults to 2): Number of attention heads the guided attention loss will be applied to. Use -1 to apply this loss to all attention heads. guided_attention_loss_sigma (`float`, *optional*, defaults to 0.4): Standard deviation for guided attention loss. guided_attention_loss_scale (`float`, *optional*, defaults to 10.0): Scaling coefficient for guided attention loss (also known as lambda). Example: ```python >>> from transformers import SpeechT5Model, SpeechT5Config >>> # Initializing a "microsoft/speecht5_asr" style configuration >>> configuration = SpeechT5Config() >>> # Initializing a model (with random weights) from the "microsoft/speecht5_asr" style configuration >>> model = SpeechT5Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```speecht5num_attention_headsencoder_attention_headsnum_hidden_layersencoder_layersii 皙?gelug{Gz?gh㈵>FgroupgTg?g?iig?g$@c \<\VP4Vn\SV`!R/VBR#)N)lenconv_dimnum_feat_extract_layerssuper __post_init__)selfkwargs __class__s&,t/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/speecht5/configuration_speecht5.pyrSpeechT5Config.__post_init__s$'*4=='9$ ''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$SpeechT5Config.validate_architectures!! "d&B&B BD$$%)E)EEDMM"d&B&BB &''Ec$JZJZF[E\]//243C3C/D.ERI Crcb\P!\PVP^4#)) functoolsreduceoperatormulr r#s&rinputs_to_logits_ratio%SpeechT5Config.inputs_to_logits_ratios! d.>.>BBrcv<V^8dQh/S[;R&S[;R&S[;R&S[;R&S[;R&S[S[,;R&S[;R&S[;R&S[;R &S[S[,;R &S[;R &S[S[,;R &S[S[,;R &S[S[,;R&S[S[,;R&S[;R&S[;R&S[;R&S[;R&S[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$,;R%&S[R$,;R&&S[S[S[,,R$,;R'&S[R$,;R(&S[;R)&S[;R*&S[;R+&S[S[,;R,&S[;R-&S[;R.&S[;R/&S[;R0&S[S[,;R1&S[;R2&S[;R3&S[;R4&S[;R5&S[;R6&S[;R7&S[;R8&S[;R9&S[;R:&S[;R;&S[;R<&#)= vocab_size hidden_sizer r encoder_ffn_dimencoder_layerdropdecoder_layersdecoder_ffn_dimdecoder_attention_headsdecoder_layerdrop hidden_actpositional_dropouthidden_dropoutattention_dropoutactivation_dropoutinitializer_rangelayer_norm_epsscale_embeddingfeat_extract_normfeat_proj_dropoutfeat_extract_activation.rr r! conv_biasnum_conv_pos_embeddingsnum_conv_pos_embedding_groupsapply_spec_augmentmask_time_probmask_time_lengthmask_time_min_masksmask_feature_probmask_feature_lengthmask_feature_min_masksN pad_token_id bos_token_id eos_token_iddecoder_start_token_id num_mel_binsspeech_decoder_prenet_layersspeech_decoder_prenet_unitsspeech_decoder_prenet_dropoutspeaker_embedding_dimspeech_decoder_postnet_layersspeech_decoder_postnet_unitsspeech_decoder_postnet_kernelspeech_decoder_postnet_dropoutreduction_factormax_speech_positionsmax_text_positionsencoder_max_relative_positionuse_guided_attention_lossguided_attention_loss_num_headsguided_attention_loss_sigmaguided_attention_loss_scale use_cacheis_encoder_decodertie_word_embeddings)intfloatstrboollisttuple)format __classdict__s"r __annotate__SpeechT5Config.__annotate__sjklmnop!%qrsts{(uvwxyz!%{|s{(}~@ )ABCK%CDs{(EF )GH#IJ KL!MN$OPs{(QR!)ST3i%S/)OUVcU38_,DWXcU38_,EYZ[\!&]^$'+_`#abCK&cdef ghs{(ij!kl #mn* op* qrS /D(,st $J*uvwx#&)yz"%*{|$)3;4}~$@$'*AB#&+CD$'*EF%*CK5GHIJ$KL!MN$',OP $*QR&),ST"',UV"'-WXYZ#[\$]r)r)rrrrrrr)r/r/r/r/r/r/) rqrqrqr/r/)H__name__ __module__ __qualname____firstlineno____doc__ model_type attribute_mapr0r1r r r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrr r!rCrDrErFrGrHrIrJrKrLrMrNrOrPrQrRrSrTrUrVrWrXrYrZr[r\r]r^r_r`rarbrcrdrr$r,__annotate_func____static_attributes____classdictcell__ __classcell__)rrls@@rrrsm ^J*,EGZ\lmMJKN#%O%(NO#%%(J&)"%N%(&)# N!O$%(#),OH/DK/EKI#&)+!#"&N %(!"# L L+,L)*L() '*14!!$)*!(+ )*!25" $!),!&*+,#),)-I# $( CErrc\a]tRt^toRtRt^PtRtRtR t R t R t Rt Rt RtRtV3RltR tVtR #)SpeechT5HifiGanConfiga` model_in_dim (`int`, *optional*, defaults to 80): The number of frequency bins in the input log-mel spectrogram. upsample_initial_channel (`int`, *optional*, defaults to 512): The number of input channels into the upsampling network. upsample_rates (`tuple[int]` or `list[int]`, *optional*, defaults to `[4, 4, 4, 4]`): A tuple of integers defining the stride of each 1D convolutional layer in the upsampling network. The length of *upsample_rates* defines the number of convolutional layers and has to match the length of *upsample_kernel_sizes*. upsample_kernel_sizes (`tuple[int]` or `list[int]`, *optional*, defaults to `[8, 8, 8, 8]`): A tuple of integers defining the kernel size of each 1D convolutional layer in the upsampling network. The length of *upsample_kernel_sizes* defines the number of convolutional layers and has to match the length of *upsample_rates*. resblock_kernel_sizes (`tuple[int]` or `list[int]`, *optional*, defaults to `[3, 7, 11]`): A tuple of integers defining the kernel sizes of the 1D convolutional layers in the multi-receptive field fusion (MRF) module. resblock_dilation_sizes (`tuple[tuple[int]]` or `list[list[int]]`, *optional*, defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`): A nested tuple of integers defining the dilation rates of the dilated 1D convolutional layers in the multi-receptive field fusion (MRF) module. leaky_relu_slope (`float`, *optional*, defaults to 0.1): The angle of the negative slope used by the leaky ReLU activation. normalize_before (`bool`, *optional*, defaults to `True`): Whether or not to normalize the spectrogram before vocoding using the vocoder's learned mean and variance. Example: ```python >>> from transformers import SpeechT5HifiGan, SpeechT5HifiGanConfig >>> # Initializing a "microsoft/speecht5_hifigan" style configuration >>> configuration = SpeechT5HifiGanConfig() >>> # Initializing a model (with random weights) from the "microsoft/speecht5_hifigan" style configuration >>> model = SpeechT5HifiGan(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```speecht5_hifigani>rg{Gz?r Tc><V^8dQh/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[S[,;R&S[;R &S[;R &S[;R &#) r/ model_in_dim sampling_rateupsample_initial_channel.upsample_ratesupsample_kernel_sizesresblock_kernel_sizesresblock_dilation_sizesr=leaky_relu_slopenormalize_before)rerirjrfrh)rkrls"rrm"SpeechT5HifiGanConfig.__annotate__sXYZ[\"']^Ic3h/>_` 9uS#X6Eab 9uS#X6Ccd"E\Mef#gh!ij!krrN)rrr)rrr)rq )r'rqrorr)rrrsrtrurvrwrrrrrrrr=rrryrzr{)rls@rr~r~sS% N$JLM$'2>N9E9C,M#!!krr~) rvr(r*huggingface_hub.dataclassesrconfiguration_utilsrutilsrrr~__all__rrrrs#.3#34AC%AC5ACH343",3"53"l 4 5r