+ j{"lRt^RIHt^RIHt^RIHt]!RR7]!RR]444tR.tR#) zVITS model configuration)strict)PreTrainedConfig)auto_docstringzfacebook/mms-tts-eng) checkpointca]tRt^toRtRt^&t^t^t^t ^t Rt Rt Rt ^t^tRtRtRtRtRtRtR tRt^t^tR tRtRtRtRtRt^t ^t!^ t"R t#^t$R t%^t&R t'^t(^t)^t*^t+^t,Rt-Rt.Rt/Rt0Rt1Rt2Rt3V3Rlt4Rt5Vt6R#) VitsConfiga window_size (`int`, *optional*, defaults to 4): Window size for the relative positional embeddings in the attention layers of the Transformer encoder. use_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in the key, query, value projection layers in the Transformer encoder. ffn_kernel_size (`int`, *optional*, defaults to 3): Kernel size of the 1D convolution layers used by the feed-forward network in the Transformer encoder. flow_size (`int`, *optional*, defaults to 192): Dimensionality of the flow layers. spectrogram_bins (`int`, *optional*, defaults to 513): Number of frequency bins in the target spectrogram. use_stochastic_duration_prediction (`bool`, *optional*, defaults to `True`): Whether to use the stochastic duration prediction module or the regular duration predictor. num_speakers (`int`, *optional*, defaults to 1): Number of speakers if this is a multi-speaker model. speaker_embedding_size (`int`, *optional*, defaults to 0): Number of channels used by the speaker embeddings. Is zero for single-speaker models. upsample_initial_channel (`int`, *optional*, defaults to 512): The number of input channels into the HiFi-GAN upsampling network. upsample_rates (`tuple[int]` or `list[int]`, *optional*, defaults to `[8, 8, 2, 2]`): A tuple of integers defining the stride of each 1D convolutional layer in the HiFi-GAN 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 `[16, 16, 4, 4]`): A tuple of integers defining the kernel size of each 1D convolutional layer in the HiFi-GAN 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 HiFi-GAN 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 HiFi-GAN 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. depth_separable_channels (`int`, *optional*, defaults to 2): Number of channels to use in each depth-separable block. depth_separable_num_layers (`int`, *optional*, defaults to 3): Number of convolutional layers to use in each depth-separable block. duration_predictor_flow_bins (`int`, *optional*, defaults to 10): Number of channels to map using the unonstrained rational spline in the duration predictor model. duration_predictor_tail_bound (`float`, *optional*, defaults to 5.0): Value of the tail bin boundary when computing the unconstrained rational spline in the duration predictor model. duration_predictor_kernel_size (`int`, *optional*, defaults to 3): Kernel size of the 1D convolution layers used in the duration predictor model. duration_predictor_dropout (`float`, *optional*, defaults to 0.5): The dropout ratio for the duration predictor model. duration_predictor_num_flows (`int`, *optional*, defaults to 4): Number of flow stages used by the duration predictor model. duration_predictor_filter_channels (`int`, *optional*, defaults to 256): Number of channels for the convolution layers used in the duration predictor model. prior_encoder_num_flows (`int`, *optional*, defaults to 4): Number of flow stages used by the prior encoder flow model. prior_encoder_num_wavenet_layers (`int`, *optional*, defaults to 4): Number of WaveNet layers used by the prior encoder flow model. posterior_encoder_num_wavenet_layers (`int`, *optional*, defaults to 16): Number of WaveNet layers used by the posterior encoder model. wavenet_kernel_size (`int`, *optional*, defaults to 5): Kernel size of the 1D convolution layers used in the WaveNet model. wavenet_dilation_rate (`int`, *optional*, defaults to 1): Dilation rates of the dilated 1D convolutional layers used in the WaveNet model. wavenet_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the WaveNet layers. speaking_rate (`float`, *optional*, defaults to 1.0): Speaking rate. Larger values give faster synthesised speech. noise_scale (`float`, *optional*, defaults to 0.667): How random the speech prediction is. Larger values create more variation in the predicted speech. noise_scale_duration (`float`, *optional*, defaults to 0.8): How random the duration prediction is. Larger values create more variation in the predicted durations. Example: ```python >>> from transformers import VitsModel, VitsConfig >>> # Initializing a "facebook/mms-tts-eng" style configuration >>> configuration = VitsConfig() >>> # Initializing a model (with random weights) from the "facebook/mms-tts-eng" style configuration >>> model = VitsModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```vitsTig?irelug{Gz?gh㈵>ig@g?gg?gMbX?g?i>Nc \VP4\VP48wd9\R\VP4 R\VP4 R24hR#)zOPart of `@strict`-powered validation. Validates the architecture of the config.z'The length of `upsample_kernel_sizes` (z-) must match the length of `upsample_rates` ()N)lenupsample_kernel_sizesupsample_rates ValueError)selfs&l/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/vits/configuration_vits.pyvalidate_architecture VitsConfig.validate_architecturese t)) *c$2E2E.F F9#d>X>X:Y9Z[%%()<)<%=$>aA  Gc`<V^8dQh/S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[S[,;R&S[;R &S[;R &S[;R &S[;R &S[S[,;R &S[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[S[,;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R &S[S[,;R!&S[;R"&S[;R#&S[;R$&S[;R%&S[;R&&S[;R'&S[;R(&S[S[,;R)&S[S[,;R*&S[;R+&S[;R,&S[;R-&S[R.,;R/&#)0 vocab_size hidden_sizenum_hidden_layersnum_attention_heads window_sizeuse_biasffn_dim layerdropffn_kernel_size flow_sizespectrogram_bins hidden_acthidden_dropoutattention_dropoutactivation_dropoutinitializer_rangelayer_norm_eps"use_stochastic_duration_prediction num_speakersspeaker_embedding_sizeupsample_initial_channel.rrresblock_kernel_sizesresblock_dilation_sizesleaky_relu_slopedepth_separable_channelsdepth_separable_num_layersduration_predictor_flow_binsduration_predictor_tail_boundduration_predictor_kernel_sizeduration_predictor_dropoutduration_predictor_num_flows"duration_predictor_filter_channelsprior_encoder_num_flows prior_encoder_num_wavenet_layers$posterior_encoder_num_wavenet_layerswavenet_kernel_sizewavenet_dilation_ratewavenet_dropout speaking_rate noise_scalenoise_scale_duration sampling_rateN pad_token_id)intboolfloatstrlisttuple)format __classdict__s"r __annotate__VitsConfig.__annotate__svwxyz{| }~@ABCDs{ EFGHIJKLMNCK%OPs{(QR )ST#UV WX)-3YZ[\ #]^"'_`Ic3h/>ab 9uS#X6Gcd 9uS#X6Cef"E\Mgh!ij"%kl!$'mn#&*op$).qr%(+st!& 1uv#&)wx),1yz!${|'*-}~+.2@ AB"CDS[&EF3;$GHIJ %KLMN*#Or)rNrr)rOrP) )rQrTrT)7__name__ __module__ __qualname____firstlineno____doc__ model_typerrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,rrr-r.r/r0r1r2r3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBr__annotate_func____static_attributes____classdictcell__)rJs@rrrsT lJJK KHG IOIJ"%N%(&)# N/3&L"#$'2>N9G9C,M!$%&'(* +.!*+".1() .1&#$,-$02( !"#&O!$MK"%M#LSrrN) r[huggingface_hub.dataclassesrconfiguration_utilsrutilsrr__all__rMrrrdsJ.3#12M!M3M` .r