Zj{"dSrSSKJr SSKJr SSKJr \"SS9\"SS \555rS /rg ) zVITS model configuration)strict)PreTrainedConfig)auto_docstringzfacebook/mms-tts-eng) checkpointc\rSrSr%SrSrSr\\S'Sr \\S'Sr \\S 'S r \\S 'S r \\S 'Sr \\S'Sr\\S'Sr\\-\S'Sr\\S'Sr\\S'Sr\\S'Sr\\S'Sr\\-\S'Sr\\-\S'Sr\\-\S'Sr\\S'S r\\S!'Sr\\S"'S#r\\S$'S%r\\S&'S'r\\S('S)r \!\\"\S*4-\S+'S,r#\!\\"\S*4-\S-'S.r$\!\\"\S*4-\S/'S0r%\!\"-\S1'Sr&\\S2'S r'\\S3'Sr(\\S4'S5r)\\S6'S7r*\\S8'Sr+\\S9'S:r,\\-\S;'S r-\\S<'S=r.\\S>'S r/\\S?'S r0\\S@'SAr1\\SB'SCr2\\SD'S#r3\\SE'SFr4\\-\SG'SHr5\\-\SI'SJr6\\SK'SLr7\\SM'SNr8\\SO'SPr9\SP-\SQ'SRr:SSr;gP)T 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 ```vits& vocab_size hidden_sizenum_hidden_layersnum_attention_heads window_sizeTuse_biasiffn_dimg? layerdroprffn_kernel_size flow_sizeispectrogram_binsrelu hidden_acthidden_dropoutattention_dropoutactivation_dropoutg{Gz?initializer_rangegh㈵>layer_norm_eps"use_stochastic_duration_prediction num_speakersrspeaker_embedding_sizeiupsample_initial_channel)r(rr.upsample_rates)r*rrupsample_kernel_sizes)r resblock_kernel_sizes)r$rr/r/resblock_dilation_sizesleaky_relu_slopedepth_separable_channelsdepth_separable_num_layers duration_predictor_flow_binsg@duration_predictor_tail_boundduration_predictor_kernel_sizeg?duration_predictor_dropoutduration_predictor_num_flows"duration_predictor_filter_channelsprior_encoder_num_flows prior_encoder_num_wavenet_layersr*$posterior_encoder_num_wavenet_layersr0wavenet_kernel_sizewavenet_dilation_rategwavenet_dropoutg? speaking_rategMbX? noise_scaleg?noise_scale_durationi> sampling_rateN pad_token_idc[UR5[UR5:wa8[S[UR5S[UR5S35eg)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)lenr+r) ValueError)selfs |/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/vits/configuration_vits.pyvalidate_architecture VitsConfig.validate_architecturese t)) *c$2E2E.F F9#d>X>X:Y9Z[%%()<)<%=$>aA  G)<__name__ __module__ __qualname____firstlineno____doc__ model_typer int__annotations__rrrrrboolrrfloatrrrrstrrrr r!r"r#r%r&r'r)listtupler+r.r1r2r3r4r6r7r8r9r:r<r=r>r?r@rArBrCrDrErFrGrN__static_attributes__rQrPrMr r sIT lJJKs  KHdGS Ius{ OSIscJ"%NECK%%(us{(&) )#u# NE /3&3L#"#C#$'c'2>NDIc3h/>9G49uS#X6G9C49uS#X6C,MTE\M!e!$%c%&''(* #*+.!5.*+"C+.1 1() #).1&1#$S$,-$c-02(#2  !"3"#&OUS[&!$M53;$K"%%%M3#L#*#rPr N) rVhuggingface_hub.dataclassesrconfiguration_utilsrutilsrr __all__rQrPrMrdsJ.3#12M!M3M` .rP