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"""VITS model configuration""" from huggingface_hub.dataclasses import strict from ...configuration_utils import PreTrainedConfig from ...utils import auto_docstring @auto_docstring(checkpoint="facebook/mms-tts-eng") @strict class VitsConfig(PreTrainedConfig): r""" 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 ```""" model_type = "vits" vocab_size: int = 38 hidden_size: int = 192 num_hidden_layers: int = 6 num_attention_heads: int = 2 window_size: int = 4 use_bias: bool = True ffn_dim: int = 768 layerdrop: float | int = 0.1 ffn_kernel_size: int = 3 flow_size: int = 192 spectrogram_bins: int = 513 hidden_act: str = "relu" hidden_dropout: float | int = 0.1 attention_dropout: float | int = 0.1 activation_dropout: float | int = 0.1 initializer_range: float = 0.02 layer_norm_eps: float = 1e-5 use_stochastic_duration_prediction: bool = True num_speakers: int = 1 speaker_embedding_size: int = 0 upsample_initial_channel: int = 512 upsample_rates: list[int] | tuple[int, ...] = (8, 8, 2, 2) upsample_kernel_sizes: list[int] | tuple[int, ...] = (16, 16, 4, 4) resblock_kernel_sizes: list[int] | tuple[int, ...] = (3, 7, 11) resblock_dilation_sizes: list | tuple = ((1, 3, 5), (1, 3, 5), (1, 3, 5)) leaky_relu_slope: float = 0.1 depth_separable_channels: int = 2 depth_separable_num_layers: int = 3 duration_predictor_flow_bins: int = 10 duration_predictor_tail_bound: float = 5.0 duration_predictor_kernel_size: int = 3 duration_predictor_dropout: float | int = 0.5 duration_predictor_num_flows: int = 4 duration_predictor_filter_channels: int = 256 prior_encoder_num_flows: int = 4 prior_encoder_num_wavenet_layers: int = 4 posterior_encoder_num_wavenet_layers: int = 16 wavenet_kernel_size: int = 5 wavenet_dilation_rate: int = 1 wavenet_dropout: float | int = 0.0 speaking_rate: float | int = 1.0 noise_scale: float = 0.667 noise_scale_duration: float = 0.8 sampling_rate: int = 16_000 pad_token_id: int | None = None def validate_architecture(self): """Part of `@strict`-powered validation. Validates the architecture of the config.""" if len(self.upsample_kernel_sizes) != len(self.upsample_rates): raise ValueError( f"The length of `upsample_kernel_sizes` ({len(self.upsample_kernel_sizes)}) must match the length of " f"`upsample_rates` ({len(self.upsample_rates)})" ) __all__ = ["VitsConfig"]