# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """UnivNetModel model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class UnivNetConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`UnivNetModel`]. It is used to instantiate a UnivNet vocoder model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the UnivNet [dg845/univnet-dev](https://huggingface.co/dg845/univnet-dev) architecture, which corresponds to the 'c32' architecture in [maum-ai/univnet](https://github.com/maum-ai/univnet/blob/master/config/default_c32.yaml). Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: model_in_channels (`int`, *optional*, defaults to 64): The number of input channels for the UnivNet residual network. This should correspond to `noise_sequence.shape[1]` and the value used in the [`UnivNetFeatureExtractor`] class. model_hidden_channels (`int`, *optional*, defaults to 32): The number of hidden channels of each residual block in the UnivNet residual network. num_mel_bins (`int`, *optional*, defaults to 100): The number of frequency bins in the conditioning log-mel spectrogram. This should correspond to the value used in the [`UnivNetFeatureExtractor`] class. resblock_kernel_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[3, 3, 3]`): A tuple of integers defining the kernel sizes of the 1D convolutional layers in the UnivNet residual network. The length of `resblock_kernel_sizes` defines the number of resnet blocks and should match that of `resblock_stride_sizes` and `resblock_dilation_sizes`. resblock_stride_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[8, 8, 4]`): A tuple of integers defining the stride sizes of the 1D convolutional layers in the UnivNet residual network. The length of `resblock_stride_sizes` should match that of `resblock_kernel_sizes` and `resblock_dilation_sizes`. resblock_dilation_sizes (`Tuple[Tuple[int]]` or `List[List[int]]`, *optional*, defaults to `[[1, 3, 9, 27], [1, 3, 9, 27], [1, 3, 9, 27]]`): A nested tuple of integers defining the dilation rates of the dilated 1D convolutional layers in the UnivNet residual network. The length of `resblock_dilation_sizes` should match that of `resblock_kernel_sizes` and `resblock_stride_sizes`. The length of each nested list in `resblock_dilation_sizes` defines the number of convolutional layers per resnet block. kernel_predictor_num_blocks (`int`, *optional*, defaults to 3): The number of residual blocks in the kernel predictor network, which calculates the kernel and bias for each location variable convolution layer in the UnivNet residual network. kernel_predictor_hidden_channels (`int`, *optional*, defaults to 64): The number of hidden channels for each residual block in the kernel predictor network. kernel_predictor_conv_size (`int`, *optional*, defaults to 3): The kernel size of each 1D convolutional layer in the kernel predictor network. kernel_predictor_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for each residual block in the kernel predictor network. initializer_range (`float`, *optional*, defaults to 0.01): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. leaky_relu_slope (`float`, *optional*, defaults to 0.2): The angle of the negative slope used by the leaky ReLU activation. Example: ```python >>> from transformers import UnivNetModel, UnivNetConfig >>> # Initializing a Tortoise TTS style configuration >>> configuration = UnivNetConfig() >>> # Initializing a model (with random weights) from the Tortoise TTS style configuration >>> model = UnivNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "univnet" def __init__( self, model_in_channels=64, model_hidden_channels=32, num_mel_bins=100, resblock_kernel_sizes=[3, 3, 3], resblock_stride_sizes=[8, 8, 4], resblock_dilation_sizes=[[1, 3, 9, 27], [1, 3, 9, 27], [1, 3, 9, 27]], kernel_predictor_num_blocks=3, kernel_predictor_hidden_channels=64, kernel_predictor_conv_size=3, kernel_predictor_dropout=0.0, initializer_range=0.01, leaky_relu_slope=0.2, **kwargs, ): if not (len(resblock_kernel_sizes) == len(resblock_stride_sizes) == len(resblock_dilation_sizes)): raise ValueError( "`resblock_kernel_sizes`, `resblock_stride_sizes`, and `resblock_dilation_sizes` must all have the" " same length (which will be the number of resnet blocks in the model)." ) self.model_in_channels = model_in_channels self.model_hidden_channels = model_hidden_channels self.num_mel_bins = num_mel_bins self.resblock_kernel_sizes = resblock_kernel_sizes self.resblock_stride_sizes = resblock_stride_sizes self.resblock_dilation_sizes = resblock_dilation_sizes self.kernel_predictor_num_blocks = kernel_predictor_num_blocks self.kernel_predictor_hidden_channels = kernel_predictor_hidden_channels self.kernel_predictor_conv_size = kernel_predictor_conv_size self.kernel_predictor_dropout = kernel_predictor_dropout self.initializer_range = initializer_range self.leaky_relu_slope = leaky_relu_slope super().__init__(**kwargs) __all__ = ["UnivNetConfig"]