126 lines
6.7 KiB
Python
126 lines
6.7 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" UnivNetModel model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import UNIVNET_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class UnivNetConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`UnivNetModel`]. It is used to instantiate a
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UnivNet vocoder model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the UnivNet
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[dg845/univnet-dev](https://huggingface.co/dg845/univnet-dev) architecture, which corresponds to the 'c32'
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architecture in [maum-ai/univnet](https://github.com/maum-ai/univnet/blob/master/config/default_c32.yaml).
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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model_in_channels (`int`, *optional*, defaults to 64):
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The number of input channels for the UnivNet residual network. This should correspond to
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`noise_sequence.shape[1]` and the value used in the [`UnivNetFeatureExtractor`] class.
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model_hidden_channels (`int`, *optional*, defaults to 32):
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The number of hidden channels of each residual block in the UnivNet residual network.
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num_mel_bins (`int`, *optional*, defaults to 100):
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The number of frequency bins in the conditioning log-mel spectrogram. This should correspond to the value
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used in the [`UnivNetFeatureExtractor`] class.
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resblock_kernel_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[3, 3, 3]`):
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A tuple of integers defining the kernel sizes of the 1D convolutional layers in the UnivNet residual
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network. The length of `resblock_kernel_sizes` defines the number of resnet blocks and should match that of
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`resblock_stride_sizes` and `resblock_dilation_sizes`.
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resblock_stride_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[8, 8, 4]`):
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A tuple of integers defining the stride sizes of the 1D convolutional layers in the UnivNet residual
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network. The length of `resblock_stride_sizes` should match that of `resblock_kernel_sizes` and
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`resblock_dilation_sizes`.
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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]]`):
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A nested tuple of integers defining the dilation rates of the dilated 1D convolutional layers in the
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UnivNet residual network. The length of `resblock_dilation_sizes` should match that of
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`resblock_kernel_sizes` and `resblock_stride_sizes`. The length of each nested list in
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`resblock_dilation_sizes` defines the number of convolutional layers per resnet block.
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kernel_predictor_num_blocks (`int`, *optional*, defaults to 3):
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The number of residual blocks in the kernel predictor network, which calculates the kernel and bias for
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each location variable convolution layer in the UnivNet residual network.
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kernel_predictor_hidden_channels (`int`, *optional*, defaults to 64):
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The number of hidden channels for each residual block in the kernel predictor network.
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kernel_predictor_conv_size (`int`, *optional*, defaults to 3):
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The kernel size of each 1D convolutional layer in the kernel predictor network.
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kernel_predictor_dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for each residual block in the kernel predictor network.
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initializer_range (`float`, *optional*, defaults to 0.01):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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leaky_relu_slope (`float`, *optional*, defaults to 0.2):
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The angle of the negative slope used by the leaky ReLU activation.
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Example:
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```python
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>>> from transformers import UnivNetModel, UnivNetConfig
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>>> # Initializing a Tortoise TTS style configuration
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>>> configuration = UnivNetConfig()
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>>> # Initializing a model (with random weights) from the Tortoise TTS style configuration
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>>> model = UnivNetModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
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model_type = "univnet"
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def __init__(
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self,
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model_in_channels=64,
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model_hidden_channels=32,
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num_mel_bins=100,
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resblock_kernel_sizes=[3, 3, 3],
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resblock_stride_sizes=[8, 8, 4],
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resblock_dilation_sizes=[[1, 3, 9, 27], [1, 3, 9, 27], [1, 3, 9, 27]],
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kernel_predictor_num_blocks=3,
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kernel_predictor_hidden_channels=64,
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kernel_predictor_conv_size=3,
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kernel_predictor_dropout=0.0,
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initializer_range=0.01,
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leaky_relu_slope=0.2,
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**kwargs,
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):
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if not (len(resblock_kernel_sizes) == len(resblock_stride_sizes) == len(resblock_dilation_sizes)):
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raise ValueError(
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"`resblock_kernel_sizes`, `resblock_stride_sizes`, and `resblock_dilation_sizes` must all have the"
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" same length (which will be the number of resnet blocks in the model)."
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)
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self.model_in_channels = model_in_channels
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self.model_hidden_channels = model_hidden_channels
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self.num_mel_bins = num_mel_bins
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self.resblock_kernel_sizes = resblock_kernel_sizes
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self.resblock_stride_sizes = resblock_stride_sizes
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self.resblock_dilation_sizes = resblock_dilation_sizes
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self.kernel_predictor_num_blocks = kernel_predictor_num_blocks
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self.kernel_predictor_hidden_channels = kernel_predictor_hidden_channels
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self.kernel_predictor_conv_size = kernel_predictor_conv_size
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self.kernel_predictor_dropout = kernel_predictor_dropout
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self.initializer_range = initializer_range
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self.leaky_relu_slope = leaky_relu_slope
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super().__init__(**kwargs)
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