428 lines
23 KiB
Python
428 lines
23 KiB
Python
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# coding=utf-8
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# Copyright 2023 The Fairseq Authors, Microsoft Research, and the HuggingFace Inc. 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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""" SpeechT5 model configuration"""
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import functools
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import operator
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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 SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP = {
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"microsoft/speecht5_hifigan": "https://huggingface.co/microsoft/speecht5_hifigan/resolve/main/config.json",
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}
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class SpeechT5Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`SpeechT5Model`]. It is used to instantiate a
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SpeechT5 model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the SpeechT5
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[microsoft/speecht5_asr](https://huggingface.co/microsoft/speecht5_asr) architecture.
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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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vocab_size (`int`, *optional*, defaults to 81):
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Vocabulary size of the SpeechT5 model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed to the forward method of [`SpeechT5Model`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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encoder_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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encoder_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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encoder_ffn_dim (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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encoder_layerdrop (`float`, *optional*, defaults to 0.1):
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The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
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for more details.
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decoder_layers (`int`, *optional*, defaults to 6):
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Number of hidden layers in the Transformer decoder.
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decoder_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer decoder.
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decoder_ffn_dim (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer decoder.
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decoder_layerdrop (`float`, *optional*, defaults to 0.1):
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The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
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for more details.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` are supported.
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positional_dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for the text position encoding layers.
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hidden_dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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activation_dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio for activations inside the fully connected layer.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-5):
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The epsilon used by the layer normalization layers.
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scale_embedding (`bool`, *optional*, defaults to `False`):
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Scale embeddings by diving by sqrt(d_model).
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feat_extract_norm (`str`, *optional*, defaults to `"group"`):
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The norm to be applied to 1D convolutional layers in the speech encoder pre-net. One of `"group"` for group
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normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
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convolutional layers.
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feat_proj_dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for output of the speech encoder pre-net.
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feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the 1D convolutional layers of the feature
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extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
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conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
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A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
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speech encoder pre-net. The length of *conv_dim* defines the number of 1D convolutional layers.
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conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
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A tuple of integers defining the stride of each 1D convolutional layer in the speech encoder pre-net. The
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length of *conv_stride* defines the number of convolutional layers and has to match the length of
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*conv_dim*.
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conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
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A tuple of integers defining the kernel size of each 1D convolutional layer in the speech encoder pre-net.
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The length of *conv_kernel* defines the number of convolutional layers and has to match the length of
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*conv_dim*.
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conv_bias (`bool`, *optional*, defaults to `False`):
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Whether the 1D convolutional layers have a bias.
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num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
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Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
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embeddings layer.
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num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
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Number of groups of 1D convolutional positional embeddings layer.
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apply_spec_augment (`bool`, *optional*, defaults to `True`):
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Whether to apply *SpecAugment* data augmentation to the outputs of the speech encoder pre-net. For
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reference see [SpecAugment: A Simple Data Augmentation Method for Automatic Speech
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Recognition](https://arxiv.org/abs/1904.08779).
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mask_time_prob (`float`, *optional*, defaults to 0.05):
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Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
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procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
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reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
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masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
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actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`.
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mask_time_length (`int`, *optional*, defaults to 10):
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Length of vector span along the time axis.
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mask_time_min_masks (`int`, *optional*, defaults to 2),:
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The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
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irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
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mask_time_min_masks''
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mask_feature_prob (`float`, *optional*, defaults to 0.0):
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Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
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masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
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the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
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span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
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may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
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True`.
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mask_feature_length (`int`, *optional*, defaults to 10):
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Length of vector span along the feature axis.
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mask_feature_min_masks (`int`, *optional*, defaults to 0),:
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The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
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step, irrespectively of `mask_feature_prob`. Only relevant if
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''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
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num_mel_bins (`int`, *optional*, defaults to 80):
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Number of mel features used per input features. Used by the speech decoder pre-net. Should correspond to
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the value used in the [`SpeechT5Processor`] class.
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speech_decoder_prenet_layers (`int`, *optional*, defaults to 2):
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Number of layers in the speech decoder pre-net.
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speech_decoder_prenet_units (`int`, *optional*, defaults to 256):
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Dimensionality of the layers in the speech decoder pre-net.
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speech_decoder_prenet_dropout (`float`, *optional*, defaults to 0.5):
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The dropout probability for the speech decoder pre-net layers.
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speaker_embedding_dim (`int`, *optional*, defaults to 512):
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Dimensionality of the *XVector* embedding vectors.
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speech_decoder_postnet_layers (`int`, *optional*, defaults to 5):
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Number of layers in the speech decoder post-net.
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speech_decoder_postnet_units (`int`, *optional*, defaults to 256):
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Dimensionality of the layers in the speech decoder post-net.
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speech_decoder_postnet_kernel (`int`, *optional*, defaults to 5):
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Number of convolutional filter channels in the speech decoder post-net.
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speech_decoder_postnet_dropout (`float`, *optional*, defaults to 0.5):
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The dropout probability for the speech decoder post-net layers.
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reduction_factor (`int`, *optional*, defaults to 2):
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Spectrogram length reduction factor for the speech decoder inputs.
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max_speech_positions (`int`, *optional*, defaults to 4000):
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The maximum sequence length of speech features that this model might ever be used with.
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max_text_positions (`int`, *optional*, defaults to 450):
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The maximum sequence length of text features that this model might ever be used with.
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encoder_max_relative_position (`int`, *optional*, defaults to 160):
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Maximum distance for relative position embedding in the encoder.
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use_guided_attention_loss (`bool`, *optional*, defaults to `True`):
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Whether to apply guided attention loss while training the TTS model.
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guided_attention_loss_num_heads (`int`, *optional*, defaults to 2):
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Number of attention heads the guided attention loss will be applied to. Use -1 to apply this loss to all
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attention heads.
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guided_attention_loss_sigma (`float`, *optional*, defaults to 0.4):
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Standard deviation for guided attention loss.
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guided_attention_loss_scale (`float`, *optional*, defaults to 10.0):
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Scaling coefficient for guided attention loss (also known as lambda).
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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Example:
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```python
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>>> from transformers import SpeechT5Model, SpeechT5Config
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>>> # Initializing a "microsoft/speecht5_asr" style configuration
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>>> configuration = SpeechT5Config()
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>>> # Initializing a model (with random weights) from the "microsoft/speecht5_asr" style configuration
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>>> model = SpeechT5Model(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "speecht5"
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attribute_map = {"num_attention_heads": "encoder_attention_heads", "num_hidden_layers": "encoder_layers"}
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def __init__(
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self,
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vocab_size=81,
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hidden_size=768,
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encoder_layers=12,
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encoder_attention_heads=12,
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encoder_ffn_dim=3072,
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encoder_layerdrop=0.1,
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decoder_layers=6,
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decoder_ffn_dim=3072,
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decoder_attention_heads=12,
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decoder_layerdrop=0.1,
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hidden_act="gelu",
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positional_dropout=0.1,
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hidden_dropout=0.1,
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attention_dropout=0.1,
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activation_dropout=0.1,
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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scale_embedding=False,
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feat_extract_norm="group",
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feat_proj_dropout=0.0,
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feat_extract_activation="gelu",
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conv_dim=(512, 512, 512, 512, 512, 512, 512),
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conv_stride=(5, 2, 2, 2, 2, 2, 2),
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conv_kernel=(10, 3, 3, 3, 3, 2, 2),
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conv_bias=False,
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num_conv_pos_embeddings=128,
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num_conv_pos_embedding_groups=16,
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apply_spec_augment=True,
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mask_time_prob=0.05,
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mask_time_length=10,
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mask_time_min_masks=2,
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mask_feature_prob=0.0,
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mask_feature_length=10,
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mask_feature_min_masks=0,
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pad_token_id=1,
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bos_token_id=0,
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eos_token_id=2,
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decoder_start_token_id=2,
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num_mel_bins=80,
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speech_decoder_prenet_layers=2,
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speech_decoder_prenet_units=256,
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speech_decoder_prenet_dropout=0.5,
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speaker_embedding_dim=512,
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speech_decoder_postnet_layers=5,
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speech_decoder_postnet_units=256,
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speech_decoder_postnet_kernel=5,
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speech_decoder_postnet_dropout=0.5,
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reduction_factor=2,
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max_speech_positions=4000,
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max_text_positions=450,
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encoder_max_relative_position=160,
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use_guided_attention_loss=True,
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guided_attention_loss_num_heads=2,
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guided_attention_loss_sigma=0.4,
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guided_attention_loss_scale=10.0,
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use_cache=True,
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is_encoder_decoder=True,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.encoder_layers = encoder_layers
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self.encoder_ffn_dim = encoder_ffn_dim
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self.encoder_attention_heads = encoder_attention_heads
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self.encoder_layerdrop = encoder_layerdrop
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self.decoder_layers = decoder_layers
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self.decoder_ffn_dim = decoder_ffn_dim
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self.decoder_attention_heads = decoder_attention_heads
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self.decoder_layerdrop = decoder_layerdrop
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self.hidden_act = hidden_act
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self.positional_dropout = positional_dropout
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.scale_embedding = scale_embedding
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self.feat_extract_norm = feat_extract_norm
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self.feat_proj_dropout = feat_proj_dropout
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self.feat_extract_activation = feat_extract_activation
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self.conv_dim = list(conv_dim)
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self.conv_stride = list(conv_stride)
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self.conv_kernel = list(conv_kernel)
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self.conv_bias = conv_bias
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self.num_conv_pos_embeddings = num_conv_pos_embeddings
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self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
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self.num_feat_extract_layers = len(self.conv_dim)
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if (
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(len(self.conv_stride) != self.num_feat_extract_layers)
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or (len(self.conv_kernel) != self.num_feat_extract_layers)
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or (len(self.conv_dim) != self.num_feat_extract_layers)
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):
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raise ValueError(
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"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
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" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
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f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
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f" `len(config.conv_kernel) = {len(self.conv_kernel)}`."
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)
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||
|
|
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||
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|
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
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|
|
self.apply_spec_augment = apply_spec_augment
|
||
|
|
self.mask_time_prob = mask_time_prob
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||
|
|
self.mask_time_length = mask_time_length
|
||
|
|
self.mask_time_min_masks = mask_time_min_masks
|
||
|
|
self.mask_feature_prob = mask_feature_prob
|
||
|
|
self.mask_feature_length = mask_feature_length
|
||
|
|
self.mask_feature_min_masks = mask_feature_min_masks
|
||
|
|
|
||
|
|
self.num_mel_bins = num_mel_bins
|
||
|
|
self.speech_decoder_prenet_layers = speech_decoder_prenet_layers
|
||
|
|
self.speech_decoder_prenet_units = speech_decoder_prenet_units
|
||
|
|
self.speech_decoder_prenet_dropout = speech_decoder_prenet_dropout
|
||
|
|
self.speaker_embedding_dim = speaker_embedding_dim
|
||
|
|
|
||
|
|
self.speech_decoder_postnet_layers = speech_decoder_postnet_layers
|
||
|
|
self.speech_decoder_postnet_units = speech_decoder_postnet_units
|
||
|
|
self.speech_decoder_postnet_kernel = speech_decoder_postnet_kernel
|
||
|
|
self.speech_decoder_postnet_dropout = speech_decoder_postnet_dropout
|
||
|
|
self.reduction_factor = reduction_factor
|
||
|
|
|
||
|
|
self.max_speech_positions = max_speech_positions
|
||
|
|
self.max_text_positions = max_text_positions
|
||
|
|
self.encoder_max_relative_position = encoder_max_relative_position
|
||
|
|
|
||
|
|
self.use_guided_attention_loss = use_guided_attention_loss
|
||
|
|
self.guided_attention_loss_num_heads = guided_attention_loss_num_heads
|
||
|
|
self.guided_attention_loss_sigma = guided_attention_loss_sigma
|
||
|
|
self.guided_attention_loss_scale = guided_attention_loss_scale
|
||
|
|
|
||
|
|
self.use_cache = use_cache
|
||
|
|
self.is_encoder_decoder = is_encoder_decoder
|
||
|
|
|
||
|
|
super().__init__(
|
||
|
|
pad_token_id=pad_token_id,
|
||
|
|
bos_token_id=bos_token_id,
|
||
|
|
eos_token_id=eos_token_id,
|
||
|
|
is_encoder_decoder=is_encoder_decoder,
|
||
|
|
decoder_start_token_id=decoder_start_token_id,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
def inputs_to_logits_ratio(self):
|
||
|
|
return functools.reduce(operator.mul, self.conv_stride, 1)
|
||
|
|
|
||
|
|
|
||
|
|
class SpeechT5HifiGanConfig(PretrainedConfig):
|
||
|
|
r"""
|
||
|
|
This is the configuration class to store the configuration of a [`SpeechT5HifiGanModel`]. It is used to instantiate
|
||
|
|
a SpeechT5 HiFi-GAN 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 SpeechT5
|
||
|
|
[microsoft/speecht5_hifigan](https://huggingface.co/microsoft/speecht5_hifigan) architecture.
|
||
|
|
|
||
|
|
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_dim (`int`, *optional*, defaults to 80):
|
||
|
|
The number of frequency bins in the input log-mel spectrogram.
|
||
|
|
sampling_rate (`int`, *optional*, defaults to 16000):
|
||
|
|
The sampling rate at which the output audio will be generated, expressed in hertz (Hz).
|
||
|
|
upsample_initial_channel (`int`, *optional*, defaults to 512):
|
||
|
|
The number of input channels into the upsampling network.
|
||
|
|
upsample_rates (`Tuple[int]` or `List[int]`, *optional*, defaults to `[4, 4, 4, 4]`):
|
||
|
|
A tuple of integers defining the stride of each 1D convolutional layer in the 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 `[8, 8, 8, 8]`):
|
||
|
|
A tuple of integers defining the kernel size of each 1D convolutional layer in the 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 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
|
||
|
|
multi-receptive field fusion (MRF) module.
|
||
|
|
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 0.1):
|
||
|
|
The angle of the negative slope used by the leaky ReLU activation.
|
||
|
|
normalize_before (`bool`, *optional*, defaults to `True`):
|
||
|
|
Whether or not to normalize the spectrogram before vocoding using the vocoder's learned mean and variance.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import SpeechT5HifiGan, SpeechT5HifiGanConfig
|
||
|
|
|
||
|
|
>>> # Initializing a "microsoft/speecht5_hifigan" style configuration
|
||
|
|
>>> configuration = SpeechT5HifiGanConfig()
|
||
|
|
|
||
|
|
>>> # Initializing a model (with random weights) from the "microsoft/speecht5_hifigan" style configuration
|
||
|
|
>>> model = SpeechT5HifiGan(configuration)
|
||
|
|
|
||
|
|
>>> # Accessing the model configuration
|
||
|
|
>>> configuration = model.config
|
||
|
|
```"""
|
||
|
|
|
||
|
|
model_type = "hifigan"
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
model_in_dim=80,
|
||
|
|
sampling_rate=16000,
|
||
|
|
upsample_initial_channel=512,
|
||
|
|
upsample_rates=[4, 4, 4, 4],
|
||
|
|
upsample_kernel_sizes=[8, 8, 8, 8],
|
||
|
|
resblock_kernel_sizes=[3, 7, 11],
|
||
|
|
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
|
||
|
|
initializer_range=0.01,
|
||
|
|
leaky_relu_slope=0.1,
|
||
|
|
normalize_before=True,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
self.model_in_dim = model_in_dim
|
||
|
|
self.sampling_rate = sampling_rate
|
||
|
|
self.upsample_initial_channel = upsample_initial_channel
|
||
|
|
self.upsample_rates = upsample_rates
|
||
|
|
self.upsample_kernel_sizes = upsample_kernel_sizes
|
||
|
|
self.resblock_kernel_sizes = resblock_kernel_sizes
|
||
|
|
self.resblock_dilation_sizes = resblock_dilation_sizes
|
||
|
|
self.initializer_range = initializer_range
|
||
|
|
self.leaky_relu_slope = leaky_relu_slope
|
||
|
|
self.normalize_before = normalize_before
|
||
|
|
super().__init__(**kwargs)
|