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