# coding=utf-8 # Copyright 2021 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. """Speech2Text model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class Speech2TextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Speech2TextModel`]. It is used to instantiate a Speech2Text 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 Speech2Text [facebook/s2t-small-librispeech-asr](https://huggingface.co/facebook/s2t-small-librispeech-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 10000): Vocabulary size of the Speech2Text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Speech2TextModel`] encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. encoder_ffn_dim (`int`, *optional*, defaults to 2048): Dimensionality of the "intermediate" (often named feed-forward) layer in encoder. encoder_attention_heads (`int`, *optional*, defaults to 4): Number of attention heads for each attention layer in the Transformer encoder. decoder_layers (`int`, *optional*, defaults to 6): Number of decoder layers. decoder_ffn_dim (`int`, *optional*, defaults to 2048): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. decoder_attention_heads (`int`, *optional*, defaults to 4): Number of attention heads for each attention layer in the Transformer decoder. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](https://arxiv.org/abs/1909.11556) for more details. use_cache (`bool`, *optional*, defaults to `True`): Whether the model should return the last key/values attentions (not used by all models). is_encoder_decoder (`bool`, *optional*, defaults to `True`): Whether the model is set up as an encoder-decoder architecture for sequence-to-sequence tasks. activation_function (`str` or `function`, *optional*, defaults to `"relu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. d_model (`int`, *optional*, defaults to 256): Dimensionality of the layers and the pooler layer. 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 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. decoder_start_token_id (`int`, *optional*, defaults to 2): The initial token ID of the decoder when decoding sequences. scale_embedding (`bool`, *optional*, defaults to `True`): Whether the embeddings are scaled by the square root of `d_model`. pad_token_id (`int`, *optional*, defaults to 1): Padding token id. bos_token_id (`int`, *optional*, defaults to 0): The id of the beginning-of-sequence token. eos_token_id (`int`, *optional*, defaults to 2): The id of the end-of-sequence token. max_source_positions (`int`, *optional*, defaults to 6000): The maximum sequence length of log-mel filter-bank features that this model might ever be used with. max_target_positions (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically, set this to something large just in case (e.g., 512 or 1024 or 2048). num_conv_layers (`int`, *optional*, defaults to 2): Number of 1D convolutional layers in the conv module. conv_kernel_sizes (`Tuple[int]`, *optional*, defaults to `(5, 5)`): A tuple of integers defining the kernel size of each 1D convolutional layer in the conv module. The length of `conv_kernel_sizes` has to match `num_conv_layers`. conv_channels (`int`, *optional*, defaults to 1024): An integer defining the number of output channels of each convolution layers except the final one in the conv module. input_feat_per_channel (`int`, *optional*, defaults to 80): An integer specifying the size of feature vector. This is also the dimensions of log-mel filter-bank features. input_channels (`int`, *optional*, defaults to 1): An integer specifying number of input channels of the input feature vector. Example: ```python >>> from transformers import Speech2TextConfig, Speech2TextModel >>> # Initializing a Speech2Text s2t_transformer_s style configuration >>> configuration = Speech2TextConfig() >>> # Initializing a model (with random weights) from the s2t_transformer_s style configuration >>> model = Speech2TextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "speech_to_text" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=10000, encoder_layers=12, encoder_ffn_dim=2048, encoder_attention_heads=4, decoder_layers=6, decoder_ffn_dim=2048, decoder_attention_heads=4, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function="relu", d_model=256, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=2, scale_embedding=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, max_source_positions=6000, max_target_positions=1024, num_conv_layers=2, conv_kernel_sizes=(5, 5), conv_channels=1024, input_feat_per_channel=80, input_channels=1, **kwargs, ): self.vocab_size = vocab_size self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.max_source_positions = max_source_positions self.max_target_positions = max_target_positions self.num_conv_layers = num_conv_layers self.conv_kernel_sizes = list(conv_kernel_sizes) self.conv_channels = conv_channels self.input_feat_per_channel = input_feat_per_channel self.input_channels = input_channels if len(self.conv_kernel_sizes) != self.num_conv_layers: raise ValueError( "Configuration for convolutional module is incorrect. " "It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` " f"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`, " f"`config.num_conv_layers = {self.num_conv_layers}`." ) 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, )