Source code for transformers.models.speech_to_text_2.configuration_speech_to_text_2

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""" Speech2Text model configuration """

from ...configuration_utils import PretrainedConfig
from ...utils import logging

logger = logging.get_logger(__name__)

    "facebook/s2t-small-librispeech-asr": "",
    # See all Speech2Text models at

[docs]class Speech2Text2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.Speech2Text2ForCausalLM`. It is used to instantiate an Speech2Text2 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 Speech2Text2 `facebook/s2t-small-librispeech-asr <>`__ architecture. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: vocab_size (:obj:`int`, `optional`, defaults to 50265): Vocabulary size of the Speech2Text model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.Speech2TextModel` d_model (:obj:`int`, `optional`, defaults to 1024): Dimensionality of the layers and the pooler layer. decoder_layers (:obj:`int`, `optional`, defaults to 12): Number of decoder layers. decoder_attention_heads (:obj:`int`, `optional`, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (:obj:`int`, `optional`, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`): The non-linear activation function (function or string) in the pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported. dropout (:obj:`float`, `optional`, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, and pooler. attention_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. classifier_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for classifier. init_std (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices.>`__ for more details. decoder_layerdrop: (:obj:`float`, `optional`, defaults to 0.0): The LayerDrop probability for the decoder. See the `LayerDrop paper <see>`__ for more details. use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not the model should return the last key/values attentions (not used by all models). max_source_positions (:obj:`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: (:obj:`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). Example:: >>> from transformers import Speech2Text2ForCausalLM, Speech2Text2Config >>> # Initializing a Speech2Text2 s2t_transformer_s style configuration >>> configuration = Speech2Text2Config() >>> # Initializing a model from the s2t_transformer_s style configuration >>> model = Speech2Text2ForCausalLM(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "speech_to_text_2" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=10000, decoder_layers=6, decoder_ffn_dim=2048, decoder_attention_heads=4, decoder_layerdrop=0.0, use_cache=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, classifier_dropout=0.0, scale_embedding=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, max_source_positions=6000, max_target_positions=1024, **kwargs ): self.vocab_size = vocab_size self.d_model = d_model 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.decoder_layerdrop = decoder_layerdrop self.classifier_dropout = classifier_dropout self.use_cache = use_cache self.num_hidden_layers = decoder_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 super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, decoder_start_token_id=decoder_start_token_id, **kwargs, )