codingutf8 2021 the huggingface inc team 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 orglicenseslicense2 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 configurationutils import pretrainedconfig from utils import logging logger logging getloggername speechtotext2pretrainedconfigarchivemap facebooks2twav2vec2largeende https huggingface cofacebooks2twav2vec2largeenderesolvemainconfig json see all speech2text models at https huggingface comodels filterspeech2text2 class speech2text2configpretrainedconfig r this is the configuration class to store the configuration of a 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 facebooks2twav2vec2largeendehttps huggingface cofacebooks2twav2vec2largeende architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args vocabsize int optional defaults to 50265 vocabulary size of the speech2text model defines the number of different tokens that can be represented by the inputsids passed when calling speech2textmodel dmodel int optional defaults to 1024 dimensionality of the layers and the pooler layer decoderlayers int optional defaults to 12 number of decoder layers decoderattentionheads int optional defaults to 16 number of attention heads for each attention layer in the transformer decoder decoderffndim int optional defaults to 4096 dimensionality of the intermediate often named feedforward layer in decoder activationfunction str or function optional defaults to gelu the nonlinear activation function function or string in the pooler if string gelu relu silu and gelunew are supported dropout float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings and pooler attentiondropout float optional defaults to 0 0 the dropout ratio for the attention probabilities activationdropout float optional defaults to 0 0 the dropout ratio for activations inside the fully connected layer initstd float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices https arxiv orgabs1909 11556 for more details decoderlayerdrop float optional defaults to 0 0 the layerdrop probability for the decoder see the layerdrop papersee https arxiv orgabs1909 11556 for more details usecache bool optional defaults to true whether or not the model should return the last keyvalues attentions not used by all models maxtargetpositions 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 python from transformers import speech2text2config speech2text2forcausallm initializing a speech2text2 s2ttransformers style configuration configuration speech2text2config initializing a model with random weights from the s2ttransformers style configuration model speech2text2forcausallmconfiguration accessing the model configuration configuration model config modeltype speechtotext2 keystoignoreatinference pastkeyvalues attributemap numattentionheads decoderattentionheads hiddensize dmodel def init self vocabsize10000 decoderlayers6 decoderffndim2048 decoderattentionheads4 decoderlayerdrop0 0 usecachetrue activationfunctionrelu dmodel256 dropout0 1 attentiondropout0 0 activationdropout0 0 initstd0 02 decoderstarttokenid2 scaleembeddingtrue padtokenid1 bostokenid0 eostokenid2 maxtargetpositions1024 kwargs self vocabsize vocabsize self dmodel dmodel self decoderffndim decoderffndim self decoderlayers decoderlayers self decoderattentionheads decoderattentionheads self dropout dropout self attentiondropout attentiondropout self activationdropout activationdropout self activationfunction activationfunction self initstd initstd self decoderlayerdrop decoderlayerdrop self usecache usecache self numhiddenlayers decoderlayers self scaleembedding scaleembedding scale factor will be sqrtdmodel if true self maxtargetpositions maxtargetpositions super init padtokenidpadtokenid bostokenidbostokenid eostokenideostokenid decoderstarttokeniddecoderstarttokenid kwargs coding utf 8 2021 the huggingface inc team 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 see all speech2text models at https huggingface co models filter speech2text2 this is the configuration class to store the configuration of a 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 wav2vec2 large en de https huggingface co facebook s2t wav2vec2 large en de 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 50265 vocabulary size of the speech2text model defines the number of different tokens that can be represented by the inputs_ids passed when calling speech2textmodel d_model int optional defaults to 1024 dimensionality of the layers and the pooler layer decoder_layers int optional defaults to 12 number of decoder layers decoder_attention_heads int optional defaults to 16 number of attention heads for each attention layer in the transformer decoder decoder_ffn_dim int optional defaults to 4096 dimensionality of the intermediate often named feed forward layer in decoder activation_function str or function optional defaults to gelu the non linear activation function function or string in the pooler if string gelu relu silu and gelu_new are supported dropout float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings 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 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 see https arxiv org abs 1909 11556 for more details use_cache bool optional defaults to true whether or not the model should return the last key values attentions not used by all models 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 example python from transformers import speech2text2config speech2text2forcausallm initializing a speech2text2 s2t_transformer_s style configuration configuration speech2text2config initializing a model with random weights from the s2t_transformer_s style configuration model speech2text2forcausallm configuration accessing the model configuration configuration model config scale factor will be sqrt d_model if true
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    
}


class Speech2Text2Config(PretrainedConfig):
    r

    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,
        scale_embedding=True,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        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.use_cache = use_cache
        self.num_hidden_layers = decoder_layers
        self.scale_embedding = scale_embedding  
        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,
        )