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 speechtotextpretrainedconfigarchivemap facebooks2tsmalllibrispeechasr https huggingface cofacebooks2tsmalllibrispeechasrresolvemainconfig json see all speech2text models at https huggingface comodels filterspeechtotext class speech2textconfigpretrainedconfig 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 facebooks2tsmalllibrispeechasrhttps huggingface cofacebooks2tsmalllibrispeechasr 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 10000 vocabulary size of the speech2text model defines the number of different tokens that can be represented by the inputsids passed when calling speech2textmodel encoderlayers int optional defaults to 12 number of encoder layers encoderffndim int optional defaults to 2048 dimensionality of the intermediate often named feedforward layer in encoder encoderattentionheads int optional defaults to 4 number of attention heads for each attention layer in the transformer encoder decoderlayers int optional defaults to 6 number of decoder layers decoderffndim int optional defaults to 2048 dimensionality of the intermediate often named feedforward layer in decoder decoderattentionheads int optional defaults to 4 number of attention heads for each attention layer in the transformer decoder encoderlayerdrop float optional defaults to 0 0 the layerdrop probability for the encoder see the layerdrop paperhttps arxiv orgabs1909 11556 for more details decoderlayerdrop float optional defaults to 0 0 the layerdrop probability for the decoder see the layerdrop paperhttps arxiv orgabs1909 11556 for more details usecache bool optional defaults to true whether the model should return the last keyvalues attentions not used by all models isencoderdecoder bool optional defaults to true whether the model is set up as an encoderdecoder architecture for sequencetosequence tasks activationfunction str or function optional defaults to relu the nonlinear activation function function or string in the encoder and pooler if string gelu relu silu and gelunew are supported dmodel 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 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 decoderstarttokenid int optional defaults to 2 the initial token id of the decoder when decoding sequences scaleembedding bool optional defaults to true whether the embeddings are scaled by the square root of dmodel padtokenid int optional defaults to 1 padding token id bostokenid int optional defaults to 0 the id of the beginningofsequence token eostokenid int optional defaults to 2 the id of the endofsequence token maxsourcepositions int optional defaults to 6000 the maximum sequence length of logmel filterbank features that this model might ever be used with 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 numconvlayers int optional defaults to 2 number of 1d convolutional layers in the conv module convkernelsizes tupleint 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 convkernelsizes has to match numconvlayers convchannels 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 inputfeatperchannel int optional defaults to 80 an integer specifying the size of feature vector this is also the dimensions of logmel filterbank features inputchannels 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 s2ttransformers style configuration configuration speech2textconfig initializing a model with random weights from the s2ttransformers style configuration model speech2textmodelconfiguration accessing the model configuration configuration model config modeltype speechtotext keystoignoreatinference pastkeyvalues attributemap numattentionheads encoderattentionheads hiddensize dmodel def init self vocabsize10000 encoderlayers12 encoderffndim2048 encoderattentionheads4 decoderlayers6 decoderffndim2048 decoderattentionheads4 encoderlayerdrop0 0 decoderlayerdrop0 0 usecachetrue isencoderdecodertrue activationfunctionrelu dmodel256 dropout0 1 attentiondropout0 0 activationdropout0 0 initstd0 02 decoderstarttokenid2 scaleembeddingtrue padtokenid1 bostokenid0 eostokenid2 maxsourcepositions6000 maxtargetpositions1024 numconvlayers2 convkernelsizes5 5 convchannels1024 inputfeatperchannel80 inputchannels1 kwargs self vocabsize vocabsize self dmodel dmodel self encoderffndim encoderffndim self encoderlayers encoderlayers self encoderattentionheads encoderattentionheads 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 encoderlayerdrop encoderlayerdrop self decoderlayerdrop decoderlayerdrop self usecache usecache self numhiddenlayers encoderlayers self scaleembedding scaleembedding scale factor will be sqrtdmodel if true self maxsourcepositions maxsourcepositions self maxtargetpositions maxtargetpositions self numconvlayers numconvlayers self convkernelsizes listconvkernelsizes self convchannels convchannels self inputfeatperchannel inputfeatperchannel self inputchannels inputchannels if lenself convkernelsizes self numconvlayers raise valueerror configuration for convolutional module is incorrect it is required that lenconfig convkernelsizes config numconvlayers fbut is lenconfig convkernelsizes lenself convkernelsizes fconfig numconvlayers self numconvlayers super init padtokenidpadtokenid bostokenidbostokenid eostokenideostokenid isencoderdecoderisencoderdecoder 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 speech_to_text 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 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_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    
}


class Speech2TextConfig(PretrainedConfig):
    r

    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  
        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,
        )