codingutf8 2022 google ai and 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 audio spectogram transformer ast model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername audiospectrogramtransformerpretrainedconfigarchivemap mitastfinetunedaudioset10100 4593 https huggingface comitastfinetunedaudioset10100 4593resolvemainconfig json class astconfigpretrainedconfig r this is the configuration class to store the configuration of a astmodel it is used to instantiate an ast 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 ast mitastfinetunedaudioset10100 4593https huggingface comitastfinetunedaudioset10100 4593 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args hiddensize int optional defaults to 768 dimensionality of the encoder layers and the pooler layer numhiddenlayers int optional defaults to 12 number of hidden layers in the transformer encoder numattentionheads int optional defaults to 12 number of attention heads for each attention layer in the transformer encoder intermediatesize int optional defaults to 3072 dimensionality of the intermediate i e feedforward layer in the transformer encoder hiddenact str or function optional defaults to gelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu selu and gelunew are supported hiddendropoutprob float optional defaults to 0 0 the dropout probability for all fully connected layers in the embeddings encoder and pooler attentionprobsdropoutprob float optional defaults to 0 0 the dropout ratio for the attention probabilities initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices layernormeps float optional defaults to 1e12 the epsilon used by the layer normalization layers patchsize int optional defaults to 16 the size resolution of each patch qkvbias bool optional defaults to true whether to add a bias to the queries keys and values frequencystride int optional defaults to 10 frequency stride to use when patchifying the spectrograms timestride int optional defaults to 10 temporal stride to use when patchifying the spectrograms maxlength int optional defaults to 1024 temporal dimension of the spectrograms nummelbins int optional defaults to 128 frequency dimension of the spectrograms number of melfrequency bins example python from transformers import astconfig astmodel initializing a ast mitastfinetunedaudioset10100 4593 style configuration configuration astconfig initializing a model with random weights from the mitastfinetunedaudioset10100 4593 style configuration model astmodelconfiguration accessing the model configuration configuration model config modeltype audiospectrogramtransformer def init self hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelu hiddendropoutprob0 0 attentionprobsdropoutprob0 0 initializerrange0 02 layernormeps1e12 patchsize16 qkvbiastrue frequencystride10 timestride10 maxlength1024 nummelbins128 kwargs super initkwargs self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self initializerrange initializerrange self layernormeps layernormeps self patchsize patchsize self qkvbias qkvbias self frequencystride frequencystride self timestride timestride self maxlength maxlength self nummelbins nummelbins coding utf 8 2022 google ai and 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 audio spectogram transformer ast model configuration this is the configuration class to store the configuration of a astmodel it is used to instantiate an ast 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 ast mit ast finetuned audioset 10 10 0 4593 https huggingface co mit ast finetuned audioset 10 10 0 4593 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args hidden_size int optional defaults to 768 dimensionality of the encoder layers and the pooler layer num_hidden_layers int optional defaults to 12 number of hidden layers in the transformer encoder num_attention_heads int optional defaults to 12 number of attention heads for each attention layer in the transformer encoder intermediate_size int optional defaults to 3072 dimensionality of the intermediate i e feed forward layer in the transformer encoder 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 hidden_dropout_prob float optional defaults to 0 0 the dropout probability for all fully connected layers in the embeddings encoder and pooler attention_probs_dropout_prob float optional defaults to 0 0 the dropout ratio for the attention probabilities 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 12 the epsilon used by the layer normalization layers patch_size int optional defaults to 16 the size resolution of each patch qkv_bias bool optional defaults to true whether to add a bias to the queries keys and values frequency_stride int optional defaults to 10 frequency stride to use when patchifying the spectrograms time_stride int optional defaults to 10 temporal stride to use when patchifying the spectrograms max_length int optional defaults to 1024 temporal dimension of the spectrograms num_mel_bins int optional defaults to 128 frequency dimension of the spectrograms number of mel frequency bins example python from transformers import astconfig astmodel initializing a ast mit ast finetuned audioset 10 10 0 4593 style configuration configuration astconfig initializing a model with random weights from the mit ast finetuned audioset 10 10 0 4593 style configuration model astmodel configuration accessing the model configuration configuration model config
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "MIT/ast-finetuned-audioset-10-10-0.4593": (
        "https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
    ),
}


class ASTConfig(PretrainedConfig):
    r

    model_type = "audio-spectrogram-transformer"

    def __init__(
        self,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        patch_size=16,
        qkv_bias=True,
        frequency_stride=10,
        time_stride=10,
        max_length=1024,
        num_mel_bins=128,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.patch_size = patch_size
        self.qkv_bias = qkv_bias
        self.frequency_stride = frequency_stride
        self.time_stride = time_stride
        self.max_length = max_length
        self.num_mel_bins = num_mel_bins