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/
transformers_4_35_0
/models
/audio_spectrogram_transformer
/configuration_audio_spectrogram_transformer.py
| # coding=utf-8 | |
| # Copyright 2022 Google AI and 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. | |
| """ Audio Spectogram Transformer (AST) model configuration""" | |
| 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""" | |
| 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 | |
| ```""" | |
| 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 | |