# coding=utf-8 # Copyright 2023 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. """CLAP model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class ClapTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ClapTextModel`]. It is used to instantiate a CLAP 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 CLAP [calp-hsat-fused](https://huggingface.co/laion/clap-hsat-fused) 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 30522): Vocabulary size of the CLAP model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ClapTextModel`]. 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" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `Callable`, *optional*, defaults to `"relu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"relu"`, `"relu"`, `"silu"` and `"relu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): 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). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`ClapTextModel`]. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. position_embedding_type (`str`, *optional*, defaults to `"absolute"`): Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). is_decoder (`bool`, *optional*, defaults to `False`): Whether the model is used as a decoder or not. If `False`, the model is used as an encoder. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. projection_hidden_act (`str`, *optional*, defaults to `"relu"`): The non-linear activation function (function or string) in the projection layer. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. projection_dim (`int`, *optional*, defaults to 512) Dimension of the projection head of the `ClapTextModelWithProjection`. Examples: ```python >>> from transformers import ClapTextConfig, ClapTextModel >>> # Initializing a CLAP text configuration >>> configuration = ClapTextConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = ClapTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "clap_text_model" base_config_key = "text_config" def __init__( self, vocab_size=50265, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=514, type_vocab_size=1, initializer_factor=1.0, layer_norm_eps=1e-12, projection_dim=512, pad_token_id=1, bos_token_id=0, eos_token_id=2, position_embedding_type="absolute", use_cache=True, projection_hidden_act="relu", **kwargs, ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_factor = initializer_factor self.layer_norm_eps = layer_norm_eps self.position_embedding_type = position_embedding_type self.use_cache = use_cache self.projection_hidden_act = projection_hidden_act self.projection_dim = projection_dim class ClapAudioConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ClapAudioModel`]. It is used to instantiate a CLAP audio encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the audio encoder of the CLAP [laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: window_size (`int`, *optional*, defaults to 8): Image size of the spectrogram num_mel_bins (`int`, *optional*, defaults to 64): Number of mel features used per frames. Should correspond to the value used in the `ClapProcessor` class. spec_size (`int`, *optional*, defaults to 256): Desired input size of the spectrogram that the model supports. It can be different from the output of the `ClapFeatureExtractor`, in which case the input features will be resized. Corresponds to the `image_size` of the audio models. hidden_act (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. patch_size (`int`, *optional*, defaults to 4): Patch size for the audio spectrogram patch_stride (`list`, *optional*, defaults to `[4, 4]`): Patch stride for the audio spectrogram num_classes (`int`, *optional*, defaults to 527): Number of classes used for the head training hidden_size (`int`, *optional*, defaults to 768): Hidden size of the output of the audio encoder. Correspond to the dimension of the penultimate layer's output,which is sent to the projection MLP layer. projection_dim (`int`, *optional*, defaults to 512): Hidden size of the projection layer. depths (`list`, *optional*, defaults to `[2, 2, 6, 2]`): Depths used for the Swin Layers of the audio model num_attention_heads (`list`, *optional*, defaults to `[4, 8, 16, 32]`): Number of attention heads used for the Swin Layers of the audio model enable_fusion (`bool`, *optional*, defaults to `False`): Whether or not to enable patch fusion. This is the main contribution of the authors, and should give the best results. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the encoder. fusion_type (`[type]`, *optional*): Fusion type used for the patch fusion. patch_embed_input_channels (`int`, *optional*, defaults to 1): Number of channels used for the input spectrogram flatten_patch_embeds (`bool`, *optional*, defaults to `True`): Whether or not to flatten the patch embeddings patch_embeds_hidden_size (`int`, *optional*, defaults to 96): Hidden size of the patch embeddings. It is used as the number of output channels. enable_patch_layer_norm (`bool`, *optional*, defaults to `True`): Whether or not to enable layer normalization for the patch embeddings drop_path_rate (`float`, *optional*, defaults to 0.0): Drop path rate for the patch fusion attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. qkv_bias (`bool`, *optional*, defaults to `True`): Whether or not to add a bias to the query, key, value projections. mlp_ratio (`float`, *optional*, defaults to 4.0): Ratio of the mlp hidden dim to embedding dim. aff_block_r (`int`, *optional*, defaults to 4): downsize_ratio used in the AudioFF block num_hidden_layers (`int`, *optional*, defaults to 4): Number of hidden layers in the Transformer encoder. projection_hidden_act (`str`, *optional*, defaults to `"relu"`): The non-linear activation function (function or string) in the projection layer. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. layer_norm_eps (`[type]`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). Example: ```python >>> from transformers import ClapAudioConfig, ClapAudioModel >>> # Initializing a ClapAudioConfig with laion/clap-htsat-fused style configuration >>> configuration = ClapAudioConfig() >>> # Initializing a ClapAudioModel (with random weights) from the laion/clap-htsat-fused style configuration >>> model = ClapAudioModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "clap_audio_model" base_config_key = "audio_config" def __init__( self, window_size=8, num_mel_bins=64, spec_size=256, hidden_act="gelu", patch_size=4, patch_stride=[4, 4], num_classes=527, hidden_size=768, projection_dim=512, depths=[2, 2, 6, 2], num_attention_heads=[4, 8, 16, 32], enable_fusion=False, hidden_dropout_prob=0.1, fusion_type=None, patch_embed_input_channels=1, flatten_patch_embeds=True, patch_embeds_hidden_size=96, enable_patch_layer_norm=True, drop_path_rate=0.0, attention_probs_dropout_prob=0.0, qkv_bias=True, mlp_ratio=4.0, aff_block_r=4, num_hidden_layers=4, projection_hidden_act="relu", layer_norm_eps=1e-5, initializer_factor=1.0, **kwargs, ): super().__init__(**kwargs) self.window_size = window_size self.num_mel_bins = num_mel_bins self.spec_size = spec_size self.patch_size = patch_size self.patch_stride = patch_stride self.num_classes = num_classes self.hidden_size = hidden_size self.depths = depths self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.window_size = window_size self.enable_fusion = enable_fusion self.fusion_type = fusion_type self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.projection_dim = projection_dim self.flatten_patch_embeds = flatten_patch_embeds self.patch_embeds_hidden_size = patch_embeds_hidden_size self.enable_patch_layer_norm = enable_patch_layer_norm self.drop_path_rate = drop_path_rate self.attention_probs_dropout_prob = attention_probs_dropout_prob self.qkv_bias = qkv_bias self.mlp_ratio = mlp_ratio self.patch_embed_input_channels = patch_embed_input_channels self.aff_block_r = aff_block_r self.layer_norm_eps = layer_norm_eps self.initializer_factor = initializer_factor self.projection_hidden_act = projection_hidden_act class ClapConfig(PretrainedConfig): r""" [`ClapConfig`] is the configuration class to store the configuration of a [`ClapModel`]. It is used to instantiate a CLAP model according to the specified arguments, defining the text model and audio model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the CLAP [laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`ClapTextConfig`]. audio_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`ClapAudioConfig`]. logit_scale_init_value (`float`, *optional*, defaults to 14.29): The initial value of the *logit_scale* parameter. Default is used as per the original CLAP implementation. projection_dim (`int`, *optional*, defaults to 512): Dimensionality of text and audio projection layers. projection_hidden_act (`str`, *optional*, defaults to `"relu"`): Activation function for the projection layers. initializer_factor (`float`, *optional*, defaults to 1.0): Factor to scale the initialization of the model weights. kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import ClapConfig, ClapModel >>> # Initializing a ClapConfig with laion-ai/base style configuration >>> configuration = ClapConfig() >>> # Initializing a ClapModel (with random weights) from the laion-ai/base style configuration >>> model = ClapModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a ClapConfig from a ClapTextConfig and a ClapAudioConfig >>> from transformers import ClapTextConfig, ClapAudioConfig >>> # Initializing a ClapText and ClapAudioConfig configuration >>> config_text = ClapTextConfig() >>> config_audio = ClapAudioConfig() >>> config = ClapConfig.from_text_audio_configs(config_text, config_audio) ```""" model_type = "clap" sub_configs = {"text_config": ClapTextConfig, "audio_config": ClapAudioConfig} def __init__( self, text_config=None, audio_config=None, logit_scale_init_value=(1 / 0.07), projection_dim=512, projection_hidden_act="relu", initializer_factor=1.0, **kwargs, ): super().__init__(**kwargs) if text_config is None: text_config = {} logger.info("text_config is None. Initializing the ClapTextConfig with default values.") if audio_config is None: audio_config = {} logger.info("audio_config is None. initializing the ClapAudioConfig with default values.") self.text_config = ClapTextConfig(**text_config) self.audio_config = ClapAudioConfig(**audio_config) self.text_config.projection_dim = projection_dim self.audio_config.projection_dim = projection_dim self.text_config.projection_hidden_act = projection_hidden_act self.audio_config.projection_hidden_act = projection_hidden_act self.projection_dim = projection_dim self.projection_hidden_act = projection_hidden_act self.hidden_size = self.text_config.hidden_size self.logit_scale_init_value = logit_scale_init_value self.initializer_factor = initializer_factor self.num_hidden_layers = self.text_config.num_hidden_layers + len(self.audio_config.depths) @classmethod def from_text_audio_configs(cls, text_config: ClapTextConfig, audio_config: ClapAudioConfig, **kwargs): r""" Instantiate a [`ClapConfig`] (or a derived class) from clap text model configuration and clap audio model configuration. Returns: [`ClapConfig`]: An instance of a configuration object """ return cls(text_config=text_config.to_dict(), audio_config=audio_config.to_dict(), **kwargs) __all__ = ["ClapAudioConfig", "ClapConfig", "ClapTextConfig"]