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""" CLAP model configuration""" |
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|
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import os |
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from typing import Union |
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|
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from ...configuration_utils import PretrainedConfig |
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from ...utils import logging |
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|
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logger = logging.get_logger(__name__) |
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CLAP_PRETRAINED_MODEL_ARCHIVE_LIST = { |
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"laion/clap-htsat-fused": "https://huggingface.co/laion/clap-htsat-fused/resolve/main/config.json", |
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"laion/clap-htsat-unfused": "https://huggingface.co/laion/clap-htsat-unfused/resolve/main/config.json", |
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} |
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class ClapTextConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a [`ClapTextModel`]. It is used to instantiate a CLAP |
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the |
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defaults will yield a similar configuration to that of the CLAP |
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[calp-hsat-fused](https://huggingface.co/laion/clap-hsat-fused) architecture. |
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|
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the |
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documentation from [`PretrainedConfig`] for more information. |
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Args: |
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vocab_size (`int`, *optional*, defaults to 30522): |
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Vocabulary size of the CLAP model. Defines the number of different tokens that can be represented by the |
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`inputs_ids` passed when calling [`ClapTextModel`]. |
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hidden_size (`int`, *optional*, defaults to 768): |
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Dimensionality of the encoder layers and the pooler layer. |
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num_hidden_layers (`int`, *optional*, defaults to 12): |
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Number of hidden layers in the Transformer encoder. |
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num_attention_heads (`int`, *optional*, defaults to 12): |
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Number of attention heads for each attention layer in the Transformer encoder. |
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intermediate_size (`int`, *optional*, defaults to 3072): |
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. |
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hidden_act (`str` or `Callable`, *optional*, defaults to `"relu"`): |
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"relu"`, |
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`"relu"`, `"silu"` and `"relu_new"` are supported. |
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1): |
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. |
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): |
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The dropout ratio for the attention probabilities. |
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max_position_embeddings (`int`, *optional*, defaults to 512): |
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The maximum sequence length that this model might ever be used with. Typically set this to something large |
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just in case (e.g., 512 or 1024 or 2048). |
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type_vocab_size (`int`, *optional*, defaults to 2): |
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The vocabulary size of the `token_type_ids` passed when calling [`ClapTextModel`]. |
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layer_norm_eps (`float`, *optional*, defaults to 1e-12): |
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The epsilon used by the layer normalization layers. |
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position_embedding_type (`str`, *optional*, defaults to `"absolute"`): |
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Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For |
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positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to |
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[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). |
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For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models |
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with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). |
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is_decoder (`bool`, *optional*, defaults to `False`): |
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Whether the model is used as a decoder or not. If `False`, the model is used as an encoder. |
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use_cache (`bool`, *optional*, defaults to `True`): |
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Whether or not the model should return the last key/values attentions (not used by all models). Only |
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relevant if `config.is_decoder=True`. |
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projection_hidden_act (`str`, *optional*, defaults to `"relu"`): |
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The non-linear activation function (function or string) in the projection layer. If string, `"gelu"`, |
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`"relu"`, `"silu"` and `"gelu_new"` are supported. |
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projection_dim (`int`, *optional*, defaults to 512) |
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Dimension of the projection head of the `ClapTextModelWithProjection`. |
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|
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Examples: |
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|
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```python |
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>>> from transformers import ClapTextConfig, ClapTextModel |
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|
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>>> # Initializing a CLAP text configuration |
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>>> configuration = ClapTextConfig() |
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|
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>>> # Initializing a model (with random weights) from the configuration |
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>>> model = ClapTextModel(configuration) |
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|
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>>> # Accessing the model configuration |
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>>> configuration = model.config |
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```""" |
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model_type = "clap_text_model" |
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|
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def __init__( |
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self, |
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vocab_size=50265, |
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hidden_size=768, |
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num_hidden_layers=12, |
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num_attention_heads=12, |
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intermediate_size=3072, |
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hidden_act="gelu", |
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hidden_dropout_prob=0.1, |
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attention_probs_dropout_prob=0.1, |
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max_position_embeddings=514, |
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type_vocab_size=1, |
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initializer_factor=1.0, |
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layer_norm_eps=1e-12, |
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projection_dim=512, |
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pad_token_id=1, |
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bos_token_id=0, |
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eos_token_id=2, |
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position_embedding_type="absolute", |
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use_cache=True, |
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projection_hidden_act="relu", |
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**kwargs, |
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): |
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) |
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self.vocab_size = vocab_size |
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self.hidden_size = hidden_size |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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self.hidden_act = hidden_act |
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self.intermediate_size = intermediate_size |
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self.hidden_dropout_prob = hidden_dropout_prob |
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self.attention_probs_dropout_prob = attention_probs_dropout_prob |
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self.max_position_embeddings = max_position_embeddings |
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self.type_vocab_size = type_vocab_size |
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self.initializer_factor = initializer_factor |
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self.layer_norm_eps = layer_norm_eps |
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self.position_embedding_type = position_embedding_type |
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self.use_cache = use_cache |
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self.projection_hidden_act = projection_hidden_act |
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self.projection_dim = projection_dim |
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|
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@classmethod |
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": |
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cls._set_token_in_kwargs(kwargs) |
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) |
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if config_dict.get("model_type") == "clap": |
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config_dict = config_dict["text_config"] |
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: |
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logger.warning( |
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " |
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." |
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) |
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return cls.from_dict(config_dict, **kwargs) |
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class ClapAudioConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a [`ClapAudioModel`]. It is used to instantiate a |
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CLAP audio encoder according to the specified arguments, defining the model architecture. Instantiating a |
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configuration with the defaults will yield a similar configuration to that of the audio encoder of the CLAP |
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[laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) architecture. |
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|
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the |
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documentation from [`PretrainedConfig`] for more information. |
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|
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Args: |
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window_size (`int`, *optional*, defaults to 8): |
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Image size of the spectrogram |
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num_mel_bins (`int`, *optional*, defaults to 64): |
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Number of mel features used per frames. Should correspond to the value used in the `ClapProcessor` class. |
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spec_size (`int`, *optional*, defaults to 256): |
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Desired input size of the spectrogram that the model supports. It can be different from the output of the |
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`ClapFeatureExtractor`, in which case the input features will be resized. Corresponds to the `image_size` |
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of the audio models. |
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hidden_act (`str`, *optional*, defaults to `"gelu"`): |
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, |
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`"relu"`, `"silu"` and `"gelu_new"` are supported. |
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patch_size (`int`, *optional*, defaults to 4): |
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Patch size for the audio spectrogram |
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patch_stride (`list`, *optional*, defaults to `[4, 4]`): |
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Patch stride for the audio spectrogram |
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num_classes (`int`, *optional*, defaults to 527): |
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Number of classes used for the head training |
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hidden_size (`int`, *optional*, defaults to 768): |
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Hidden size of the output of the audio encoder. Correspond to the dimension of the penultimate layer's |
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output,which is sent to the projection MLP layer. |
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projection_dim (`int`, *optional*, defaults to 512): |
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Hidden size of the projection layer. |
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depths (`list`, *optional*, defaults to `[2, 2, 6, 2]`): |
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Depths used for the Swin Layers of the audio model |
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num_attention_heads (`list`, *optional*, defaults to `[4, 8, 16, 32]`): |
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Number of attention heads used for the Swin Layers of the audio model |
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enable_fusion (`bool`, *optional*, defaults to `False`): |
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Whether or not to enable patch fusion. This is the main contribution of the authors, and should give the |
|
best results. |
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1): |
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The dropout probabilitiy for all fully connected layers in the encoder. |
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fusion_type (`[type]`, *optional*): |
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Fusion type used for the patch fusion. |
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patch_embed_input_channels (`int`, *optional*, defaults to 1): |
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Number of channels used for the input spectrogram |
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flatten_patch_embeds (`bool`, *optional*, defaults to `True`): |
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Whether or not to flatten the patch embeddings |
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patch_embeds_hidden_size (`int`, *optional*, defaults to 96): |
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Hidden size of the patch embeddings. It is used as the number of output channels. |
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enable_patch_layer_norm (`bool`, *optional*, defaults to `True`): |
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Whether or not to enable layer normalization for the patch embeddings |
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drop_path_rate (`float`, *optional*, defaults to 0.0): |
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Drop path rate for the patch fusion |
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): |
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The dropout ratio for the attention probabilities. |
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qkv_bias (`bool`, *optional*, defaults to `True`): |
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Whether or not to add a bias to the query, key, value projections. |
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mlp_ratio (`float`, *optional*, defaults to 4.0): |
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Ratio of the mlp hidden dim to embedding dim. |
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aff_block_r (`int`, *optional*, defaults to 4): |
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downsize_ratio used in the AudioFF block |
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num_hidden_layers (`int`, *optional*, defaults to 4): |
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Number of hidden layers in the Transformer encoder. |
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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): |
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The epsilon used by the layer normalization layers. |
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initializer_factor (`float`, *optional*, defaults to 1.0): |
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A factor for initializing all weight matrices (should be kept to 1, used internally for initialization |
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testing). |
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|
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Example: |
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|
|
```python |
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>>> from transformers import ClapAudioConfig, ClapAudioModel |
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|
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>>> # Initializing a ClapAudioConfig with laion/clap-htsat-fused style configuration |
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>>> configuration = ClapAudioConfig() |
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|
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>>> # Initializing a ClapAudioModel (with random weights) from the laion/clap-htsat-fused style configuration |
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>>> model = ClapAudioModel(configuration) |
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|
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>>> # Accessing the model configuration |
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>>> configuration = model.config |
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```""" |
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|
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model_type = "clap_audio_model" |
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|
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def __init__( |
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self, |
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window_size=8, |
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num_mel_bins=64, |
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spec_size=256, |
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hidden_act="gelu", |
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patch_size=4, |
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patch_stride=[4, 4], |
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num_classes=527, |
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hidden_size=768, |
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projection_dim=512, |
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depths=[2, 2, 6, 2], |
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num_attention_heads=[4, 8, 16, 32], |
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enable_fusion=False, |
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hidden_dropout_prob=0.1, |
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fusion_type=None, |
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patch_embed_input_channels=1, |
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flatten_patch_embeds=True, |
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patch_embeds_hidden_size=96, |
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enable_patch_layer_norm=True, |
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drop_path_rate=0.0, |
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attention_probs_dropout_prob=0.0, |
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qkv_bias=True, |
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mlp_ratio=4.0, |
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aff_block_r=4, |
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num_hidden_layers=4, |
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projection_hidden_act="relu", |
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layer_norm_eps=1e-5, |
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initializer_factor=1.0, |
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**kwargs, |
|
): |
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super().__init__(**kwargs) |
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self.window_size = window_size |
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self.num_mel_bins = num_mel_bins |
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self.spec_size = spec_size |
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self.patch_size = patch_size |
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self.patch_stride = patch_stride |
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self.num_classes = num_classes |
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self.hidden_size = hidden_size |
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self.depths = depths |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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self.window_size = window_size |
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self.enable_fusion = enable_fusion |
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self.fusion_type = fusion_type |
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self.hidden_act = hidden_act |
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self.hidden_dropout_prob = hidden_dropout_prob |
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self.projection_dim = projection_dim |
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self.flatten_patch_embeds = flatten_patch_embeds |
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self.patch_embeds_hidden_size = patch_embeds_hidden_size |
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self.enable_patch_layer_norm = enable_patch_layer_norm |
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self.drop_path_rate = drop_path_rate |
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self.attention_probs_dropout_prob = attention_probs_dropout_prob |
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self.qkv_bias = qkv_bias |
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self.mlp_ratio = mlp_ratio |
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self.patch_embed_input_channels = patch_embed_input_channels |
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self.aff_block_r = aff_block_r |
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self.layer_norm_eps = layer_norm_eps |
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self.initializer_factor = initializer_factor |
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self.projection_hidden_act = projection_hidden_act |
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|
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@classmethod |
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": |
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cls._set_token_in_kwargs(kwargs) |
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|
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) |
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|
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if config_dict.get("model_type") == "clap": |
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config_dict = config_dict["audio_config"] |
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|
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: |
|
logger.warning( |
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " |
|
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." |
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) |
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|
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return cls.from_dict(config_dict, **kwargs) |
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|
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class ClapConfig(PretrainedConfig): |
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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: |
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text_config (`dict`, *optional*): |
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Dictionary of configuration options used to initialize [`ClapTextConfig`]. |
|
audio_config (`dict`, *optional*): |
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Dictionary of configuration options used to initialize [`ClapAudioConfig`]. |
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logit_scale_init_value (`float`, *optional*, defaults to 14.29): |
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The inital value of the *logit_scale* paramter. Default is used as per the original CLAP implementation. |
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projection_dim (`int`, *optional*, defaults to 512): |
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Dimentionality of text and audio projection layers. |
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projection_hidden_act (`str`, *optional*, defaults to `"relu"`): |
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Activation function for the projection layers. |
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initializer_factor (`float`, *optional*, defaults to 1.0): |
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Factor to scale the initialization of the model weights. |
|
kwargs (*optional*): |
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Dictionary of keyword arguments. |
|
|
|
Example: |
|
|
|
```python |
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>>> from transformers import ClapConfig, ClapModel |
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|
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>>> # Initializing a ClapConfig with laion-ai/base style configuration |
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>>> configuration = ClapConfig() |
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|
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>>> # Initializing a ClapModel (with random weights) from the laion-ai/base style configuration |
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>>> model = ClapModel(configuration) |
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|
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>>> # Accessing the model configuration |
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>>> configuration = model.config |
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|
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>>> # We can also initialize a ClapConfig from a ClapTextConfig and a ClapAudioConfig |
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>>> from transformers import ClapTextConfig, ClapAudioConfig |
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|
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>>> # Initializing a ClapText and ClapAudioConfig configuration |
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>>> config_text = ClapTextConfig() |
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>>> config_audio = ClapAudioConfig() |
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|
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>>> config = ClapConfig.from_text_audio_configs(config_text, config_audio) |
|
```""" |
|
|
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model_type = "clap" |
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|
|
def __init__( |
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self, |
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text_config=None, |
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audio_config=None, |
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logit_scale_init_value=(1 / 0.07), |
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projection_dim=512, |
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projection_hidden_act="relu", |
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initializer_factor=1.0, |
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**kwargs, |
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): |
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super().__init__(**kwargs) |
|
|
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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.") |
|
|
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self.text_config = ClapTextConfig(**text_config) |
|
self.audio_config = ClapAudioConfig(**audio_config) |
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self.text_config.projection_dim = projection_dim |
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self.audio_config.projection_dim = projection_dim |
|
|
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self.text_config.projection_hidden_act = projection_hidden_act |
|
self.audio_config.projection_hidden_act = projection_hidden_act |
|
|
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self.projection_dim = projection_dim |
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self.projection_hidden_act = projection_hidden_act |
|
self.hidden_size = self.text_config.hidden_size |
|
|
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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) |
|
|
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@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) |
|
|