#
CLAP

##
Overview

CLAP モデルは、Large Scale Contrastive Language-Audio pretraining with feature fusion and keyword-to-caption augmentation、Yusong Wu、Ke Chen、Tianyu Zhang、Yuchen Hui、Taylor Berg-Kirkpatrick、Shlomo Dubnov 著。

CLAP (Contrastive Language-Audio Pretraining) は、さまざまな (音声、テキスト) ペアでトレーニングされたニューラル ネットワークです。タスクに合わせて直接最適化することなく、音声が与えられた場合に最も関連性の高いテキスト スニペットを予測するように指示できます。 CLAP モデルは、SWINTransformer を使用して log-Mel スペクトログラム入力からオーディオ特徴を取得し、RoBERTa モデルを使用してテキスト特徴を取得します。次に、テキストとオーディオの両方の特徴が、同じ次元の潜在空間に投影されます。投影されたオーディオとテキストの特徴の間のドット積が、同様のスコアとして使用されます。

論文の要約は次のとおりです。

対照学習は、マルチモーダル表現学習の分野で目覚ましい成功を収めています。この論文では、音声データと自然言語記述を組み合わせて音声表現を開発する、対照的な言語音声事前トレーニングのパイプラインを提案します。この目標を達成するために、私たちはまず、さまざまなデータ ソースからの 633,526 個の音声とテキストのペアの大規模なコレクションである LAION-Audio-630K をリリースします。次に、さまざまなオーディオ エンコーダとテキスト エンコーダを考慮して、対照的な言語とオーディオの事前トレーニング モデルを構築します。機能融合メカニズムとキーワードからキャプションへの拡張をモデル設計に組み込んで、モデルが可変長の音声入力を処理できるようにし、パフォーマンスを向上させます。 3 番目に、包括的な実験を実行して、テキストから音声への取得、ゼロショット音声分類、教師付き音声分類の 3 つのタスクにわたってモデルを評価します。結果は、私たちのモデルがテキストから音声への検索タスクにおいて優れたパフォーマンスを達成していることを示しています。オーディオ分類タスクでは、モデルはゼロショット設定で最先端のパフォーマンスを達成し、非ゼロショット設定でもモデルの結果に匹敵するパフォーマンスを得ることができます。 LAION-オーディオ-6

このモデルは、Younes Belkada および Arthur Zucker によって提供されました。 元のコードは こちら にあります。

##
ClapConfig

###
class transformers.ClapConfig

< source >

( text_config = None audio_config = None logit_scale_init_value = 14.285714285714285 projection_dim = 512 projection_hidden_act = 'relu' initializer_factor = 1.0 **kwargs )

Parameters

- 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.

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 architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> 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)

####
from_text_audio_configs

< source >

( text_config: ClapTextConfig audio_config: ClapAudioConfig **kwargs ) →
ClapConfig

Returns

ClapConfig

An instance of a configuration object

Instantiate a ClapConfig (or a derived class) from clap text model configuration and clap audio model configuration.

##
ClapTextConfig

###
class transformers.ClapTextConfig

< source >

( 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 )

Parameters

- 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.). For more information on `"relative_key_query"`, please refer to Method 4 in Improve Transformer Models with Better Relative Position Embeddings (Huang et al.).
- 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`.

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 architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Examples:

    >>> 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

##
ClapAudioConfig

###
class transformers.ClapAudioConfig

< source >

( 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-05 initializer_factor = 1.0 **kwargs )

Parameters

- 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).

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 architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> 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

##
ClapFeatureExtractor

###
class transformers.ClapFeatureExtractor

< source >

( feature_size = 64 sampling_rate = 48000 hop_length = 480 max_length_s = 10 fft_window_size = 1024 padding_value = 0.0 return_attention_mask = False frequency_min: float = 0 frequency_max: float = 14000 top_db: typing.Optional[int] = None truncation: str = 'fusion' padding: str = 'repeatpad' **kwargs )

Parameters

- feature_size (`int`, optional, defaults to 64) — The feature dimension of the extracted Mel spectrograms. This corresponds to the number of mel filters (`n_mels`).
- sampling_rate (`int`, optional, defaults to 48000) — The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). This only serves to warn users if the audio fed to the feature extractor does not have the same sampling rate.
- hop_length (`int`,optional, defaults to 480) — Length of the overlapping windows for the STFT used to obtain the Mel Spectrogram. The audio will be split in smaller `frames` with a step of `hop_length` between each frame.
- max_length_s (`int`, optional, defaults to 10) — The maximum input length of the model in seconds. This is used to pad the audio.
- fft_window_size (`int`, optional, defaults to 1024) — Size of the window (in samples) on which the Fourier transform is applied. This controls the frequency resolution of the spectrogram. 400 means that the fourier transform is computed on windows of 400 samples.
- padding_value (`float`, optional, defaults to 0.0) — Padding value used to pad the audio. Should correspond to silences.
- return_attention_mask (`bool`, optional, defaults to `False`) — Whether or not the model should return the attention masks corresponding to the input.
- frequency_min (`float`, optional, defaults to 0) — The lowest frequency of interest. The STFT will not be computed for values below this.
- frequency_max (`float`, optional, defaults to 14000) — The highest frequency of interest. The STFT will not be computed for values above this.
- top_db (`float`, optional) — The highest decibel value used to convert the mel spectrogram to the log scale. For more details see the `audio_utils.power_to_db` function
- truncation (`str`, optional, defaults to `"fusion"`) — Truncation pattern for long audio inputs. Two patterns are available:
  - `fusion` will use `_random_mel_fusion`, which stacks 3 random crops from the mel spectrogram and a downsampled version of the entire mel spectrogram. If `config.fusion` is set to True, shorter audios also need to to return 4 mels, which will just be a copy of the original mel obtained from the padded audio.
  - `rand_trunc` will select a random crop of the mel spectrogram.
- padding (`str`, optional, defaults to `"repeatpad"`) — Padding pattern for shorter audio inputs. Three patterns were originally implemented:
  - `repeatpad`: the audio is repeated, and then padded to fit the `max_length`.
  - `repeat`: the audio is repeated and then cut to fit the `max_length`
  - `pad`: the audio is padded.

Constructs a CLAP feature extractor.

This feature extractor inherits from SequenceFeatureExtractor which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

This class extracts mel-filter bank features from raw speech using a custom numpy implementation of the Short Time Fourier Transform (STFT) which should match pytorch’s `torch.stft` equivalent.

####
to_dict

< source >

( ) →
`Dict[str, Any]`

Returns

`Dict[str, Any]`

Dictionary of all the attributes that make up this configuration instance, except for the mel filter banks, which do not need to be saved or printed as they are too long.

Serializes this instance to a Python dictionary.

##
ClapProcessor

###
class transformers.ClapProcessor

< source >

( feature_extractor tokenizer )

Parameters

- feature_extractor (ClapFeatureExtractor) — The audio processor is a required input.
- tokenizer (`RobertaTokenizerFast`) — The tokenizer is a required input.

Constructs a CLAP processor which wraps a CLAP feature extractor and a RoBerta tokenizer into a single processor.

ClapProcessor offers all the functionalities of ClapFeatureExtractor and `RobertaTokenizerFast`. See the `__call__()` and decode() for more information.

####
batch_decode

< source >

( *args **kwargs )

This method forwards all its arguments to RobertaTokenizerFast’s batch_decode(). Please refer to the docstring of this method for more information.

####
decode

< source >

( *args **kwargs )

This method forwards all its arguments to RobertaTokenizerFast’s decode(). Please refer to the docstring of this method for more information.

##
ClapModel

###
class transformers.ClapModel

< source >

( config: ClapConfig )

Parameters

- config (ClapConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The bare Clap Model outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.LongTensor] = None input_features: typing.Optional[torch.FloatTensor] = None is_longer: typing.Optional[torch.BoolTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None return_loss: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
`transformers.models.clap.modeling_clap.ClapOutput` or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- input_features (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Input audio features. This should be returned by the ClapFeatureExtractor class that you can also retrieve from AutoFeatureExtractor. See `ClapFeatureExtractor.__call__()` for details.
- is_longer (`torch.BoolTensor`, of shape `(batch_size, 1)`, optional) — Whether the audio clip is longer than `max_length`. If `True`, a feature fusion will be enabled to enhance the features.
- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  What are position IDs?

- return_loss (`bool`, optional) — Whether or not to return the contrastive loss.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

`transformers.models.clap.modeling_clap.ClapOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.clap.modeling_clap.ClapOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (ClapConfig) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `return_loss` is `True`) — Contrastive loss for audio-text similarity.
- logits_per_audio (`torch.FloatTensor` of shape `(audio_batch_size, text_batch_size)`) — The scaled dot product scores between `audio_embeds` and `text_embeds`. This represents the audio-text similarity scores.
- logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, audio_batch_size)`) — The scaled dot product scores between `text_embeds` and `audio_embeds`. This represents the text-audio similarity scores.
- text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`) — The text embeddings obtained by applying the projection layer to the pooled output of ClapTextModel.
- audio_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`) — The audio embeddings obtained by applying the projection layer to the pooled output of ClapAudioModel.
- text_model_output (`BaseModelOutputWithPooling`) — The output of the ClapTextModel.
- audio_model_output (`BaseModelOutputWithPooling`) — The output of the ClapAudioModel.

The ClapModel forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

    >>> from datasets import load_dataset
    >>> from transformers import AutoProcessor, ClapModel

    >>> dataset = load_dataset("hf-internal-testing/ashraq-esc50-1-dog-example")
    >>> audio_sample = dataset["train"]["audio"][0]["array"]

    >>> model = ClapModel.from_pretrained("laion/clap-htsat-unfused")
    >>> processor = AutoProcessor.from_pretrained("laion/clap-htsat-unfused")

    >>> input_text = ["Sound of a dog", "Sound of vaccum cleaner"]

    >>> inputs = processor(text=input_text, audios=audio_sample, return_tensors="pt", padding=True)

    >>> outputs = model(**inputs)
    >>> logits_per_audio = outputs.logits_per_audio  # this is the audio-text similarity score
    >>> probs = logits_per_audio.softmax(dim=-1)  # we can take the softmax to get the label probabilities

####
get_text_features

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`)

Parameters

- input_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- position_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  What are position IDs?

- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`)

The text embeddings obtained by applying the projection layer to the pooled output of ClapTextModel.

Examples:

    >>> from transformers import AutoTokenizer, ClapModel

    >>> model = ClapModel.from_pretrained("laion/clap-htsat-unfused")
    >>> tokenizer = AutoTokenizer.from_pretrained("laion/clap-htsat-unfused")

    >>> inputs = tokenizer(["the sound of a cat", "the sound of a dog"], padding=True, return_tensors="pt")
    >>> text_features = model.get_text_features(**inputs)

####
get_audio_features

< source >

( input_features: typing.Optional[torch.Tensor] = None is_longer: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
audio_features (`torch.FloatTensor` of shape `(batch_size, output_dim`)

Parameters

- input_features (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`) — Input audio features. This should be returned by the ClapFeatureExtractor class that you can also retrieve from AutoFeatureExtractor. See `ClapFeatureExtractor.__call__()` for details.
- is_longer (`torch.Tensor`, of shape `(batch_size, 1)`, optional) — Whether the audio clip is longer than `max_length`. If `True`, a feature fusion will be enabled to enhance the features.
- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

audio_features (`torch.FloatTensor` of shape `(batch_size, output_dim`)

The audio embeddings obtained by applying the projection layer to the pooled output of ClapAudioModel.

Examples:

    >>> from transformers import AutoFeatureExtractor, ClapModel
    >>> import torch

    >>> model = ClapModel.from_pretrained("laion/clap-htsat-unfused")
    >>> feature_extractor = AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused")
    >>> random_audio = torch.rand((16_000))
    >>> inputs = feature_extractor(random_audio, return_tensors="pt")
    >>> audio_features = model.get_audio_features(**inputs)

##
ClapTextModel

###
class transformers.ClapTextModel

< source >

( config add_pooling_layer = True )

Parameters

- config (ClapTextModel) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
- add_pooling_layer (`bool`, optional, defaults to `True`) — Whether to add a pooling layer

The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in Attention is all you need_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.

To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.

.. _Attention is all you need: https://arxiv.org/abs/1706.03762

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None encoder_hidden_states: typing.Optional[torch.Tensor] = None encoder_attention_mask: typing.Optional[torch.Tensor] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- token_type_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- position_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  What are position IDs?

- head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model’s internal embedding lookup matrix.
- encoder_hidden_states (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.
- encoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.
- past_key_values (`List[torch.FloatTensor]`, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Two formats are allowed:

  - a `Cache` instance, see our kv cache guide;
  - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format.

  The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned.

  If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`.

- use_cache (`bool`, optional) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (ClapConfig) and inputs.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.

- pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) — Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

- cross_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

- past_key_values (`Cache`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — It is a `Cache` instance. For more details, see our kv cache guide.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

The ClapTextModel forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

##
ClapTextModelWithProjection

###
class transformers.ClapTextModelWithProjection

< source >

( config: ClapTextConfig )

Parameters

- config (ClapTextConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The Clap Model with a projection layer on top (a linear layer on top of the pooled output).

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
`transformers.models.clap.modeling_clap.ClapTextModelOutput` or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- position_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  What are position IDs?

- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

`transformers.models.clap.modeling_clap.ClapTextModelOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.clap.modeling_clap.ClapTextModelOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (ClapConfig) and inputs.

- text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` optional returned when model is initialized with `with_projection=True`) — The text embeddings obtained by applying the projection layer to the pooler_output.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The ClapTextModelWithProjection forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

    >>> from transformers import AutoTokenizer, ClapTextModelWithProjection

    >>> model = ClapTextModelWithProjection.from_pretrained("laion/clap-htsat-unfused")
    >>> tokenizer = AutoTokenizer.from_pretrained("laion/clap-htsat-unfused")

    >>> inputs = tokenizer(["a sound of a cat", "a sound of a dog"], padding=True, return_tensors="pt")

    >>> outputs = model(**inputs)
    >>> text_embeds = outputs.text_embeds

##
ClapAudioModel

###
class transformers.ClapAudioModel

< source >

( config: ClapAudioConfig )

####
forward

< source >

( input_features: typing.Optional[torch.FloatTensor] = None is_longer: typing.Optional[torch.BoolTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
transformers.modeling_outputs.BaseModelOutputWithPooling or `tuple(torch.FloatTensor)`

Parameters

- input_features (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Input audio features. This should be returned by the ClapFeatureExtractor class that you can also retrieve from AutoFeatureExtractor. See `ClapFeatureExtractor.__call__()` for details.
- is_longer (`torch.BoolTensor`, of shape `(batch_size, 1)`, optional) — Whether the audio clip is longer than `max_length`. If `True`, a feature fusion will be enabled to enhance the features.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_outputs.BaseModelOutputWithPooling or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.BaseModelOutputWithPooling or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (ClapConfig) and inputs.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.

- pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) — Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The ClapAudioModel forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

    >>> from datasets import load_dataset
    >>> from transformers import AutoProcessor, ClapAudioModel

    >>> dataset = load_dataset("hf-internal-testing/ashraq-esc50-1-dog-example")
    >>> audio_sample = dataset["train"]["audio"][0]["array"]

    >>> model = ClapAudioModel.from_pretrained("laion/clap-htsat-fused")
    >>> processor = AutoProcessor.from_pretrained("laion/clap-htsat-fused")

    >>> inputs = processor(audios=audio_sample, return_tensors="pt")

    >>> outputs = model(**inputs)
    >>> last_hidden_state = outputs.last_hidden_state

##
ClapAudioModelWithProjection

###
class transformers.ClapAudioModelWithProjection

< source >

( config: ClapAudioConfig )

Parameters

- config (ClapAudioConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The Clap Model with a projection layer on top (a linear layer on top of the pooled output).

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_features: typing.Optional[torch.FloatTensor] = None is_longer: typing.Optional[torch.BoolTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
`transformers.models.clap.modeling_clap.ClapAudioModelOutput` or `tuple(torch.FloatTensor)`

Parameters

- input_features (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Input audio features. This should be returned by the ClapFeatureExtractor class that you can also retrieve from AutoFeatureExtractor. See `ClapFeatureExtractor.__call__()` for details.
- is_longer (`torch.BoolTensor`, of shape `(batch_size, 1)`, optional) — Whether the audio clip is longer than `max_length`. If `True`, a feature fusion will be enabled to enhance the features.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

`transformers.models.clap.modeling_clap.ClapAudioModelOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.clap.modeling_clap.ClapAudioModelOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (ClapConfig) and inputs.

- audio_embeds (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) — The Audio embeddings obtained by applying the projection layer to the pooler_output.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

The ClapAudioModelWithProjection forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

    >>> from datasets import load_dataset
    >>> from transformers import ClapAudioModelWithProjection, ClapProcessor

    >>> model = ClapAudioModelWithProjection.from_pretrained("laion/clap-htsat-fused")
    >>> processor = ClapProcessor.from_pretrained("laion/clap-htsat-fused")

    >>> dataset = load_dataset("hf-internal-testing/ashraq-esc50-1-dog-example")
    >>> audio_sample = dataset["train"]["audio"][0]["array"]

    >>> inputs = processor(audios=audio_sample, return_tensors="pt")
    >>> outputs = model(**inputs)
    >>> audio_embeds = outputs.audio_embeds

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