This model was released on {release_date} and added to Hugging Face Transformers on 2025-12-16.

PE Audio (Perception Encoder Audio)

Overview

PE Audio (Perception Encoder Audio) is a state-of-the-art multimodal model that embeds audio and text into a shared (joint) embedding space. The model enables cross-modal retrieval and understanding between audio and text.

Text input

Produces a single embedding representing the full text.

Audio input

PeAudioFrameLevelModel
- Produces a sequence of embeddings, one every 40 ms of audio.
- Suitable for audio event localization and fine-grained temporal analysis.
PeAudioModel
- Produces a single embedding for the entire audio clip.
- Suitable for global audio-text retrieval tasks.

The resulting embeddings can be used for:

Audio event localization
Cross-modal (audio–text) retrieval and matching

Usage

Basic usage

TODO

PeAudioFeatureExtractor

class transformers.PeAudioFeatureExtractor

< source >

( feature_size: int = 1 sampling_rate: int = 48000 padding_value: float = 0.0 hop_length: int = 1920 **kwargs )

Parameters

feature_size (int, optional, defaults to 1) — The feature dimension of the extracted features. Use 1 for mono, 2 for stereo.
sampling_rate (int, optional, defaults to 48000) — The sampling rate at which the audio waveform should be digitalized, expressed in hertz (Hz).
padding_value (float, optional, defaults to 0.0) — The value that is used for padding.
hop_length (int, optional, defaults to 1920) — Overlap length between successive windows.

Constructs a PeAudioFeatureExtractor 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.

call

< source >

PeAudioProcessor

class transformers.PeAudioProcessor

< source >

( *args **kwargs )

call

< source >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None text: str | list[str] | list[list[str]] | None = None videos: typing.Union[list['PIL.Image.Image'], numpy.ndarray, ForwardRef('torch.Tensor'), list[numpy.ndarray], list['torch.Tensor'], list[list['PIL.Image.Image']], list[list[numpy.ndarray]], list[list['torch.Tensor']], transformers.video_utils.URL, list[transformers.video_utils.URL], list[list[transformers.video_utils.URL]], transformers.video_utils.Path, list[transformers.video_utils.Path], list[list[transformers.video_utils.Path]], NoneType] = None audio: typing.Union[numpy.ndarray, ForwardRef('torch.Tensor'), collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence['torch.Tensor'], NoneType] = None **kwargs: typing_extensions.Unpack[transformers.processing_utils.ProcessingKwargs] ) → BatchFeature

Parameters

images (PIL.Image.Image, np.ndarray, torch.Tensor, list[PIL.Image.Image], list[np.ndarray], list[torch.Tensor]) — The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. Both channels-first and channels-last formats are supported.
text (TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput], optional) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
videos (np.ndarray, torch.Tensor, List[np.ndarray], List[torch.Tensor]) — The video or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
audio (np.ndarray, torch.Tensor, list[np.ndarray], list[torch.Tensor]) — The audio or batch of audio to be prepared. Each audio can be a NumPy array or PyTorch tensor.
return_tensors (str or TensorType, optional) — If set, will return tensors of a particular framework. Acceptable values are:
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return NumPy np.ndarray objects.

Returns

BatchFeature

A BatchFeature object with processed inputs in a dict format.

Main method to prepare for model inputs. This method forwards the each modality argument to its own processor along with kwargs. Please refer to the docstring of the each processor attributes for more information.

PeAudioConfig

class transformers.PeAudioConfig

< source >

Parameters

text_config (Union[dict, ~configuration_utils.PreTrainedConfig], optional) — The config object or dictionary of the text backbone.
audio_config (Union[dict, ~configuration_utils.PreTrainedConfig], optional) — The config object or dictionary of the audio backbone.

This is the configuration class to store the configuration of a PeAudioModel. It is used to instantiate a Pe Audio 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 facebook/pe-av-large

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 PeAudioModel, PeAudioConfig

>>> # Initializing a PeAudioModel style configuration
>>> configuration = PeAudioConfig()

>>> # Initializing a model from the pe-av-large style configuration
>>> model = PeAudioModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

PeAudioEncoderConfig

class transformers.PeAudioEncoderConfig

< source >

( transformers_version: str | None = None architectures: list[str] | None = None output_hidden_states: bool | None = False return_dict: bool | None = True dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None chunk_size_feed_forward: int = 0 is_encoder_decoder: bool = False id2label: dict[int, str] | dict[str, str] | None = None label2id: dict[str, int] | dict[str, str] | None = None problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None dac_config: dict | transformers.configuration_utils.PreTrainedConfig | None = None hidden_size: int = 1792 intermediate_size: int = 4800 num_hidden_layers: int = 6 num_attention_heads: int = 14 num_key_value_heads: int | None = None head_dim: int = 128 hidden_act: str = 'silu' max_position_embeddings: int = 10000 initializer_range: float = 0.02 rms_norm_eps: float = 1e-05 rope_parameters: transformers.modeling_rope_utils.RopeParameters | dict | None = None attention_bias: bool = False attention_dropout: float | int = 0.0 )

Parameters

dac_config (Union[PreTrainedConfig, dict], optional) — Configuration for the DAC audio encoder used to tokenize the raw audio inputs. If a dictionary is passed, it will be used to instantiate a DacConfig with default DAC hyperparameters.
hidden_size (int, optional, defaults to 1792) — Dimension of the hidden representations.
intermediate_size (int, optional, defaults to 4800) — Dimension of the MLP representations.
num_hidden_layers (int, optional, defaults to 6) — Number of hidden layers in the Transformer decoder.
num_attention_heads (int, optional, defaults to 14) — Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (int, optional) — This is the number of key_value heads that should be used to implement Grouped Query Attention. If num_key_value_heads=num_attention_heads, the model will use Multi Head Attention (MHA), if num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out this paper. If it is not specified, will default to num_attention_heads.
head_dim (int, optional, defaults to 128) — The attention head dimension. If None, it will default to hidden_size // num_attention_heads
hidden_act (str, optional, defaults to silu) — The non-linear activation function (function or string) in the decoder. For example, "gelu", "relu", "silu", etc.
max_position_embeddings (int, optional, defaults to 10000) — The maximum sequence length that this model might ever be used with.
initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (float, optional, defaults to 1e-05) — The epsilon used by the rms normalization layers.
rope_parameters (Union[~modeling_rope_utils.RopeParameters, dict], optional) — Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for rope_theta and optionally parameters used for scaling in case you want to use RoPE with longer max_position_embeddings.
attention_bias (bool, optional, defaults to False) — Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (Union[float, int], optional, defaults to 0.0) — The dropout ratio for the attention probabilities.

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

>>> from transformers import PeAudioEncoder, PeAudioEncoderConfig

>>> # Initializing a PeAudioEncoder style configuration
>>> configuration = PeAudioEncoderConfig()

>>> # Initializing a model from the pe-av-large style configuration
>>> model = PeAudioEncoder(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

PeAudioEncoder

class transformers.PeAudioEncoder

< source >

( config: PeAudioEncoderConfig )

Parameters

config (PeAudioEncoderConfig) — 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 PeAudio Encoder model.

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_values: Tensor padding_mask: torch.Tensor | None = None **kwargs )

PeAudioFrameLevelModel

class transformers.PeAudioFrameLevelModel

< source >

( config: PeAudioConfig )

forward

< source >

( input_ids: Tensor input_values: Tensor attention_mask: torch.Tensor | None = None padding_mask: torch.Tensor | None = None return_loss: bool | None = None **kwargs )

PeAudioModel

class transformers.PeAudioModel

< source >

( config: PeAudioConfig )

forward

< source >

( input_ids: Tensor input_values: Tensor attention_mask: torch.Tensor | None = None padding_mask: torch.Tensor | None = None return_loss: bool | None = None **kwargs )

Update on GitHub

Transformers

PE Audio (Perception Encoder Audio)

Overview

Usage

Basic usage

PeAudioFeatureExtractor

class transformers.PeAudioFeatureExtractor

__call__

PeAudioProcessor

class transformers.PeAudioProcessor

__call__

PeAudioConfig

class transformers.PeAudioConfig

PeAudioEncoderConfig

class transformers.PeAudioEncoderConfig

PeAudioEncoder

class transformers.PeAudioEncoder

forward

PeAudioFrameLevelModel

class transformers.PeAudioFrameLevelModel

forward

PeAudioModel

class transformers.PeAudioModel

forward

call

call