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*This model was published in HF papers on 2025-04-17 and contributed to Hugging Face Transformers on 2025-12-16.*
*This model was released on 2025-04-17 and added to Hugging Face Transformers on 2025-12-16.*

# PE Audio

[PE Audio](https://huggingface.co/papers/2504.13181) is the audio branch of Meta's Perception Encoder family. It contrastively aligns raw waveforms with text into a shared embedding space, trained on paired audio–caption data for cross-modal retrieval and zero-shot audio classification.

Two heads are exposed on top of the same encoder. [`PeAudioModel`] returns one pooled embedding per clip for clip-level retrieval, while [`PeAudioFrameLevelModel`] returns one embedding every 40 ms for event localization and fine-grained temporal analysis.

You can find all the official PE Audio checkpoints under the [perception-encoder-audio-visual](https://huggingface.co/collections/facebook/perception-encoder-audio-visual) collection.

## Quickstart

```py
import torch
from datasets import load_dataset
from transformers import AutoProcessor, PeAudioModel

processor = AutoProcessor.from_pretrained("facebook/pe-av-large")
model = PeAudioModel.from_pretrained(
    "facebook/pe-av-large",
    device_map="auto",
)

ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
audio = ds[0]["audio"]["array"]
labels = ["a dog barking", "a person speaking", "music playing"]

audio_inputs = processor.feature_extractor(audio, sampling_rate=48_000, return_tensors="pt").to(model.device)
text_inputs = processor.tokenizer(labels, padding=True, return_tensors="pt").to(model.device)
inputs = {**audio_inputs, **text_inputs}

with torch.no_grad():
    outputs = model(**inputs)

probs = outputs.logits_audio_text.sigmoid()
print({label: p.item() for label, p in zip(labels, probs[0])})
```

## Usage tips and notes

- Audio must be mono (`feature_size=1`) and resampled to 48 kHz — the feature extractor warns but does not resample for you. Stereo input is not supported.
- Variable-length audio is handled with `padding_mask` (not the usual `attention_mask`). The mask is downsampled internally by `dac_config.hop_length` before it reaches the encoder, so pass the raw waveform-resolution mask that the feature extractor returns.
- [`PeAudioModel`] returns logits of shape `(n_audio, n_text)`. [`PeAudioFrameLevelModel`] returns `(n_audio, n_text, n_frames)` with one frame every 40 ms. Pick the class that matches the task — they share weights so swapping is cheap.
- The text tower is a shared encoder loaded via `AutoModel` from `config.text_config`. The tokenizer is attached to the processor via `AutoTokenizer`, not a dedicated class.

## PeAudioConfig

[[autodoc]] PeAudioConfig

## PeAudioEncoderConfig

[[autodoc]] PeAudioEncoderConfig

## PeAudioFeatureExtractor

[[autodoc]] PeAudioFeatureExtractor
    - __call__

## PeAudioProcessor

[[autodoc]] PeAudioProcessor

## PeAudioEncoder

[[autodoc]] PeAudioEncoder
    - forward

## PeAudioModel

[[autodoc]] PeAudioModel
    - forward

## PeAudioFrameLevelModel

[[autodoc]] PeAudioFrameLevelModel
    - forward
