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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 Video

[PE Audio Video](https://huggingface.co/papers/2504.13181) is the joint audio–video branch of Meta's Perception Encoder family. It encodes audio and video streams together with a shared text tower, producing contrastive embeddings for every pairwise combination, audio-text, video-text, audio-video, and audio+text-video, from a single forward pass.

Internally the model aligns the video feature sequence to the audio's temporal resolution via nearest-neighbor interpolation, so clips with different frame rates from sample rates stay in lockstep. The text encoder weights are tied across the audio and video branches.

You can find all the official PE Audio Video 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, PeAudioVideoModel
from transformers.video_utils import load_video

processor = AutoProcessor.from_pretrained("facebook/pe-av-large")
model = PeAudioVideoModel.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"]
video, _ = load_video("https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4")
labels = ["a person playing tennis with background crowd", "a dog barking in a park"]

audio_inputs = processor.feature_extractor(audio, sampling_rate=48_000, return_tensors="pt").to(model.device)
video_inputs = processor.video_processor(video, num_frames=16, return_tensors="pt").to(model.device)
text_inputs = processor.tokenizer(labels, padding=True, return_tensors="pt").to(model.device)
inputs = {**audio_inputs, **video_inputs, **text_inputs}

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

print("audio-text:", outputs.logits_audio_text.sigmoid().tolist())
print("video-text:", outputs.logits_video_text.sigmoid().tolist())
print("audio-video:", outputs.logits_audio_video.sigmoid().tolist())
```

## Usage tips and notes

- [`PeAudioVideoModel`] requires at least two of `input_ids`, `input_values`, `pixel_values_videos` — if only two are provided it dispatches to the audio-only or video-only sub-model. Passing all three triggers the joint audio-video-text path and the full set of logit matrices in [`PeAudioVideoOutput`].
- Audio uses `padding_mask` and video uses `padding_mask_videos` simultaneously. They are independent masks; do not conflate them with `attention_mask`, which is reserved for the text tower.
- Audio–video alignment runs per-batch-element inside `_align_video_hidden_state`, so batches with very different audio/video lengths iterate rather than vectorizing. Keep batch items roughly balanced for throughput.
- The text tower's weights are tied across branches via `_tied_weights_keys` — do not try to load separate text encoders for the audio and video halves.

## PeAudioVideoConfig

[[autodoc]] PeAudioVideoConfig

## PeAudioVideoEncoderConfig

[[autodoc]] PeAudioVideoEncoderConfig

## PeAudioVideoProcessor

[[autodoc]] PeAudioVideoProcessor

## PeAudioVideoEncoder

[[autodoc]] PeAudioVideoEncoder
    - forward

## PeAudioVideoModel

[[autodoc]] PeAudioVideoModel
    - forward
