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+<<<<<<< Updated upstream
+*This model was released on 2025-04-17 and added to Hugging Face Transformers on 2025-12-16.*
+=======
+*This model was published in HF papers on 2025-04-17 and contributed to Hugging Face Transformers on 2025-12-16.*
+>>>>>>> Stashed changes
+
+# 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