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*This model was published in HF papers on 2022-10-24 and contributed to Hugging Face Transformers on 2023-06-14.*

# EnCodec


## Overview

The EnCodec neural codec model was proposed in [High Fidelity Neural Audio Compression](https://huggingface.co/papers/2210.13438) by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.

The abstract from the paper is the following:

*We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural networks. It consists in a streaming encoder-decoder architecture with quantized latent space trained in an end-to-end fashion. We simplify and speed-up the training by using a single multiscale spectrogram adversary that efficiently reduces artifacts and produce high-quality samples. We introduce a novel loss balancer mechanism to stabilize training: the weight of a loss now defines the fraction of the overall gradient it should represent, thus decoupling the choice of this hyper-parameter from the typical scale of the loss. Finally, we study how lightweight Transformer models can be used to further compress the obtained representation by up to 40%, while staying faster than real time. We provide a detailed description of the key design choices of the proposed model including: training objective, architectural changes and a study of various perceptual loss functions. We present an extensive subjective evaluation (MUSHRA tests) together with an ablation study for a range of bandwidths and audio domains, including speech, noisy-reverberant speech, and music. Our approach is superior to the baselines methods across all evaluated settings, considering both 24 kHz monophonic and 48 kHz stereophonic audio.*

This model was contributed by [Matthijs](https://huggingface.co/Matthijs), [Patrick Von Platen](https://huggingface.co/patrickvonplaten) and [Arthur Zucker](https://huggingface.co/ArthurZ).
The original code can be found [here](https://github.com/facebookresearch/encodec).

## Usage example

Here is a quick example of how to encode and decode an audio using this model:

```python
from datasets import Audio, load_dataset

from transformers import AutoProcessor, EncodecModel


librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")

model = EncodecModel.from_pretrained("facebook/encodec_24khz", device_map="auto")
processor = AutoProcessor.from_pretrained("facebook/encodec_24khz")
librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate))
audio_sample = librispeech_dummy[-1]["audio"]["array"]
inputs = processor(raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors="pt").to(model.device)

encoder_outputs = model.encode(inputs["input_values"], inputs["padding_mask"])
# `encoder_outputs.audio_codes` contains discrete codes
audio_values = model.decode(**encoder_outputs, padding_mask=inputs["padding_mask"])[0]
# or the equivalent with a forward pass
audio_values = model(inputs["input_values"], inputs["padding_mask"]).audio_values
```

## EncodecConfig

[[autodoc]] EncodecConfig

## EncodecFeatureExtractor

[[autodoc]] EncodecFeatureExtractor
    - __call__

## EncodecModel

[[autodoc]] EncodecModel
    - decode
    - encode
    - forward
