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EnCodec

docs/source/en/model_doc/encodec.md

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This model was released on 2022-10-24 and added to Hugging Face Transformers on 2023-06-14.

EnCodec

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Overview

The EnCodec neural codec model was proposed in High Fidelity Neural Audio Compression 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, Patrick Von Platen and Arthur Zucker. The original code can be found here.

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