66 lines
3.8 KiB
Markdown
66 lines
3.8 KiB
Markdown
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# EnCodec
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## Overview
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The EnCodec neural codec model was proposed in [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438) by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.
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The abstract from the paper is the following:
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*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.*
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This model was contributed by [Matthijs](https://huggingface.co/Matthijs), [Patrick Von Platen](https://huggingface.co/patrickvonplaten) and [Arthur Zucker](https://huggingface.co/ArthurZ).
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The original code can be found [here](https://github.com/facebookresearch/encodec).
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## Usage example
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Here is a quick example of how to encode and decode an audio using this model:
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```python
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>>> from datasets import load_dataset, Audio
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>>> from transformers import EncodecModel, AutoProcessor
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>>> librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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>>> model = EncodecModel.from_pretrained("facebook/encodec_24khz")
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>>> processor = AutoProcessor.from_pretrained("facebook/encodec_24khz")
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>>> librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate))
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>>> audio_sample = librispeech_dummy[-1]["audio"]["array"]
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>>> inputs = processor(raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors="pt")
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>>> encoder_outputs = model.encode(inputs["input_values"], inputs["padding_mask"])
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>>> audio_values = model.decode(encoder_outputs.audio_codes, encoder_outputs.audio_scales, inputs["padding_mask"])[0]
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>>> # or the equivalent with a forward pass
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>>> audio_values = model(inputs["input_values"], inputs["padding_mask"]).audio_values
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```
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## EncodecConfig
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[[autodoc]] EncodecConfig
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## EncodecFeatureExtractor
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[[autodoc]] EncodecFeatureExtractor
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- __call__
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## EncodecModel
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[[autodoc]] EncodecModel
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- decode
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- encode
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- forward
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