| EnCodec | |
| 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: | |
| thon | |
| from datasets import load_dataset, Audio | |
| from transformers import EncodecModel, AutoProcessor | |
| librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") | |
| model = EncodecModel.from_pretrained("facebook/encodec_24khz") | |
| 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") | |
| encoder_outputs = model.encode(inputs["input_values"], inputs["padding_mask"]) | |
| audio_values = model.decode(encoder_outputs.audio_codes, encoder_outputs.audio_scales, 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 |