facebookresearch/encodec
Encodec
EnCodec is a neural audio codec and compression tool designed to transform raw audio waveforms into discrete codes and reconstruct them back into sound. It functions as a system for neural audio representation, converting continuous audio signals into sequences of integer indices for use in generative AI tasks.
The project utilizes a residual vector quantizer, which employs multiple layers of codebooks to represent audio signals with high precision at low bitrates. This approach allows the system to compress audio to discrete codes and perform low bitrate audio coding for efficient transmission and storage.
The framework covers the full pipeline of neural audio compression, including the extraction of discrete audio representations and the reconstruction of audio waveforms.
Features
- Neural Audio Compression - Provides high-fidelity neural compression of audio using discrete tokenizers for efficient storage.
- Audio Sample Reconstruction - Converts discrete compressed codes back into playable audio signals without distortion.
- Waveform Decoders - Converts discrete neural tokens back into high-fidelity audio waveforms.
- Discrete Audio Representations - Transforms continuous audio signals into sequences of integer indices for efficient storage and generative AI tasks.
- Audio Codebook Tokenization - Maps continuous neural embeddings to a finite vocabulary to enable discrete audio tokenization.
- Residual Vector Quantizers - Implements a residual vector quantizer with multiple codebook layers to achieve high-precision audio compression at low bitrates.
- Residual - Employs additive codebooks to achieve high precision audio representation at low bitrates.
- Discrete Token Extraction - Transforms audio waveforms into sequences of discrete codes for machine learning tasks.
- One-Dimensional Convolutions - Implements one-dimensional convolutional filters to extract hierarchical temporal features from raw audio samples.
- Quantized Audio Encoder-Decoders - Uses a mirrored encoder-decoder structure with a quantization bottleneck for audio reconstruction.
- Latent Space Encoders - Constrains audio information into a low-dimensional discrete sequence to extract the most salient features.
- Convolutional Architectures - Implements a symmetric convolutional architecture to downsample and upsample raw audio waveforms.
- Vector-Quantized VAEs - Utilizes a vector-quantized variational autoencoder to map audio to a discrete latent codebook.
- Audio Feature Extraction - Uses stacked convolutional filters to extract temporal features across multiple time scales from raw audio.
- Low Bitrate Audio Coding - Encodes audio at extremely low bandwidths for efficient transmission and storage.
Star history
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