High Fidelity Neural Audio Compression

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The idea is to use a codec (short for coder-decoder) that can learn the patterns in the audio and represent them with fewer bits without losing too much quality.

Here’s how it works: first, you preprocess the audio by converting it into a spectrogram using some fancy math stuff called STFT (Short Time Fourier Transform). This basically breaks down the sound waves into smaller chunks that can be analyzed more easily. Then, you feed those chunks through a neural network that has been trained to learn how to represent them with fewer bits without losing too much quality.

The cool thing about this method is that it’s really flexible you can adjust the target bitrate (how many bits per second) and the model will automatically figure out how to compress the audio accordingly. And because it uses a neural network, it can learn from data and improve over time as more audio files are processed.

Here’s an example of what this might look like in code:

# Import necessary libraries
import librosa # library for working with audio
import torch # library for working with tensors
from encodec import EnCodecModel # import EnCodecModel from encodec library

# Load the audio file using LibROSA
audio, sr = librosa.load('your_audio_file.wav', sr=16000) # load audio file with a sampling rate of 16000 Hz

# Convert the audio to a spectrogram using STFT
S = librosa.stft(y=audio, n_fft=2048, hop_length=512, window='hann') # convert audio to a spectrogram using STFT with a window size of 2048 and hop length of 512

# Preprocess the spectrogram for EnCodec
X = torch.from_numpy(S).float().unsqueeze(0) # convert spectrogram to a tensor and add a batch dimension

# Set up the target bitrate using EnCodec's set_target_bandwidth() method
model = EnCodecModel.encodec_model_24khz() # create an instance of EnCodecModel with a sampling rate of 24kHz
model.set_target_bandwidth(6.0) # set the target bitrate to 6kbps (bits per second)

# Compress the spectrogram using EnCodec's encode() method
encoded = model.encode(X) # compress the spectrogram using EnCodec's encode method

# Extract the compressed data as an array of integers
codes, scale = encoded[0] # extract the compressed data and scaling factor from the encoded output

In terms of applications, Meta AI clgoals that their new technique could support “faster, better-quality calls” in bad network conditions. And, of course, being Meta, the researchers also mention EnCodec’s metaverse implications, saying the technology could eventually deliver “rich metaverse experiences without requiring major bandwidth improvements.”

The paper titled “High Fidelity Neural Audio Compression,” authored by Meta AI researchers Alexandre Défossez, Jade Copet, Gabriel Synnaeve, and Yossi Adi, introduces a new audio compression method called EnCodec. This technique uses neural networks to compress audio files with fewer bits without losing too much quality. The process involves converting the audio into spectrograms using STFT (Short Time Fourier Transform), feeding those chunks through a trained neural network that learns how to represent them with fewer bits, and then decoding the compressed data back into audio in real time using a single CPU. This method is flexible and can adjust target bitrates automatically based on the input data. Meta AI clgoals that EnCodec could support faster, better-quality calls in bad network conditions and eventually deliver rich metaverse experiences without requiring major bandwidth improvements.

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