sq_codec 0.0.1

SQCodec: A nerual audio codec with one quantizer

SQCodec

This repository contains the implementation of SQCodec, a lightweight audio codec based on a single quantizer, introduced in the paper titled "One Quantizer is Enough: Toward a Lightweight Audio Codec".

Paper

Model Weights

Comparison of various audio codec

install

pip install sq_codec

demo

Firstly, make sure you have installed the librosa package to load the example audio file. You can install it using pip:

pip install librosa

Then, you can use the following code to load a sample audio file, encode it using the SQCodec model, and decode it back to audio. The code also calculates the mean squared error (MSE) between the original and generated audio.

import librosa
import torch
import sq_codec

all_models = sq_codec.list_models()
print(f"Available models: {all_models}")

MODEL_USED = '6kbps'
codec = sq_codec.get_model(MODEL_USED)
print(f"loaded codec({MODEL_USED}) and codec sample rate: {codec.config.sample_rate}")

sample_audio, sample_rate = librosa.load(librosa.example("libri1"))
sample_audio = sample_audio[None, :]
print(f"loaded sample audio and audio sample_rate :{sample_rate}")

sample_audio = librosa.resample(sample_audio, orig_sr=sample_rate, target_sr=codec.config.sample_rate)

codec.network.cuda()
codec.network.eval()
with torch.inference_mode():
    audio_in = torch.tensor(sample_audio, dtype=torch.float32, device='cuda')
    _, audio_length = audio_in.shape
    print(f"{audio_in.shape=}")
    q_feature, indices = codec.encode_audio(audio_in)
    audio_out = codec.decode_audio(q_feature)  # or
    # audio_out = codec.decode_audio(indices=indices)
    generated_audio = audio_out[:, :audio_length].detach().cpu().numpy()

mse = ((sample_audio - generated_audio) ** 2).mean().item()
print(f"codec({MODEL_USED}) mse: {mse}")