LPCNet superseded by FARGAN

[jmvalin]

LPCNet is no longer being actively developed. It will continue to be available but for most applications, users are encouraged to switch to the Framewise Autoregressive GAN (FARGAN). FARGAN achieves better quality than LPCNet with just 600 MFLOPS complexity. That's 1/5 of the complexity of the most optimized LPCNet and 1/20 of the original LPCNet.

• J.-M. Valin, A. Mustafa, Jan Büthe, Very Low Complexity Speech Synthesis Using Framewise Autoregressive GAN (FARGAN) with Pitch Prediction, IEEE Signal Processing Letters, 2024, arXiv:2405.21069.

See our demo page for comparisons with LPCNet, HiFi-GAN, CARGAN and FWGAN. The PyTorch source code along with an optimized C implementation are available as part of the larger Opus codec implementation (FARGAN is used for PLC and deep redundancy within Opus).

[YYX666660]

Hi~
I've read the code and articles about FARGAN. In the latest opus 1.5.2 source code, FARGAN acts as a vocoder after the silk plc for improving the quality of synthesized speech, rather than directly for packet loss concealment.
I would like to ask if I want to train a specialized FARGAN network to perform packet loss concealment directly, what should I do? Is there a code repository implementation for this?
Thanks in advance,
Freya

[jmvalin]

FARGAN is a complete vocoder just like LPCNet. In Opus, the FARGAN signal completely replaces (not enhance) the SILK PLC output.

[YYX666660]

Thank you so much for your reply!

I'm a beginner of codec, and I'm not sure if my understanding is correct 🙏 In the Opus1.5.2 source code, when it comes to lpcnet_plc_conceal module, it seems that the process begins with using compute_plc_pred to update the features and states (using PLCModel, part of the LPCNet), and then compute_pitchdnn computes features for FARGAN (using PitchDNN model). Lastly, FARGAN (using FARGAN model) is used to synthesize pcm. So these three models are used to complete plc together?

If I want to perform plc directly, those three models must be trained and replaced the existing ones in Opus1.5.2, how can I train them separately? (only find the pytorch implementation of FARGAN)