Name Date Size #Lines LOC

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data/H25-Apr-2025-229155

engine/H25-Apr-2025-14180

models/H25-Apr-2025-750513

scripts/H25-Apr-2025-504332

utils/H25-Apr-2025-1,7151,284

README.mdH A D25-Apr-2025782 2816

add_dataset_config.pyH A D25-Apr-20252.6 KiB7856

make_default_setup.pyH A D25-Apr-20252 KiB5742

make_test_config.pyH A D25-Apr-20253.3 KiB7964

print_lpcnet_complexity.pyH A D25-Apr-20252.3 KiB6549

test_lpcnet.pyH A D25-Apr-20252.8 KiB9065

train_lpcnet.pyH A D25-Apr-20259.1 KiB273195

README.md

1# LPCNet
2
3Incomplete pytorch implementation of LPCNet
4
5## Data preparation
6For data preparation use dump_data in github.com/xiph/LPCNet. To turn this into
7a training dataset, copy data and feature file to a folder and run
8
9python add_dataset_config.py my_dataset_folder
10
11
12## Training
13To train a model, create and adjust a setup file, e.g. with
14
15python make_default_setup.py my_setup.yml --path2dataset my_dataset_folder
16
17Then simply run
18
19python train_lpcnet.py my_setup.yml my_output
20
21## Inference
22Create feature file with dump_data from github.com/xiph/LPCNet. Then run e.g.
23
24python test_lpcnet.py features.f32 my_output/checkpoints/checkpoint_ep_10.pth out.wav
25
26Inference runs on CPU and takes usually between 3 and 20 seconds per generated second of audio,
27depending on the CPU.
28