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This network is equivalent to removing RVQ from the Encodec. Please refer to the Encodec for the diagram
This project is an unofficial reproduction of encodec. The training framework comes from so-vits-svc.
https://pytorch.org/get-started/locally/
conda create -n hifi-vaegan python=3.10
conda activate hifi-vaegan
pip install -r requirements.txt
You can use any high-quality audio data, including but not limited to vocals and instruments, and there is no need for classification.
Place all data in the "dataset_raw" folder without any nested subfolders.
python resample.py
python preprocess_config.py
Then you can modify the following options as needed in the configs\config.json file
log_interval: Log interval recording, unit is step
eval_interval: Verification interval, unit is step
learning_rate: learning rate
batch_size: Increase it as much as possible to use all the video memory
fp16_run: "False: is fp32, "True" uses half_type to control accuracy
half_type: Choose from bf16, fp16
lr_decay: The decay rate of the learning rate after each round
segment_size: Slice length, please find a balance between batch_size and this option
keep_ckpts: How many checkpoints to save
all_in_mem: If you have TB-level memory, turning it on can slightly speed up training and reduce hard disk pressure.
python train.py -m <Experiment name>
tensorboard --logdir=logs --load_fast=False