Prepare Vocoder

We use HiFi-GAN as the default vocoder.

LJSpeech

Use Pretrained Model

wget https://github.com/xx/xx/releases/download/pretrain-model/hifi_lj.zip
unzip hifi_lj.zip
mv hifi_lj checkpoints/hifi_lj

Train Your Vocoder

Set Config Path and Experiment Name

export CONFIG_NAME=egs/datasets/audio/lj/hifigan.yaml  
export MY_EXP_NAME=my_hifigan_exp

Prepare Dataset

Prepare dataset following prepare_data.md.

If you have run the prepare_data step of the acoustic model (e.g., PortaSpeech and DiffSpeech), you only need to binarize the dataset for the vocoder training:

python data_gen/tts/runs/binarize.py --config $CONFIG_NAME

Training

CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config $CONFIG_NAME --exp_name $MY_EXP_NAME --reset

Inference (Testing)

CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config $PS_CONFIG --exp_name $MY_EXP_NAME --infer

Use the trained vocoder

Modify the vocoder_ckpt in config files of acoustic models (e.g., egs/datasets/audio/lj/base_text2mel.yaml) to $MY_EXP_NAME (e.g., vocoder_ckpt: checkpoints/my_hifigan_exp)