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model_name: "model_name"
# If you want to use a specific dataset path, uncomment the following line.
# Otherwise, the dataset path is `{dataset_root}/{model_name}`.
# dataset_path: "your/dataset/path"
resample:
sampling_rate: 44100
in_dir: "raw"
out_dir: "wavs"
preprocess_text:
transcription_path: "esd.list"
cleaned_path: ""
train_path: "train.list"
val_path: "val.list"
config_path: "config.json"
val_per_lang: 4
max_val_total: 12
clean: true
bert_gen:
config_path: "config.json"
num_processes: 2
device: "cuda"
use_multi_device: false
style_gen:
config_path: "config.json"
num_processes: 4
device: "cuda"
train_ms:
env:
MASTER_ADDR: "localhost"
MASTER_PORT: 10086
WORLD_SIZE: 1
LOCAL_RANK: 0
RANK: 0
model_dir: "models" # The directory to save the model (for training), relative to `{dataset_root}/{model_name}`.
config_path: "config.json"
num_workers: 16
spec_cache: True
keep_ckpts: 1 # Set this to 0 to keep all checkpoints
webui: # For `webui.py`, which is not supported yet in Style-Bert-VITS2.
# 推理设备
device: "cuda"
# 模型路径
model: "models/G_8000.pth"
# 配置文件路径
config_path: "config.json"
# 端口号
port: 7860
# 是否公开部署，对外网开放
share: false
# 是否开启debug模式
debug: false
# 语种识别库，可选langid, fastlid
language_identification_library: "langid"
# server_fastapi's config
server:
port: 5000
device: "cuda"
language: "JP"
limit: 100
origins:
- "*"
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