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: - "*"