egs/svc/DiffComoSVC/exp_config.json

{
    "base_config": "config/comosvc.json",
    "model_type": "DiffComoSVC",
    "dataset": [
        "m4singer",
        "opencpop",
        "opensinger",
        "svcc",
        "vctk"
    ],
    "dataset_path": {
        // TODO: Fill in your dataset path
        "m4singer": "[M4Singer dataset path]",
        "opencpop": "[Opencpop dataset path]",
        "opensinger": "[OpenSinger dataset path]",
        "svcc": "[SVCC dataset path]",
        "vctk": "[VCTK dataset path]"
    },
    // TODO: Fill in the output log path
    "log_dir": "[Your path to save logs and checkpoints]",
    "preprocess": {
        // TODO: Fill in the output data path
        "processed_dir": "[Your path to save processed data]",
        // Config for features extraction
        "extract_mel": true,
        "extract_pitch": true,
        "extract_energy": true,
        "extract_whisper_feature": true,
        "extract_contentvec_feature": true,
        "extract_wenet_feature": false,
        "whisper_batch_size": 30, // decrease it if your GPU is out of memory
        "contentvec_batch_size": 1,
        // Fill in the content-based pretrained model's path
        "contentvec_file": "pretrained/contentvec/checkpoint_best_legacy_500.pt",
        "wenet_model_path": "pretrained/wenet/20220506_u2pp_conformer_exp/final.pt",
        "wenet_config": "pretrained/wenet/20220506_u2pp_conformer_exp/train.yaml",
        "whisper_model": "medium",
        "whisper_model_path": "pretrained/whisper/medium.pt",
        // Config for features usage
        "use_mel": true,
        "use_min_max_norm_mel": true,
        "use_frame_pitch": true,
        "use_frame_energy": true,
        "use_spkid": true,
        "use_whisper": true,
        "use_contentvec": true,
        "use_wenet": false,
        "n_mel": 100,
        "sample_rate": 24000
    },
    "model": {
        "teacher_model_path":"[Your_teacher_model_checkpoint].bin",
        "condition_encoder": {
            // Config for features usage
            "use_whisper": true,
            "use_contentvec": true,
            "use_wenet": false,
            "whisper_dim": 1024,
            "contentvec_dim": 256,
            "wenet_dim": 512,
            "use_singer_encoder": false,
            "pitch_min": 50,
            "pitch_max": 1100
        },
        "comosvc":{
            "distill": false,
            // conformer encoder
            "input_dim": 384,
            "output_dim": 100,
            "n_heads": 2,
            "n_layers": 6,
            "filter_channels":512,
            "dropout":0.1,
            // karras diffusion
            "P_mean": -1.2,
            "P_std": 1.2,
            "sigma_data": 0.5,
            "sigma_min": 0.002,
            "sigma_max": 80,
            "rho": 7,
            "n_timesteps": 40,
        },
        "diffusion": {
            // Diffusion steps encoder
            "step_encoder": {
                "dim_raw_embedding": 128,
                "dim_hidden_layer": 512,
                "activation": "SiLU",
                "num_layer": 2,
                "max_period": 10000
            },
            // Diffusion decoder
            "model_type": "bidilconv",
            // bidilconv, unet2d, TODO: unet1d
            "bidilconv": {
                "base_channel": 384,
                "n_res_block": 20,
                "conv_kernel_size": 3,
                "dilation_cycle_length": 4,
                // specially, 1 means no dilation
                "conditioner_size": 100
            }
        }
    },
    "train": {
        "batch_size": 64,
        "gradient_accumulation_step": 1,
        "max_epoch": -1, // -1 means no limit
        "save_checkpoint_stride": [
            50,
            50
        ],
        "keep_last": [
            5,
            -1
        ],
        "run_eval": [
            false,
            true
        ],
        "adamw": {
            "lr": 4.0e-4
        },
        "reducelronplateau": {
            "factor": 0.8,
            "patience": 10,
            "min_lr": 1.0e-4
        },
        "dataloader": {
            "num_worker": 8,
            "pin_memory": true
        },
        "sampler": {
            "holistic_shuffle": false,
            "drop_last": true
        }
    },
    "inference": {
        "comosvc": {
            "inference_steps": 40
        }
    }
}