Spaces:
Running
on
T4
Running
on
T4
{ | |
"base_config": "config/diffusion.json", | |
"model_type": "DiffWaveNetSVC", | |
"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. The default value is "Amphion/ckpts/svc" | |
"log_dir": "ckpts/svc", | |
"preprocess": { | |
// TODO: Fill in the output data path. The default value is "Amphion/data" | |
"processed_dir": "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": { | |
"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 | |
}, | |
"diffusion": { | |
"scheduler": "ddpm", | |
"scheduler_settings": { | |
"num_train_timesteps": 1000, | |
"beta_start": 1.0e-4, | |
"beta_end": 0.02, | |
"beta_schedule": "linear" | |
}, | |
// 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": 512, | |
"n_res_block": 40, | |
"conv_kernel_size": 3, | |
"dilation_cycle_length": 4, | |
// specially, 1 means no dilation | |
"conditioner_size": 384 | |
} | |
} | |
}, | |
"train": { | |
"batch_size": 32, | |
"gradient_accumulation_step": 1, | |
"max_epoch": -1, // -1 means no limit | |
"save_checkpoint_stride": [ | |
3, | |
50 | |
], | |
"keep_last": [ | |
3, | |
2 | |
], | |
"run_eval": [ | |
true, | |
true | |
], | |
"adamw": { | |
"lr": 2.0e-4 | |
}, | |
"reducelronplateau": { | |
"factor": 0.8, | |
"patience": 30, | |
"min_lr": 1.0e-4 | |
}, | |
"dataloader": { | |
"num_worker": 8, | |
"pin_memory": true | |
}, | |
"sampler": { | |
"holistic_shuffle": false, | |
"drop_last": true | |
} | |
} | |
} |