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data:
sampling_rate: 44100
block_size: 512 # Equal to hop_length
duration: 2 # Audio duration during training, must be less than the duration of the shortest audio clip
encoder: 'vec768l12' # 'hubertsoft', 'vec256l9', 'vec768l12'
cnhubertsoft_gate: 10
encoder_sample_rate: 16000
encoder_hop_size: 320
encoder_out_channels: 768 # 256 if using 'hubertsoft'
training_files: "filelists/train.txt"
validation_files: "filelists/val.txt"
extensions: # List of extension included in the data collection
- wav
unit_interpolate_mode: "nearest"
model:
type: 'Diffusion'
n_layers: 20
n_chans: 512
n_hidden: 256
use_pitch_aug: true
timesteps : 1000
k_step_max: 0 # must <= timesteps, If it is 0, train all
n_spk: 1 # max number of different speakers
device: cuda
vocoder:
type: 'nsf-hifigan'
ckpt: 'pretrain/nsf_hifigan/model'
infer:
speedup: 10
method: 'dpm-solver++' # 'pndm' or 'dpm-solver' or 'ddim' or 'unipc' or 'dpm-solver++'
env:
expdir: logs/44k/diffusion
gpu_id: 0
train:
num_workers: 4 # If your cpu and gpu are both very strong, set to 0 may be faster!
amp_dtype: fp32 # fp32, fp16 or bf16 (fp16 or bf16 may be faster if it is supported by your gpu)
batch_size: 48
cache_all_data: true # Save Internal-Memory or Graphics-Memory if it is false, but may be slow
cache_device: 'cpu' # Set to 'cuda' to cache the data into the Graphics-Memory, fastest speed for strong gpu
cache_fp16: true
epochs: 100000
interval_log: 10
interval_val: 2000
interval_force_save: 5000
lr: 0.0001
decay_step: 100000
gamma: 0.5
weight_decay: 0
save_opt: false
spk:
'nyaru': 0 |