48 lines
1.5 KiB
YAML
48 lines
1.5 KiB
YAML
data:
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sampling_rate: 44100
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block_size: 512 # Equal to hop_length
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duration: 1 # Audio duration during training, must be less than the duration of the shortest audio clip
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encoder: 'vec768l12' # 'hubertsoft', 'vec256l9', 'vec768l12'
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cnhubertsoft_gate: 10
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encoder_sample_rate: 16000
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encoder_hop_size: 320
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encoder_out_channels: 768 # 256 if using 'hubertsoft'
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training_files: "filelists/train.txt"
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validation_files: "filelists/val.txt"
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extensions: # List of extension included in the data collection
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- wav
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model:
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type: 'Diffusion'
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n_layers: 20
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n_chans: 512
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n_hidden: 256
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use_pitch_aug: true
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n_spk: 1 # max number of different speakers
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device: cuda
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vocoder:
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type: 'nsf-hifigan'
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ckpt: 'pretrain/nsf_hifigan/model'
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infer:
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speedup: 10
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method: 'dpm-solver' # 'pndm' or 'dpm-solver'
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env:
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expdir: logs/44k/diffusion
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gpu_id: 0
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train:
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num_workers: 2 # If your cpu and gpu are both very strong, set to 0 may be faster!
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amp_dtype: fp32 # fp32, fp16 or bf16 (fp16 or bf16 may be faster if it is supported by your gpu)
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batch_size: 48
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cache_all_data: true # Save Internal-Memory or Graphics-Memory if it is false, but may be slow
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cache_device: 'cpu' # Set to 'cuda' to cache the data into the Graphics-Memory, fastest speed for strong gpu
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cache_fp16: true
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epochs: 100000
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interval_log: 10
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interval_val: 2000
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interval_force_save: 10000
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lr: 0.0002
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decay_step: 100000
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gamma: 0.5
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weight_decay: 0
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save_opt: false
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spk:
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'nyaru': 0 |