65 lines
3.0 KiB
YAML
65 lines
3.0 KiB
YAML
defaults:
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# - nn_params: nb8_embSum_NMT
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# - nn_params: remi8
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- nn_params: nb8_embSum_diff_t2m_150M_finetunning
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# - nn_params: nb8_embSum_diff_t2m_150M_pretraining
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# - nn_params: nb8_embSum_subPararell
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# - nn_params: nb8_embSum_diff_t2m_150M
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# - nn_params: nb8_embSum_subFeedForward
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# - nn_params: nb8_embSum_diff
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# nn_params: nb8_SA_diff
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# - nn_params: nb8_embSum_diff_main12head16dim512_ave
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# - nn_params: nb8_embSum_NMT_main12_head_16_dim512
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# - nn_params: remi8_main12_head_16_dim512
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# - nn_params: nb5_embSum_diff_main12head16dim768_sub3
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dataset: SOD # Pop1k7, Pop909, SOD, LakhClean,PretrainingDataset FinetuneDataset
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captions_path: dataset/midicaps/train_set.json
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# dataset: SymphonyNet_Dataset # Pop1k7, Pop909, SOD, LakhClean
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# captions_path: dataset/symphonyNet/syd-caption.json
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use_ddp: True # True, False | distributed data parallel
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use_fp16: True # True, False | mixed precision training
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use_diff: True # True,use diffusion in subdecoder
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diff_steps: 8 # number of diffusion steps
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use_dispLoss: True
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lambda_weight: 0.5
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tau: 0.5
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train_params:
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device: cuda
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batch_size: 3
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grad_clip: 1.0
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num_iter: 300000 # total number of iterations
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num_cycles_for_inference: 10 # number of cycles for inference, iterations_per_validation_cycle * num_cycles_for_inference
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num_cycles_for_model_checkpoint: 1 # number of cycles for model checkpoint, iterations_per_validation_cycle * num_cycles_for_model_checkpoint
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iterations_per_training_cycle: 10 # number of iterations for logging training loss
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iterations_per_validation_cycle: 5000 # number of iterations for validation process
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input_length: 3072 # input sequence length3072
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# you can use focal loss, it it's not used, set focal_gamma to 0
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focal_alpha: 1
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focal_gamma: 0
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# learning rate scheduler: 'cosinelr', 'cosineannealingwarmuprestarts', 'not-using', please check train_utils.py for more details
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scheduler : cosinelr
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initial_lr: 0.00005
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decay_step_rate: 0.8 # means it will reach its lowest point at decay_step_rate * total_num_iter
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num_steps_per_cycle: 20000 # number of steps per cycle for 'cosineannealingwarmuprestarts'
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warmup_steps: 2000 #number of warmup steps
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max_lr: 0.00015
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gamma: 0.6 # the decay rate for 'cosineannealingwarmuprestarts'
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# Distributed Data Parallel
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world_size: 5 # 0 means no distributed training
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gradient_accumulation_steps: 4 # 1 means no gradient accumulation
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inference_params:
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num_uncond_generation: 1 # number of unconditional generation
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num_cond_generation: 3 # number of conditional generation
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data_params:
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first_pred_feature: pitch # compound shifting for NB only, choose the target sub-token (remi and cp are not influenced by this argument)
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split_ratio: 0.998 # train-validation-test split ratio
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aug_type: pitch # random, null | pitch and chord augmentation type
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general:
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debug: False
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make_log: True # True, False | update the log file in wandb online to your designated project and entity
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infer_and_log: True # True, False | inference and log the results |