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full-data-config_5_lat1024.yml
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full-data-config_5_lat1024.yml
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#architecture configuration
MODEL:
n_head: 8
n_layer: 10
dropout: 0.15
d_inner: 4096
d_embed: 512
d_model: 1024
dropatt: 0.0 #attention probability dropout rate
query_dim: 16
seq_len: 512
n_token: 3020
mem_len: 512
ext_len: 0
tgt_len: 70
eval_tgt_len: 50
init: 'normal' #parameter initializer to use.
emb_init: 'normal' #parameter initializer to use.
init_range: 0.1
emb_init_range: 0.01 #parameters initialized by U(-init_range, init_range)
init_std: 0.02 #parameters initialized by N(0, init_std)
proj_init_std: 0.01
clamp_len: -1 #use the same pos embeddings after clamp_len
div_val: 1
position_concat: False
pre_lnorm: True #apply LayerNorm to the input instead of the output
same_length: True #use the same attn length for all tokens
#training configuration
TRAIN:
data_path: "D:\\Documents\\DATA\\DadaGP-4-8-lps-3-dens-per-inst-hard-reps\\splitted_train_data_XL.npz"
val_path: "D:\\Documents\\DATA\\DadaGP-4-8-lps-3-dens-per-inst-hard-reps\\splitted_test_data_XL.npz"
output_dir: "./outputs/" # directory for saving model weights
batch_size: 4
lr: 0.0001
num_epochs: 240
save_freq: 2
seed: 2222
no_cuda: False
#resume_training_model: None
resume_training_model: './outputs/5_lat102420220805-121538/ep_204.pth.tar'
vocab_data_path: './data/vocab_song_artist.pkl'
rev_vocab_data_path: './data/rev_vocab_song_artist.pkl'
#generation configuration
INFERENCE:
num_sample: 5 #number of songs to generate
bpm: 120 #tempo to generate
num_bars: 16 #bars to generate
key: "a" #key to generate
initial_wait: 240 #240 is 16th note, 480 is 8th note etc...
gpuID: '0'
experiment_dir: './model-weights' # folder containing model checkpoint
generated_dir: './inference_attempts'
checkpoint_type: epoch_idx # best_train, best_val, epoch_idx
model_epoch: 40
no_cuda: False
vocab_data_path: './data/vocab_song_artist.pkl'
rev_vocab_data_path: './data/rev_vocab_song_artist.pkl'