dataset:
  training: [
    
  ]

  validation: [

  ]
  
  speaker_name_getter: "lambda p: f'{p.parts[-3]}_{p.parts[-2]}'"
  
  use_hdf5: True
  validate: True

  workers: 8
  cache: True

  phones_range: [4, 256]
  duration_range: [1.0, 12.0]

  random_utterance: 1.0
  max_prompts: 3
  prompt_duration: 3.0

models:
  _models:
  - name: "ar"
    size: "quarter"
    resp_levels: 1
    arch_type: "retnet"

  - name: "nar"
    size: "quarter"
    resp_levels: 1
    arch_type: "retnet"
  
  prom_levels: 2

hyperparameters:
  batch_size: 32
  gradient_accumulation_steps: 4
  gradient_clipping: 100
  
  optimizer: Adamw
  learning_rate: 1.0e-4
  
  scheduler_type: ""
  #scheduler_type: OneCycle
  #scheduler_params:
  #  cycle_first_step_size: 10_000
  #  cycle_first_stair_count: 10_000

  #  cycle_second_step_size: 15_000
  #  cycle_second_stair_count: 15_000

  #  decay_step_size: 5_000

  #  cycle_min_lr: 2.5e-4 # 1.0e-5
  #  cycle_max_lr: 2.5e-4 # 1.0e-4
  #  decay_lr_rate: 0.0

  #  cycle_min_mom: 0.90
  #  cycle_max_mom: 0.99
  #  decay_mom_rate: 0.0

evaluation:
  batch_size: 32
  frequency: 250
  size: 32
  
  steps: 300
  temperature: 1.0

trainer:
  iterations: 100_000
  
  save_tag: step
  save_on_oom: True
  save_on_quit: True
  save_frequency: 100

  aggressive_optimizations: False

  #load_tag: "9500"
  #load_state_dict: True
  #load_states: False
  #strict_loading: False
  #restart_step_count: True
  
  gc_mode: None # "global_step"

  weight_dtype: bfloat16

  zero_optimization_level: 2
  use_compression_training: True

use_vocos: False