vall-e/data/config.yaml
2023-08-14 10:50:40 -05:00

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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, 128]
duration_range: [1.0, 12.0]
random_utterance: 1.0
max_prompts: 3
prompt_duration: 3.0
models:
_models:
- name: "ar"
size: "full"
resp_levels: 1
arch_type: "retnet"
- name: "nar"
size: "full"
resp_levels: 1
arch_type: "retnet"
prom_levels: 2
hyperparameters:
batch_size: 8
gradient_accumulation_steps: 16
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
ar_temperature: 1.0
nar_temperature: 0.2
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 # float16, float32
backend: deepspeed
deepspeed:
zero_optimization_level: 2
use_compression_training: True
inference:
use_vocos: True
bitsandbytes:
enabled: false