126 lines
2.3 KiB
YAML
Executable File
126 lines
2.3 KiB
YAML
Executable File
dataset:
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training: []
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validation: []
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noise: []
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speaker_name_getter: "lambda p: f'{p.parts[-3]}_{p.parts[-2]}'"
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use_hdf5: True
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use_metadata: True
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hdf5_flag: r
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validate: True
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workers: 2
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cache: True
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phones_range: [4, 256]
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duration_range: [1.0, 16.0]
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min_utterances: 32
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random_utterance: 1.0
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max_prompts: 6
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prompt_duration: 6.0
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sample_type: speaker
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tasks_list: [ "tts" ] # , [ "tts", "tts-c", "ns", "sr", "tse", "cse", "nse", "tts"]
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models:
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_prom_levels: 8
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_max_levels: 8
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_models:
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- name: "ar+nar"
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size: "double"
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resp_levels: 8
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prom_levels: 8
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tasks: 8
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arch_type: "retnet"
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training: True
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version: 2
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hyperparameters:
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batch_size: 8
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gradient_accumulation_steps: 16
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gradient_clipping: 100
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# prodigyopt is nicer, but requires even more VRAM
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#optimizer: Prodigy
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#learning_rate: 1.0 # e-4
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optimizer: AdamW
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learning_rate: 1.0e-4
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torch_optimizer: True
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scheduler_type: ""
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#scheduler_type: OneCycle
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#scheduler_params:
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# cycle_first_step_size: 10_000
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# cycle_first_stair_count: 10_000
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# cycle_second_step_size: 15_000
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# cycle_second_stair_count: 15_000
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# decay_step_size: 5_000
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# cycle_min_lr: 2.5e-4 # 1.0e-5
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# cycle_max_lr: 2.5e-4 # 1.0e-4
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# decay_lr_rate: 0.0
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# cycle_min_mom: 0.90
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# cycle_max_mom: 0.99
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# decay_mom_rate: 0.0
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evaluation:
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batch_size: 16
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frequency: 250
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size: 16
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steps: 450
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ar_temperature: 0.95
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nar_temperature: 0.25
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load_disabled_engines: True
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trainer:
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iterations: 1_000_000
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save_tag: step
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save_on_oom: True
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save_on_quit: True
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save_frequency: 100
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export_on_save: True
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keep_last_checkpoints: 4
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aggressive_optimizations: False
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load_disabled_engines: False
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#load_state_dict: True
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#strict_loading: False
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#load_tag: "9500"
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#load_states: False
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#restart_step_count: True
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gc_mode: None # "global_step"
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weight_dtype: bfloat16
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amp: False
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backend: deepspeed
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deepspeed:
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zero_optimization_level: 0
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use_compression_training: True
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activation_checkpointing: True
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inference:
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use_vocos: True
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normalize: False
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bitsandbytes:
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enabled: False
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injects: False
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linear: False
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embedding: False
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