2023-08-23 21:42:32 +00:00
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dataset:
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training: [
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"./training/${voice}/valle/",
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]
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noise: [
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"./training/valle/data/Other/noise/",
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]
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speaker_name_getter: "lambda p: p.parts[-3]" # "lambda p: f'{p.parts[-3]}_{p.parts[-2]}'"
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use_hdf5: False
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hdf5_name: data.h5
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hdf5_flag: r
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validate: True
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2023-03-14 05:02:14 +00:00
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2023-08-23 21:42:32 +00:00
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workers: 4
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cache: False
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2023-03-22 17:47:23 +00:00
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2023-08-23 21:42:32 +00:00
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phones_range: [4, 64]
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duration_range: [1.0, 8.0]
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2023-03-14 15:48:09 +00:00
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2023-08-23 21:42:32 +00:00
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random_utterance: 1.0
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max_prompts: 3
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prompt_duration: 3.0
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sample_type: speaker
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tasks_list: ["tts"] # ["tts", "ns", "sr", "tse", "cse", "nse", "tts"]
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models:
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_max_levels: 8
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_models:
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- name: "ar"
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size: "full"
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resp_levels: 1
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prom_levels: 2
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tasks: 8
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arch_type: "retnet"
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- name: "nar"
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size: "full"
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resp_levels: 3
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prom_levels: 4
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tasks: 8
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arch_type: "retnet"
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hyperparameters:
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batch_size: ${batch_size}
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gradient_accumulation_steps: ${gradient_accumulation_size}
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gradient_clipping: 100
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optimizer: AdamW
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learning_rate: 1.0e-4
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scheduler_type: ""
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evaluation:
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batch_size: ${batch_size}
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frequency: ${validation_rate}
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size: 16
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steps: 300
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ar_temperature: 0.95
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nar_temperature: 0.25
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trainer:
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iterations: ${iterations}
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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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export_on_save: True
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export_on_quit: True
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save_frequency: ${save_rate}
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keep_last_checkpoints: 4
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aggressive_optimizations: 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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backend: deepspeed
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deepspeed:
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zero_optimization_level: 2
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use_compression_training: True
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inference:
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use_vocos: True
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normalize: False
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weight_dtype: float32
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bitsandbytes:
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enabled: False
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injects: True
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linear: True
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embedding: True
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