149 lines
3.9 KiB
YAML
149 lines
3.9 KiB
YAML
sample_rate: 24_000 # 44_000 for dac
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audio_backend: "vocos" # or dac
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models:
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- name: "ar+nar" # vanity name
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size: "full" # model dimensionality
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resp_levels: 8 # RVQ levels this model targets
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prom_levels: 8 # should always be the above
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tasks: 8 # tasks this model can attend to, only tts is supported at the moment
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langs: 2 # languages this model supports, semi-unused at the moment
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tones: 1 # tones this model supports, currently unused
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arch_type: llama # underlying LLM arch to use, currently focusing on llama
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training: True # signals this model is to be trained
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version: 5 # helps keep backwards compatibility for when I add new things to the model
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attention: auto # attention mechanism to use, "auto" for safety
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dropout: 0.1 # percentage of the model to disable during training
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# factors for split loss values, remove to have a unified loss calculation
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loss_factors:
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text: 0.1 # text phoneme portion of the sequence
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prom: 0.0 # input prompt portion of the sequence
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resp: 1.0 # output audio portin of the sequence
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# experimental settings
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experimental:
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hf: False # uses vall_e.models.experimental, a wrapper around a HF model that could technically be used for non-pytorch backends later
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interleave: False # interleaves RVQ levels, only works with above for now
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audio_embedding_mode: "" # "" | "inclusive" | "exclusive", whether to utilize the audio backend's embeddings with the input embeddings
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audio_embedding_sums: False # whether the input embeddings include all prior RVQ levels (sums) or only the current one, further experimentation is needed to see if this matters
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p_rvq_levels: "equal" # "equal" | "auto", sets probabilities of which RVQ level to select during training, auto will have the next RVQ level half as likely as the previous one
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hyperparameters:
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autotune: False
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autotune_params:
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start_profile_step: 1
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end_profile_step: 50
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num_tuning_micro_batch_sizes: 8
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batch_size: 16
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gradient_accumulation_steps: 4
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gradient_clipping: 1.0
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warmup_steps: 100
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optimizer: Prodigy
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learning_rate: 1.0
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torch_optimizer: True
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scheduler: "" # ScheduleFree
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torch_scheduler: True
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evaluation:
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batch_size: 8
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frequency: 5000
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size: 8
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steps: 500
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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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#no_logger: True
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ddp: False
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#check_for_oom: False
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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: 250
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export_on_save: True
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keep_last_checkpoints: 4
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gradient_checkpointing: True
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strict_loading: False
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#load_state_dict: True
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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: float32 # float16 or bfloat16
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amp: False
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backend: deepspeed
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deepspeed:
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inferencing: True
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zero_optimization_level: 0
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use_compression_training: False
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amp: False
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load_webui: False
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inference:
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backend: deepspeed
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normalize: False
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weight_dtype: float32 # float16 or bfloat16
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amp: False
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optimizations:
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injects: False
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replace: True
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linear: False
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embedding: False
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optimizers: True
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bitsandbytes: False
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dadaptation: False
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bitnet: False
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fp8: False
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dataset:
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speaker_name_getter: "lambda p: f'{p.parts[-3]}_{p.parts[-2]}'"
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speaker_group_getter: "lambda p: f'{p.parts[-3]}'"
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speaker_languages:
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ja: []
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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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duration_range: [3.0, 5.0]
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random_utterance: 1.0
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max_prompts: 1
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prompt_duration: 3.0
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max_resps: 1
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p_resp_append: 0.25
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sample_type: path # path | speaker | group
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sample_order: duration # shuffle | duration
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sample_max_duration_batch: 0 # used when above = duration, 120 seconds per batch at 12GiB of VRAM works
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tasks_list: [ "tts" ] # , [ "tts", "tts-c", "ns", "sr", "tse", "cse", "nse", "tts"]
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training: []
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validation: []
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noise: [] |