164 lines
8.1 KiB
YAML
164 lines
8.1 KiB
YAML
sample_rate: 24_000 # 44_000 for dac
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audio_backend: "vocos" # or dac
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# model definitions to train
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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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# hyperparameter settings (could be relegated to trainer settings)
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hyperparameters:
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# deepspeed autotune
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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 # samples per batch, governs maximum batch size if using batch sampling
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gradient_accumulation_steps: 4 # gradient accumulation: batches per update
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gradient_clipping: 1.0 # smooths out the gradient when updating
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warmup_steps: 100 # steps to warm up the optimizer but not update the model
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# optimizer settings
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optimizer: Prodigy
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learning_rate: 1.0 # prodigyopt can keep its LR to 1
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torch_optimizer: True # signals to deepspeed to not instantiate one
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# deepspeed scheduler, local does have it implemented because I don't use one
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scheduler: "" # ScheduleFree
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torch_scheduler: True # signals to deepspeed to not instantiate one
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# evaluation settings (could be pushed under trainer)
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evaluation:
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batch_size: 8 # batch size for evaluation / validation pass
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frequency: 5000 # how often to perform eval during training
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size: 8 # total samples to get for eval
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steps: 500 # how many AR steps to perform
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ar_temperature: 0.95 # temperature for AR sampling
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nar_temperature: 0.25 # temperature for NAR sampling
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load_disabled_engines: True # deprecated
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trainer:
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#no_logger: True # deprecated, because the logger should always work now
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ddp: False # whether to wrap the model with DDP, should automatically be set
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#check_for_oom: False # wrap forward/backwards in a try/catch block and gracefully handles OOM conditions
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iterations: 1_000_000 # how many total iterations to train before terminating, should just have this as 0 by default to not auto-terminiate
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save_tag: step # tag name to save checkpoints under
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save_on_oom: True # save if an OOM if caught
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save_on_quit: True # save when `quit` is entered in the trainer
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save_frequency: 250 # how often to save
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export_on_save: True # export the weights every time the trainer saves
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keep_last_checkpoints: 4 # how many previous checkpoints to keep
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gradient_checkpointing: True # gradient checkpointing to save VRAM at the cost of some performance throughput
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strict_loading: False # strict state dict loading (set to False if you're going to change some model settings)
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#load_state_dict: True # load the state dict from fp32.pth instead of a checkpoint, should automagically be done
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#load_tag: "9500" # specific tag to load from (instead of having to edit latest)
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#load_states: False # flag to load optimizer / scheduler states or not
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#restart_step_count: True # clear the trainer stats
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gc_mode: None # "global_step" # flag to call GC at specific points, seems overkill now
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weight_dtype: float32 # float32 | float16 | bfloat16, dtype for the model to load under
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amp: False # mixed precision during training
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backend: deepspeed # deepspeed | local, training backend to use
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# deepspeed specific settings
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deepspeed:
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inferencing: True # use deepspeed inference wrapper for inferencing, should be relegated under inference
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zero_optimization_level: 0 # ZeRO optimization level to use
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use_compression_training: False # compression training (seems useless almost always)
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amp: False # use deepspeed's AMP instead (requires nvidia/apex installed)
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load_webui: False # initialize the web UI during training (the goal is to let you inference during training, but I never found a good way to go about it)
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# inferencing settings
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inference:
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backend: deepspeed # deepspeed | local, training backend to use
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normalize: False # normalize audio before encoding / after decoding, only enable if you know what you're doing
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weight_dtype: float32 # float32 | float16 | bfloat16, dtype for the model to load under
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amp: False # mixed precision during inferencing
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# experimental optimization flags
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optimizations:
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injects: False # replace the module in the torch package itself to achieve these
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replace: True # replace the module in the model itself to achieve these
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# bitsandbytes things
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linear: False # enable nn.Linear optimizations
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embedding: False # enable nn.Embedding optimizations
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optimizers: True # enable torch.optim optimizations
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bitsandbytes: False # use bitsandbytes
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dadaptation: False # use dadaptation
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bitnet: False # use bitnet
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fp8: False # use nvidia/transformer-engine's fp8 AMP
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# dataset settings
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dataset:
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speaker_name_getter: "lambda p: f'{p.parts[-3]}_{p.parts[-2]}'" # function to eval when fetching the speaker from a name
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speaker_group_getter: "lambda p: f'{p.parts[-3]}'" # function to eval when fetching the group from a name
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# map to classify languages under when preparing a batch (in case the language is not provided in the dataset)
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speaker_languages:
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ja: []
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use_hdf5: True # use HDF5 file to load the dataset from
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hdf5_flag: r # flag to load the HDF5 file under (should automatically set to `a` when generating the HDF5 dataset)
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use_metadata: True # use generated metadata to help prepare the dataset
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validate: True # cull samples if they are outside the duration threshold
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workers: 2 # worker processes to spawn for the dataloader
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cache: True # cache the dataloader to disk to speed things up
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duration_range: [3.0, 5.0] # allowed sample duration in the dataset
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random_utterance: 1.0 # I don't remember desu
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max_prompts: 1 # how many prompts to sample to create the input prompt
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#prompt_duration: 3.0 # sugar for the below
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prompt_duration_range: [3.0, 3.0] # range of durations for the input prompt to be trimmed under
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# deprecated
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max_resps: 1 # how many random response utterances to sample for the sample
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p_resp_append: 0.25 # probability to append additional utterances for the above
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sample_type: path # path | speaker | group, type to sample the paths from (by path, speaker, or group)
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sample_order: duration # duration | anything else, method of ordering the paths (duration is by duration, any other value will interleave reorder)
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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"], unused at the moment, but will determine which tasks to use
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training: [] # paths for the training dataset
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validation: [] # paths for the validation dataset
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noise: [] # paths for the noise dataset (unused, but for the above tasks that call for injecting noise) |