ai-voice-cloning/models/.template.yaml

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name: ${name}
model: extensibletrainer
scale: 1
gpu_ids: [0] # <-- unless you have multiple gpus, use this
start_step: -1
checkpointing_enabled: true # <-- Gradient checkpointing. Enable for huge GPU memory savings. Disable for distributed training.
fp16: false # might want to check this out
wandb: false # <-- enable to log to wandb. tensorboard logging is always enabled.
use_tb_logger: true
datasets:
train:
name: ${dataset_name}
n_workers: 8 # idk what this does
batch_size: ${batch_size} # This leads to ~16GB of vram usage on my 3090.
mode: paired_voice_audio
path: ${dataset_path}
fetcher_mode: ['lj'] # CHANGEME if your dataset isn't in LJSpeech format
phase: train
max_wav_length: 255995
max_text_length: 200
sample_rate: 22050
load_conditioning: True
num_conditioning_candidates: 2
conditioning_length: 44000
use_bpe_tokenizer: True
tokenizer_vocab: ./models/tortoise/bpe_lowercase_asr_256.json
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load_aligned_codes: False
val:
name: ${validation_name}
n_workers: 1
batch_size: 32 # this could be higher probably
mode: paired_voice_audio
path: ${validation_path}
fetcher_mode: ['lj']
phase: val # might be broken idk
max_wav_length: 255995
max_text_length: 200
sample_rate: 22050
load_conditioning: True
num_conditioning_candidates: 2
conditioning_length: 44000
use_bpe_tokenizer: True
tokenizer_vocab: ./models/tortoise/bpe_lowercase_asr_256.json
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load_aligned_codes: False
steps:
gpt_train:
training: gpt
loss_log_buffer: 500 # no idea what this does
# Generally follows the recipe from the DALLE paper.
optimizer: adamw # this should be adamw_zero if you're using distributed training
optimizer_params:
lr: !!float ${learning_rate} # CHANGEME: this was originally 1e-4; I reduced it to 1e-5 because it's fine-tuning, but **you should experiment with this value**
weight_decay: !!float 1e-2
beta1: 0.9
beta2: 0.96
clip_grad_eps: 4
injectors: # TODO: replace this entire sequence with the GptVoiceLatentInjector
paired_to_mel:
type: torch_mel_spectrogram
mel_norm_file: ./models/tortoise/clips_mel_norms.pth
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in: wav
out: paired_mel
paired_cond_to_mel:
type: for_each
subtype: torch_mel_spectrogram
mel_norm_file: ./models/tortoise/clips_mel_norms.pth
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in: conditioning
out: paired_conditioning_mel
to_codes:
type: discrete_token
in: paired_mel
out: paired_mel_codes
dvae_config: "./models/tortoise/train_diffusion_vocoder_22k_level.yml" # EXTREMELY IMPORTANT
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paired_fwd_text:
type: generator
generator: gpt
in: [paired_conditioning_mel, padded_text, text_lengths, paired_mel_codes, wav_lengths]
out: [loss_text_ce, loss_mel_ce, logits]
losses:
text_ce:
type: direct
weight: .01
key: loss_text_ce
mel_ce:
type: direct
weight: 1
key: loss_mel_ce
networks:
gpt:
type: generator
which_model_G: unified_voice2 # none of the unified_voice*.py files actually match the tortoise inference code... 4 and 3 have "alignment_head" (wtf is that?), 2 lacks the types=1 parameter.
kwargs:
layers: 30 # WAS 8
model_dim: 1024 # WAS 512
heads: 16 # WAS 8
max_text_tokens: 402 # WAS 120
max_mel_tokens: 604 # WAS 250
max_conditioning_inputs: 2 # WAS 1
mel_length_compression: 1024
number_text_tokens: 256 # supposed to be 255 for newer unified_voice files
number_mel_codes: 8194
start_mel_token: 8192
stop_mel_token: 8193
start_text_token: 255
train_solo_embeddings: False # missing in uv3/4
use_mel_codes_as_input: True # ditto
checkpointing: True
#types: 1 # this is MISSING, but in my analysis 1 is equivalent to not having it.
#only_alignment_head: False # uv3/4
path:
${pretrain_model_gpt}
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strict_load: true
${resume_state}
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# afaik all units here are measured in **steps** (i.e. one batch of batch_size is 1 unit)
train: # CHANGEME: ALL OF THESE PARAMETERS SHOULD BE EXPERIMENTED WITH
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niter: ${iterations}
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warmup_iter: -1
mega_batch_factor: ${mega_batch_factor} # <-- Gradient accumulation factor. If you are running OOM, increase this to [2,4,8].
val_freq: ${iterations}
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default_lr_scheme: MultiStepLR
gen_lr_steps: ${gen_lr_steps} #[50000, 100000, 140000, 180000]
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lr_gamma: 0.5
eval:
output_state: gen
injectors:
gen_inj_eval:
type: generator
generator: generator
in: hq
out: [gen, codebook_commitment_loss]
logger:
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print_freq: ${print_rate}
save_checkpoint_freq: ${save_rate} # CHANGEME: especially you should increase this it's really slow
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visuals: [gen, mel]
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visual_debug_rate: ${print_rate}
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is_mel_spectrogram: true