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tortoise-tts-models / finetunes /james-sunderland-v2 /dataset /train.yaml
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ecker
added dataset: SH2 James Sunderland (v2)
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name: james-finetune
model: extensibletrainer
scale: 1
gpu_ids: [0] # Superfluous, redundant, unnecessary, the way you launch the training script will set this
start_step: 0
checkpointing_enabled: true
fp16: false
wandb: false
use_tb_logger: true
datasets:
train:
name: james-train
n_workers: 2
batch_size: 68
mode: paired_voice_audio
path: ./training/james/train.txt
fetcher_mode: ['lj']
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
load_aligned_codes: False
val: # I really do not care about validation right now
name: james-val
n_workers: 1
batch_size: 1
mode: paired_voice_audio
path: ./training/james/train.txt
fetcher_mode: ['lj']
phase: val
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
load_aligned_codes: False
steps:
gpt_train:
training: gpt
loss_log_buffer: 500
# 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 1e-05 # originally: 1e-4
weight_decay: !!float 1e-2
beta1: 0.9
beta2: 0.96
clip_grad_eps: 4
injectors:
paired_to_mel:
type: torch_mel_spectrogram
mel_norm_file: ./models/tortoise/clips_mel_norms.pth
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
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"
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: 0.01
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 # originally: 8
model_dim: 1024 # originally: 512
heads: 16 # originally: 8
max_text_tokens: 402 # originally: 120
max_mel_tokens: 604 # originally: 250
max_conditioning_inputs: 2 # originally: 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: './models/tortoise/autoregressive.pth'
strict_load: true
resume_state: './training/james-finetune//training_state//500.state'
train:
niter: 2000
warmup_iter: -1
mega_batch_factor: 34
val_freq: 2000
ema_enabled: false # I really don't think EMA matters
default_lr_scheme: MultiStepLR
gen_lr_steps: [9, 18, 25, 33] #[50000, 100000, 140000, 180000]
lr_gamma: 0.5
eval:
output_state: gen
injectors:
gen_inj_eval:
type: generator
generator: generator
in: hq
out: [gen, codebook_commitment_loss]
logger:
print_freq: 5
save_checkpoint_freq: 50
visuals: [gen, mel]
visual_debug_rate: 5
is_mel_spectrogram: true