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Refactor: replace utils.HParams with HyperParameters Pydantic model

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HyperParameters is largely a drop-in replacement for utils.HParams, which ensures type safety for hyper-parameters.
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tsukumi
2024-03-08 15:52:37 +00:00
parent 7f0b252806
commit a84783a6cc
11 changed files with 190 additions and 221 deletions
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27
train_ms_jp_extra.py
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@@ -26,6 +26,7 @@ from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from style_bert_vits2.logging import logger
from style_bert_vits2.models import commons
from style_bert_vits2.models import utils
from style_bert_vits2.models.hyper_parameters import HyperParameters
from style_bert_vits2.models.models_jp_extra import (
DurationDiscriminator,
MultiPeriodDiscriminator,
@@ -129,7 +130,7 @@ def run():
local_rank = int(os.environ["LOCAL_RANK"])
n_gpus = dist.get_world_size()
hps = utils.get_hparams_from_file(args.config)
hps = HyperParameters.load_from_json(args.config)
# This is needed because we have to pass values to `train_and_evaluate()
hps.model_dir = model_dir
hps.speedup = args.speedup
@@ -298,7 +299,29 @@ def run():
n_speakers=hps.data.n_speakers,
mas_noise_scale_initial=mas_noise_scale_initial,
noise_scale_delta=noise_scale_delta,
**hps.model,
# hps.model 以下のすべての値を引数に渡す
use_spk_conditioned_encoder = hps.model.use_spk_conditioned_encoder,
use_noise_scaled_mas = hps.model.use_noise_scaled_mas,
use_mel_posterior_encoder = hps.model.use_mel_posterior_encoder,
use_duration_discriminator = hps.model.use_duration_discriminator,
use_wavlm_discriminator = hps.model.use_wavlm_discriminator,
inter_channels = hps.model.inter_channels,
hidden_channels = hps.model.hidden_channels,
filter_channels = hps.model.filter_channels,
n_heads = hps.model.n_heads,
n_layers = hps.model.n_layers,
kernel_size = hps.model.kernel_size,
p_dropout = hps.model.p_dropout,
resblock = hps.model.resblock,
resblock_kernel_sizes = hps.model.resblock_kernel_sizes,
resblock_dilation_sizes = hps.model.resblock_dilation_sizes,
upsample_rates = hps.model.upsample_rates,
upsample_initial_channel = hps.model.upsample_initial_channel,
upsample_kernel_sizes = hps.model.upsample_kernel_sizes,
n_layers_q = hps.model.n_layers_q,
use_spectral_norm = hps.model.use_spectral_norm,
gin_channels = hps.model.gin_channels,
slm = hps.model.slm,
).cuda(local_rank)
if getattr(hps.train, "freeze_JP_bert", False):
logger.info("Freezing (JP) bert encoder !!!")