Refactor: replace utils.HParams with HyperParameters Pydantic model

HyperParameters is largely a drop-in replacement for utils.HParams, which ensures type safety for hyper-parameters.

train_ms.py

@@ -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 import (
     DurationDiscriminator,
     MultiPeriodDiscriminator,
@@ -130,7 +131,7 @@ 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
@@ -288,7 +289,29 @@ 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_ZH_bert", False):
@@ -547,7 +570,7 @@ def train_and_evaluate(
     rank,
     local_rank,
     epoch,
-    hps,
+    hps: HyperParameters,
     nets,
     optims,
     schedulers,