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litagin02
@@ -9,8 +9,14 @@ from style_bert_vits2.models import commons
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from style_bert_vits2.models import utils
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from style_bert_vits2.models.hyper_parameters import HyperParameters
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from style_bert_vits2.models.models import SynthesizerTrn
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from style_bert_vits2.models.models_jp_extra import SynthesizerTrn as SynthesizerTrnJPExtra
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from style_bert_vits2.nlp import clean_text, cleaned_text_to_sequence, extract_bert_feature
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from style_bert_vits2.models.models_jp_extra import (
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SynthesizerTrn as SynthesizerTrnJPExtra,
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)
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from style_bert_vits2.nlp import (
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clean_text,
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cleaned_text_to_sequence,
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extract_bert_feature,
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)
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from style_bert_vits2.nlp.symbols import SYMBOLS
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@@ -18,69 +24,71 @@ def get_net_g(model_path: str, version: str, device: str, hps: HyperParameters):
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if version.endswith("JP-Extra"):
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logger.info("Using JP-Extra model")
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net_g = SynthesizerTrnJPExtra(
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n_vocab = len(SYMBOLS),
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spec_channels = hps.data.filter_length // 2 + 1,
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segment_size = hps.train.segment_size // hps.data.hop_length,
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n_speakers = hps.data.n_speakers,
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n_vocab=len(SYMBOLS),
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spec_channels=hps.data.filter_length // 2 + 1,
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segment_size=hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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# hps.model 以下のすべての値を引数に渡す
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use_spk_conditioned_encoder = hps.model.use_spk_conditioned_encoder,
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use_noise_scaled_mas = hps.model.use_noise_scaled_mas,
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use_mel_posterior_encoder = hps.model.use_mel_posterior_encoder,
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use_duration_discriminator = hps.model.use_duration_discriminator,
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use_wavlm_discriminator = hps.model.use_wavlm_discriminator,
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inter_channels = hps.model.inter_channels,
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hidden_channels = hps.model.hidden_channels,
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filter_channels = hps.model.filter_channels,
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n_heads = hps.model.n_heads,
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n_layers = hps.model.n_layers,
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kernel_size = hps.model.kernel_size,
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p_dropout = hps.model.p_dropout,
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resblock = hps.model.resblock,
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resblock_kernel_sizes = hps.model.resblock_kernel_sizes,
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resblock_dilation_sizes = hps.model.resblock_dilation_sizes,
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upsample_rates = hps.model.upsample_rates,
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upsample_initial_channel = hps.model.upsample_initial_channel,
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upsample_kernel_sizes = hps.model.upsample_kernel_sizes,
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n_layers_q = hps.model.n_layers_q,
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use_spectral_norm = hps.model.use_spectral_norm,
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gin_channels = hps.model.gin_channels,
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slm = hps.model.slm,
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use_spk_conditioned_encoder=hps.model.use_spk_conditioned_encoder,
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use_noise_scaled_mas=hps.model.use_noise_scaled_mas,
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use_mel_posterior_encoder=hps.model.use_mel_posterior_encoder,
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use_duration_discriminator=hps.model.use_duration_discriminator,
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use_wavlm_discriminator=hps.model.use_wavlm_discriminator,
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inter_channels=hps.model.inter_channels,
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hidden_channels=hps.model.hidden_channels,
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filter_channels=hps.model.filter_channels,
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n_heads=hps.model.n_heads,
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n_layers=hps.model.n_layers,
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kernel_size=hps.model.kernel_size,
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p_dropout=hps.model.p_dropout,
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resblock=hps.model.resblock,
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resblock_kernel_sizes=hps.model.resblock_kernel_sizes,
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resblock_dilation_sizes=hps.model.resblock_dilation_sizes,
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upsample_rates=hps.model.upsample_rates,
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upsample_initial_channel=hps.model.upsample_initial_channel,
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upsample_kernel_sizes=hps.model.upsample_kernel_sizes,
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n_layers_q=hps.model.n_layers_q,
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use_spectral_norm=hps.model.use_spectral_norm,
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gin_channels=hps.model.gin_channels,
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slm=hps.model.slm,
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).to(device)
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else:
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logger.info("Using normal model")
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net_g = SynthesizerTrn(
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n_vocab = len(SYMBOLS),
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spec_channels = hps.data.filter_length // 2 + 1,
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segment_size = hps.train.segment_size // hps.data.hop_length,
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n_vocab=len(SYMBOLS),
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spec_channels=hps.data.filter_length // 2 + 1,
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segment_size=hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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# hps.model 以下のすべての値を引数に渡す
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use_spk_conditioned_encoder = hps.model.use_spk_conditioned_encoder,
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use_noise_scaled_mas = hps.model.use_noise_scaled_mas,
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use_mel_posterior_encoder = hps.model.use_mel_posterior_encoder,
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use_duration_discriminator = hps.model.use_duration_discriminator,
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use_wavlm_discriminator = hps.model.use_wavlm_discriminator,
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inter_channels = hps.model.inter_channels,
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hidden_channels = hps.model.hidden_channels,
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filter_channels = hps.model.filter_channels,
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n_heads = hps.model.n_heads,
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n_layers = hps.model.n_layers,
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kernel_size = hps.model.kernel_size,
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p_dropout = hps.model.p_dropout,
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resblock = hps.model.resblock,
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resblock_kernel_sizes = hps.model.resblock_kernel_sizes,
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resblock_dilation_sizes = hps.model.resblock_dilation_sizes,
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upsample_rates = hps.model.upsample_rates,
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upsample_initial_channel = hps.model.upsample_initial_channel,
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upsample_kernel_sizes = hps.model.upsample_kernel_sizes,
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n_layers_q = hps.model.n_layers_q,
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use_spectral_norm = hps.model.use_spectral_norm,
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gin_channels = hps.model.gin_channels,
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slm = hps.model.slm,
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use_spk_conditioned_encoder=hps.model.use_spk_conditioned_encoder,
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use_noise_scaled_mas=hps.model.use_noise_scaled_mas,
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use_mel_posterior_encoder=hps.model.use_mel_posterior_encoder,
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use_duration_discriminator=hps.model.use_duration_discriminator,
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use_wavlm_discriminator=hps.model.use_wavlm_discriminator,
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inter_channels=hps.model.inter_channels,
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hidden_channels=hps.model.hidden_channels,
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filter_channels=hps.model.filter_channels,
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n_heads=hps.model.n_heads,
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n_layers=hps.model.n_layers,
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kernel_size=hps.model.kernel_size,
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p_dropout=hps.model.p_dropout,
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resblock=hps.model.resblock,
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resblock_kernel_sizes=hps.model.resblock_kernel_sizes,
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resblock_dilation_sizes=hps.model.resblock_dilation_sizes,
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upsample_rates=hps.model.upsample_rates,
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upsample_initial_channel=hps.model.upsample_initial_channel,
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upsample_kernel_sizes=hps.model.upsample_kernel_sizes,
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n_layers_q=hps.model.n_layers_q,
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use_spectral_norm=hps.model.use_spectral_norm,
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gin_channels=hps.model.gin_channels,
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slm=hps.model.slm,
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).to(device)
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net_g.state_dict()
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_ = net_g.eval()
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if model_path.endswith(".pth") or model_path.endswith(".pt"):
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_ = utils.checkpoints.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
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_ = utils.checkpoints.load_checkpoint(
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model_path, net_g, None, skip_optimizer=True
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)
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elif model_path.endswith(".safetensors"):
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_ = utils.safetensors.load_safetensors(model_path, net_g, True)
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else:
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@@ -102,8 +110,8 @@ def get_text(
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norm_text, phone, tone, word2ph = clean_text(
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text,
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language_str,
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use_jp_extra = use_jp_extra,
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raise_yomi_error = False,
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use_jp_extra=use_jp_extra,
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raise_yomi_error=False,
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)
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if given_tone is not None:
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if len(given_tone) != len(phone):
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