From a84783a6cc75ef17c8f6773728368c1b1642285c Mon Sep 17 00:00:00 2001 From: tsukumi Date: Fri, 8 Mar 2024 15:52:37 +0000 Subject: [PATCH] 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. --- bert_gen.py | 4 +- data_utils.py | 3 +- style_bert_vits2/models/hyper_parameters.py | 30 ++- style_bert_vits2/models/infer.py | 192 +++++++------------- style_bert_vits2/models/models.py | 4 +- style_bert_vits2/models/models_jp_extra.py | 4 +- style_bert_vits2/models/utils.py | 42 ----- style_bert_vits2/tts_model.py | 73 ++++---- style_gen.py | 3 +- train_ms.py | 29 ++- train_ms_jp_extra.py | 27 ++- 11 files changed, 190 insertions(+), 221 deletions(-) diff --git a/bert_gen.py b/bert_gen.py index 0935929..5a16af7 100644 --- a/bert_gen.py +++ b/bert_gen.py @@ -8,7 +8,7 @@ from tqdm import tqdm from config import config 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.nlp import cleaned_text_to_sequence, extract_bert_feature from style_bert_vits2.utils.stdout_wrapper import SAFE_STDOUT @@ -62,7 +62,7 @@ if __name__ == "__main__": ) args, _ = parser.parse_known_args() config_path = args.config - hps = utils.get_hparams_from_file(config_path) + hps = HyperParameters.load_from_json(config_path) lines = [] with open(hps.data.training_files, encoding="utf-8") as f: lines.extend(f.readlines()) diff --git a/data_utils.py b/data_utils.py index 99da2e4..04047e2 100644 --- a/data_utils.py +++ b/data_utils.py @@ -11,6 +11,7 @@ from config import config from mel_processing import mel_spectrogram_torch, spectrogram_torch from style_bert_vits2.logging import logger from style_bert_vits2.models import commons +from style_bert_vits2.models.hyper_parameters import HyperParametersData from style_bert_vits2.models.utils import load_filepaths_and_text, load_wav_to_torch from style_bert_vits2.nlp import cleaned_text_to_sequence @@ -24,7 +25,7 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset): 3) computes spectrograms from audio files. """ - def __init__(self, audiopaths_sid_text, hparams): + def __init__(self, audiopaths_sid_text: str, hparams: HyperParametersData): self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text) self.max_wav_value = hparams.max_wav_value self.sampling_rate = hparams.sampling_rate diff --git a/style_bert_vits2/models/hyper_parameters.py b/style_bert_vits2/models/hyper_parameters.py index cee7924..9dc5afb 100644 --- a/style_bert_vits2/models/hyper_parameters.py +++ b/style_bert_vits2/models/hyper_parameters.py @@ -1,16 +1,16 @@ """ Style-Bert-VITS2 モデルのハイパーパラメータを表す Pydantic モデル。 -デフォルト値は configs/configs_jp_extra.json 内の定義と同一で、 +デフォルト値は configs/configs_jp_extra.json 内の定義と概ね同一で、 万が一ロードした config.json に存在しないキーがあった際のフェイルセーフとして適用される。 """ from pathlib import Path from typing import Optional, Union -from pydantic import BaseModel +from pydantic import BaseModel, ConfigDict -class __HyperParametersTrain(BaseModel): +class HyperParametersTrain(BaseModel): log_interval: int = 200 eval_interval: int = 1000 seed: int = 42 @@ -36,7 +36,8 @@ class __HyperParametersTrain(BaseModel): freeze_style: bool = False freeze_decoder: bool = False -class __HyperParametersData(BaseModel): + +class HyperParametersData(BaseModel): use_jp_extra: bool = True training_files: str = "Data/dummy/train.list" validation_files: str = "Data/dummy/val.list" @@ -59,7 +60,8 @@ class __HyperParametersData(BaseModel): "Neutral": 0, } -class __HyperParametersModel(BaseModel): + +class HyperParametersModel(BaseModel): use_spk_conditioned_encoder: bool = True use_noise_scaled_mas: bool = True use_mel_posterior_encoder: bool = False @@ -93,12 +95,21 @@ class __HyperParametersModel(BaseModel): "initial_channel": 64 } + class HyperParameters(BaseModel): - version: str = "2.0-JP-Extra" model_name: str = 'dummy' - train: __HyperParametersTrain - data: __HyperParametersData - model: __HyperParametersModel + version: str = "2.0-JP-Extra" + train: HyperParametersTrain + data: HyperParametersData + model: HyperParametersModel + + # 以下は学習時にのみ動的に設定されるパラメータ (通常 config.json には存在しない) + model_dir: Optional[str] = None + speedup: bool = False + repo_id: Optional[str] = None + + # model_ 以下を Pydantic の保護対象から除外する + model_config = ConfigDict(protected_namespaces=()) @staticmethod @@ -112,5 +123,6 @@ class HyperParameters(BaseModel): Returns: HyperParameters: ハイパーパラメータ """ + with open(json_path, "r") as f: return HyperParameters.model_validate_json(f.read()) diff --git a/style_bert_vits2/models/infer.py b/style_bert_vits2/models/infer.py index 7394d0b..93bf8c2 100644 --- a/style_bert_vits2/models/infer.py +++ b/style_bert_vits2/models/infer.py @@ -1,34 +1,81 @@ +from typing import Any, cast, Optional, Union + import torch -from typing import Optional +from numpy.typing import NDArray from style_bert_vits2.constants import Languages 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 SynthesizerTrn from style_bert_vits2.models.models_jp_extra import SynthesizerTrn as SynthesizerTrnJPExtra from style_bert_vits2.nlp import clean_text, cleaned_text_to_sequence, extract_bert_feature from style_bert_vits2.nlp.symbols import SYMBOLS -def get_net_g(model_path: str, version: str, device: str, hps): +def get_net_g(model_path: str, version: str, device: str, hps: HyperParameters): if version.endswith("JP-Extra"): logger.info("Using JP-Extra model") net_g = SynthesizerTrnJPExtra( - len(SYMBOLS), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model, + n_vocab = len(SYMBOLS), + spec_channels = hps.data.filter_length // 2 + 1, + segment_size = hps.train.segment_size // hps.data.hop_length, + n_speakers = hps.data.n_speakers, + # 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, ).to(device) else: logger.info("Using normal model") net_g = SynthesizerTrn( - len(SYMBOLS), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, + n_vocab = len(SYMBOLS), + spec_channels = hps.data.filter_length // 2 + 1, + segment_size = hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, - **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, ).to(device) net_g.state_dict() _ = net_g.eval() @@ -44,7 +91,7 @@ def get_net_g(model_path: str, version: str, device: str, hps): def get_text( text: str, language_str: Languages, - hps, + hps: HyperParameters, device: str, assist_text: Optional[str] = None, assist_text_weight: float = 0.7, @@ -111,15 +158,15 @@ def get_text( def infer( text: str, - style_vec, + style_vec: NDArray[Any], sdp_ratio: float, noise_scale: float, noise_scale_w: float, length_scale: float, sid: int, # In the original Bert-VITS2, its speaker_name: str, but here it's id language: Languages, - hps, - net_g, + hps: HyperParameters, + net_g: Union[SynthesizerTrn, SynthesizerTrnJPExtra], device: str, skip_start: bool = False, skip_end: bool = False, @@ -159,25 +206,25 @@ def infer( ja_bert = ja_bert.to(device).unsqueeze(0) en_bert = en_bert.to(device).unsqueeze(0) x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) - style_vec = torch.from_numpy(style_vec).to(device).unsqueeze(0) + style_vec_tensor = torch.from_numpy(style_vec).to(device).unsqueeze(0) del phones sid_tensor = torch.LongTensor([sid]).to(device) if is_jp_extra: - output = net_g.infer( + output = cast(SynthesizerTrnJPExtra, net_g).infer( x_tst, x_tst_lengths, sid_tensor, tones, lang_ids, ja_bert, - style_vec=style_vec, + style_vec=style_vec_tensor, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, ) else: - output = net_g.infer( + output = cast(SynthesizerTrn, net_g).infer( x_tst, x_tst_lengths, sid_tensor, @@ -186,7 +233,7 @@ def infer( bert, ja_bert, en_bert, - style_vec=style_vec, + style_vec=style_vec_tensor, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, @@ -209,110 +256,5 @@ def infer( return audio -def infer_multilang( - text: str, - style_vec, - sdp_ratio: float, - noise_scale: float, - noise_scale_w: float, - length_scale: float, - sid: int, - language: Languages, - hps, - net_g, - device: str, - skip_start: bool = False, - skip_end: bool = False, -): - bert, ja_bert, en_bert, phones, tones, lang_ids = [], [], [], [], [], [] - # emo = get_emo_(reference_audio, emotion, sid) - # if isinstance(reference_audio, np.ndarray): - # emo = get_clap_audio_feature(reference_audio, device) - # else: - # emo = get_clap_text_feature(emotion, device) - # emo = torch.squeeze(emo, dim=1) - for idx, (txt, lang) in enumerate(zip(text, language)): - _skip_start = (idx != 0) or (skip_start and idx == 0) - _skip_end = (idx != len(language) - 1) or skip_end - ( - temp_bert, - temp_ja_bert, - temp_en_bert, - temp_phones, - temp_tones, - temp_lang_ids, - ) = get_text(txt, lang, hps, device) # type: ignore - if _skip_start: - temp_bert = temp_bert[:, 3:] - temp_ja_bert = temp_ja_bert[:, 3:] - temp_en_bert = temp_en_bert[:, 3:] - temp_phones = temp_phones[3:] - temp_tones = temp_tones[3:] - temp_lang_ids = temp_lang_ids[3:] - if _skip_end: - temp_bert = temp_bert[:, :-2] - temp_ja_bert = temp_ja_bert[:, :-2] - temp_en_bert = temp_en_bert[:, :-2] - temp_phones = temp_phones[:-2] - temp_tones = temp_tones[:-2] - temp_lang_ids = temp_lang_ids[:-2] - bert.append(temp_bert) - ja_bert.append(temp_ja_bert) - en_bert.append(temp_en_bert) - phones.append(temp_phones) - tones.append(temp_tones) - lang_ids.append(temp_lang_ids) - bert = torch.concatenate(bert, dim=1) - ja_bert = torch.concatenate(ja_bert, dim=1) - en_bert = torch.concatenate(en_bert, dim=1) - phones = torch.concatenate(phones, dim=0) - tones = torch.concatenate(tones, dim=0) - lang_ids = torch.concatenate(lang_ids, dim=0) - with torch.no_grad(): - x_tst = phones.to(device).unsqueeze(0) - tones = tones.to(device).unsqueeze(0) - lang_ids = lang_ids.to(device).unsqueeze(0) - bert = bert.to(device).unsqueeze(0) - ja_bert = ja_bert.to(device).unsqueeze(0) - en_bert = en_bert.to(device).unsqueeze(0) - # emo = emo.to(device).unsqueeze(0) - x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) - del phones - speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) - audio = ( - net_g.infer( - x_tst, - x_tst_lengths, - speakers, - tones, - lang_ids, - bert, - ja_bert, - en_bert, - style_vec=style_vec, - sdp_ratio=sdp_ratio, - noise_scale=noise_scale, - noise_scale_w=noise_scale_w, - length_scale=length_scale, - )[0][0, 0] - .data.cpu() - .float() - .numpy() - ) - del ( - x_tst, - tones, - lang_ids, - bert, - x_tst_lengths, - speakers, - ja_bert, - en_bert, - ) # , emo - if torch.cuda.is_available(): - torch.cuda.empty_cache() - return audio - - class InvalidToneError(ValueError): pass diff --git a/style_bert_vits2/models/models.py b/style_bert_vits2/models/models.py index b4e1eb7..4efd100 100644 --- a/style_bert_vits2/models/models.py +++ b/style_bert_vits2/models/models.py @@ -983,10 +983,10 @@ class SynthesizerTrn(nn.Module): en_bert, style_vec, noise_scale=0.667, - length_scale=1, + length_scale=1.0, noise_scale_w=0.8, max_len=None, - sdp_ratio=0, + sdp_ratio=0.0, y=None, ): # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert) diff --git a/style_bert_vits2/models/models_jp_extra.py b/style_bert_vits2/models/models_jp_extra.py index 7bae8b4..3a43d51 100644 --- a/style_bert_vits2/models/models_jp_extra.py +++ b/style_bert_vits2/models/models_jp_extra.py @@ -1029,10 +1029,10 @@ class SynthesizerTrn(nn.Module): bert, style_vec, noise_scale=0.667, - length_scale=1, + length_scale=1.0, noise_scale_w=0.8, max_len=None, - sdp_ratio=0, + sdp_ratio=0.0, y=None, ): # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert) diff --git a/style_bert_vits2/models/utils.py b/style_bert_vits2/models/utils.py index 9a7947f..37ea269 100644 --- a/style_bert_vits2/models/utils.py +++ b/style_bert_vits2/models/utils.py @@ -355,45 +355,3 @@ def check_git_hash(model_dir): ) else: open(path, "w").write(cur_hash) - - -def get_hparams_from_file(config_path): - # print("config_path: ", config_path) - with open(config_path, "r", encoding="utf-8") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - return hparams - - -class HParams: - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = HParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() diff --git a/style_bert_vits2/tts_model.py b/style_bert_vits2/tts_model.py index bf04fe2..86c8425 100644 --- a/style_bert_vits2/tts_model.py +++ b/style_bert_vits2/tts_model.py @@ -1,11 +1,12 @@ import warnings from pathlib import Path -from typing import Optional, Union +from typing import Any, Optional, Union import gradio as gr import numpy as np import torch from gradio.processing_utils import convert_to_16_bit_wav +from numpy.typing import NDArray from style_bert_vits2.constants import ( DEFAULT_ASSIST_TEXT_WEIGHT, @@ -17,15 +18,22 @@ from style_bert_vits2.constants import ( DEFAULT_SPLIT_INTERVAL, DEFAULT_STYLE, DEFAULT_STYLE_WEIGHT, + Languages, ) -from style_bert_vits2.models import utils +from style_bert_vits2.models.hyper_parameters import HyperParameters from style_bert_vits2.models.infer import get_net_g, infer from style_bert_vits2.models.models import SynthesizerTrn from style_bert_vits2.models.models_jp_extra import SynthesizerTrn as SynthesizerTrnJPExtra from style_bert_vits2.logging import logger -def adjust_voice(fs, wave, pitch_scale, intonation_scale): +def adjust_voice( + fs: int, + wave: NDArray[Any], + pitch_scale: float, + intonation_scale: float, +) -> tuple[int, NDArray[Any]]: + if pitch_scale == 1.0 and intonation_scale == 1.0: # 初期値の場合は、音質劣化を避けるためにそのまま返す return fs, wave @@ -37,15 +45,17 @@ def adjust_voice(fs, wave, pitch_scale, intonation_scale): "pyworld is not installed. Please install it by `pip install pyworld`" ) - # pyworldでf0を加工して合成 - # pyworldよりもよいのがあるかもしれないが…… + # pyworld で f0 を加工して合成 + # pyworld よりもよいのがあるかもしれないが…… + ## pyworld は Cython で書かれているが、スタブファイルがないため型補完が全く効かない… wave = wave.astype(np.double) - f0, t = pyworld.harvest(wave, fs) - # 質が高そうだしとりあえずharvestにしておく - sp = pyworld.cheaptrick(wave, f0, t, fs) - ap = pyworld.d4c(wave, f0, t, fs) + # 質が高そうだしとりあえずharvestにしておく + f0, t = pyworld.harvest(wave, fs) # type: ignore + + sp = pyworld.cheaptrick(wave, f0, t, fs) # type: ignore + ap = pyworld.d4c(wave, f0, t, fs) # type: ignore non_zero_f0 = [f for f in f0 if f != 0] f0_mean = sum(non_zero_f0) / len(non_zero_f0) @@ -55,7 +65,7 @@ def adjust_voice(fs, wave, pitch_scale, intonation_scale): continue f0[i] = pitch_scale * f0_mean + intonation_scale * (f - f0_mean) - wave = pyworld.synthesize(f0, sp, ap, fs) + wave = pyworld.synthesize(f0, sp, ap, fs) # type: ignore return fs, wave @@ -67,7 +77,7 @@ class Model: self.config_path: Path = config_path self.style_vec_path: Path = style_vec_path self.device: str = device - self.hps: utils.HParams = utils.get_hparams_from_file(self.config_path) + self.hps: HyperParameters = HyperParameters.load_from_json(self.config_path) self.spk2id: dict[str, int] = self.hps.data.spk2id self.id2spk: dict[int, str] = {v: k for k, v in self.spk2id.items()} @@ -81,7 +91,7 @@ class Model: f"Number of styles ({self.num_styles}) does not match the number of style2id ({len(self.style2id)})" ) - self.style_vectors: np.ndarray = np.load(self.style_vec_path) + self.style_vectors: NDArray[Any] = np.load(self.style_vec_path) if self.style_vectors.shape[0] != self.num_styles: raise ValueError( f"The number of styles ({self.num_styles}) does not match the number of style vectors ({self.style_vectors.shape[0]})" @@ -97,7 +107,7 @@ class Model: hps=self.hps, ) - def get_style_vector(self, style_id: int, weight: float = 1.0) -> np.ndarray: + def get_style_vector(self, style_id: int, weight: float = 1.0) -> NDArray[Any]: mean = self.style_vectors[0] style_vec = self.style_vectors[style_id] style_vec = mean + (style_vec - mean) * weight @@ -105,7 +115,7 @@ class Model: def get_style_vector_from_audio( self, audio_path: str, weight: float = 1.0 - ) -> np.ndarray: + ) -> NDArray[Any]: from style_gen import get_style_vector xvec = get_style_vector(audio_path) @@ -116,7 +126,7 @@ class Model: def infer( self, text: str, - language: str = "JP", + language: Languages = Languages.JP, sid: int = 0, reference_audio_path: Optional[str] = None, sdp_ratio: float = DEFAULT_SDP_RATIO, @@ -133,7 +143,7 @@ class Model: given_tone: Optional[list[int]] = None, pitch_scale: float = 1.0, intonation_scale: float = 1.0, - ) -> tuple[int, np.ndarray]: + ) -> tuple[int, NDArray[Any]]: logger.info(f"Start generating audio data from text:\n{text}") if language != "JP" and self.hps.version.endswith("JP-Extra"): raise ValueError( @@ -146,6 +156,7 @@ class Model: if self.net_g is None: self.load_net_g() + assert self.net_g is not None if reference_audio_path is None: style_id = self.style2id[style] style_vector = self.get_style_vector(style_id, style_weight) @@ -246,19 +257,17 @@ class ModelHolder: continue self.model_files_dict[model_dir.name] = model_files self.model_names.append(model_dir.name) - hps = utils.get_hparams_from_file(config_path) + hps = HyperParameters.load_from_json(config_path) style2id: dict[str, int] = hps.data.style2id styles = list(style2id.keys()) spk2id: dict[str, int] = hps.data.spk2id speakers = list(spk2id.keys()) - self.models_info.append( - { - "name": model_dir.name, - "files": [str(f) for f in model_files], - "styles": styles, - "speakers": speakers, - } - ) + self.models_info.append({ + "name": model_dir.name, + "files": [str(f) for f in model_files], + "styles": styles, + "speakers": speakers, + }) def load_model(self, model_name: str, model_path_str: str): model_path = Path(model_path_str) @@ -291,9 +300,9 @@ class ModelHolder: speakers = list(self.current_model.spk2id.keys()) styles = list(self.current_model.style2id.keys()) return ( - gr.Dropdown(choices=styles, value=styles[0]), + gr.Dropdown(choices=styles, value=styles[0]), # type: ignore gr.Button(interactive=True, value="音声合成"), - gr.Dropdown(choices=speakers, value=speakers[0]), + gr.Dropdown(choices=speakers, value=speakers[0]), # type: ignore ) self.current_model = Model( model_path=model_path, @@ -304,21 +313,21 @@ class ModelHolder: speakers = list(self.current_model.spk2id.keys()) styles = list(self.current_model.style2id.keys()) return ( - gr.Dropdown(choices=styles, value=styles[0]), + gr.Dropdown(choices=styles, value=styles[0]), # type: ignore gr.Button(interactive=True, value="音声合成"), - gr.Dropdown(choices=speakers, value=speakers[0]), + gr.Dropdown(choices=speakers, value=speakers[0]), # type: ignore ) def update_model_files_gr(self, model_name: str) -> gr.Dropdown: model_files = self.model_files_dict[model_name] - return gr.Dropdown(choices=model_files, value=model_files[0]) + return gr.Dropdown(choices=model_files, value=model_files[0]) # type: ignore def update_model_names_gr(self) -> tuple[gr.Dropdown, gr.Dropdown, gr.Button]: self.refresh() initial_model_name = self.model_names[0] initial_model_files = self.model_files_dict[initial_model_name] return ( - gr.Dropdown(choices=self.model_names, value=initial_model_name), - gr.Dropdown(choices=initial_model_files, value=initial_model_files[0]), + gr.Dropdown(choices=self.model_names, value=initial_model_name), # type: ignore + gr.Dropdown(choices=initial_model_files, value=initial_model_files[0]), # type: ignore gr.Button(interactive=False), # For tts_button ) diff --git a/style_gen.py b/style_gen.py index d7f692f..ec0b507 100644 --- a/style_gen.py +++ b/style_gen.py @@ -8,6 +8,7 @@ from tqdm import tqdm from style_bert_vits2.logging import logger from style_bert_vits2.models import utils +from style_bert_vits2.models.hyper_parameters import HyperParameters from style_bert_vits2.utils.stdout_wrapper import SAFE_STDOUT from config import config @@ -72,7 +73,7 @@ if __name__ == "__main__": config_path = args.config num_processes = args.num_processes - hps = utils.get_hparams_from_file(config_path) + hps = HyperParameters.load_from_json(config_path) device = config.style_gen_config.device diff --git a/train_ms.py b/train_ms.py index 977b393..b2cb02b 100644 --- a/train_ms.py +++ b/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, diff --git a/train_ms_jp_extra.py b/train_ms_jp_extra.py index 3b1c01a..a464d29 100644 --- a/train_ms_jp_extra.py +++ b/train_ms_jp_extra.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_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 !!!")