開発環境
モデルの構造確認
以下のコードでモデルの構造を確認できます
import torch from pathlib import Path import json from safetensors import safe_open import argparse def load_model(model_path): if model_path.suffix == '.safetensors': with safe_open(model_path, framework="pt", device="cpu") as f: return {key: f.get_tensor(key) for key in f.keys()} elif model_path.suffix == '.pth': return torch.load(model_path, map_location='cpu') else: raise ValueError(f"Unsupported file format: {model_path.suffix}") def analyze_model_structure(model_dict): sizes = set() important_shapes = {} for name, param in model_dict.items(): if isinstance(param, torch.Tensor): if len(param.shape) > 0: sizes.add(param.shape[-1]) if param.shape[-1] in [256, 512]: important_shapes[name] = param.shape return sizes, important_shapes def analyze_models(model_paths, config_path): config_path = Path(config_path) # configファイルを読み込む with open(config_path, 'r') as f: config = json.load(f) all_sizes = set() all_important_shapes = {} for model_path in model_paths: model_path = Path(model_path) print(f"\nAnalyzing {model_path.name}:") model_dict = load_model(model_path) if 'model' in model_dict: model_dict = model_dict['model'] sizes, important_shapes = analyze_model_structure(model_dict) all_sizes.update(sizes) all_important_shapes.update(important_shapes) print(f"Unique sizes found: {sorted(sizes)}") print("Important shapes (256 or 512):") for name, shape in important_shapes.items(): print(f" {name}: shape = {shape}") print("\nOverall summary:") print(f"All unique sizes found across models: {sorted(all_sizes)}") print("\nImportant config information:") print(f"Model name: {config.get('model_name', 'Not specified')}") print(f"Version: {config.get('version', 'Not specified')}") print(f"Gin channels: {config['model'].get('gin_channels', 'Not specified')}") print(f"Hidden channels: {config['model'].get('hidden_channels', 'Not specified')}") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Analyze VITS2 model files") parser.add_argument("config_path", type=str, help="Path to the config.json file") parser.add_argument("model_paths", type=str, nargs='+', help="Paths to the model files (.pth or .safetensors)") args = parser.parse_args() analyze_models(args.model_paths, args.config_path)
以下のようにモデルの構造を確認することができます
python .\analyze_vits2_model_structure.py .\model_assets\test\config.json .\model_assets\test\test_e1000_s25000.safetensors Analyzing test_e1000_s25000.safetensors: Unique sizes found: [1, 2, 3, 5, 7, 8, 11, 16, 29, 32, 64, 96, 128, 192, 256, 384, 512, 768] Important shapes (256 or 512): dec.cond.bias: shape = torch.Size([512]) dec.conv_pre.bias: shape = torch.Size([512]) dec.resblocks.0.convs1.0.bias: shape = torch.Size([256]) dec.resblocks.0.convs1.1.bias: shape = torch.Size([256]) dec.resblocks.0.convs1.2.bias: shape = torch.Size([256]) dec.resblocks.0.convs2.0.bias: shape = torch.Size([256]) dec.resblocks.0.convs2.1.bias: shape = torch.Size([256]) dec.resblocks.0.convs2.2.bias: shape = torch.Size([256]) dec.resblocks.1.convs1.0.bias: shape = torch.Size([256]) dec.resblocks.1.convs1.1.bias: shape = torch.Size([256]) dec.resblocks.1.convs1.2.bias: shape = torch.Size([256]) dec.resblocks.1.convs2.0.bias: shape = torch.Size([256]) dec.resblocks.1.convs2.1.bias: shape = torch.Size([256]) dec.resblocks.1.convs2.2.bias: shape = torch.Size([256]) dec.resblocks.2.convs1.0.bias: shape = torch.Size([256]) dec.resblocks.2.convs1.1.bias: shape = torch.Size([256]) dec.resblocks.2.convs1.2.bias: shape = torch.Size([256]) dec.resblocks.2.convs2.0.bias: shape = torch.Size([256]) dec.resblocks.2.convs2.1.bias: shape = torch.Size([256]) dec.resblocks.2.convs2.2.bias: shape = torch.Size([256]) dec.ups.0.bias: shape = torch.Size([256]) dp.conv_1.bias: shape = torch.Size([256]) dp.conv_2.bias: shape = torch.Size([256]) dp.norm_1.beta: shape = torch.Size([256]) dp.norm_1.gamma: shape = torch.Size([256]) dp.norm_2.beta: shape = torch.Size([256]) dp.norm_2.gamma: shape = torch.Size([256]) emb_g.weight: shape = torch.Size([1, 512]) enc_p.encoder.spk_emb_linear.weight: shape = torch.Size([192, 512]) enc_p.style_proj.weight: shape = torch.Size([192, 256]) flow.flows.0.enc.spk_emb_linear.weight: shape = torch.Size([192, 512]) flow.flows.2.enc.spk_emb_linear.weight: shape = torch.Size([192, 512]) flow.flows.4.enc.spk_emb_linear.weight: shape = torch.Size([192, 512]) flow.flows.6.enc.spk_emb_linear.weight: shape = torch.Size([192, 512]) Overall summary: All unique sizes found across models: [1, 2, 3, 5, 7, 8, 11, 16, 29, 32, 64, 96, 128, 192, 256, 384, 512, 768] Important config information: Model name: nadeko Version: 2.4.1-JP-Extra Gin channels: 512 Hidden channels: 192
