RVCの特徴量ファイル（total_fea.npyとadded～.index）を指定したフォルダにモデル名で書き出すスクリプト。
VENV_DIRmodel_path output_pathを設定する。
batとpyを同じフォルダに置いて、batを実行すると設定したvenvでpyを実行する。モデル名を訊いてくるので入力。
output_path に 「モデル名.npy」「モデル名.index」が書き出される。
| @echo off | |
| set PYTHON= | |
| set GIT= | |
| @REM venvのパスを指定 | |
| set VENV_DIR=G:\venv\RVC\.venv | |
| set COMMANDLINE_ARGS= | |
| if not defined PYTHON (set PYTHON=python) | |
| set ERROR_REPORTING=FALSE | |
| :start_venv | |
| if ["%VENV_DIR%"] == ["-"] goto :launch | |
| :activate_venv | |
| set PYTHON="%VENV_DIR%\Scripts\Python.exe" | |
| echo venv %PYTHON% | |
| :launch | |
| %PYTHON% CreateFeatureFiles.py %* | |
| pause | |
| exit /b | |
| :show_stdout_stderr | |
| echo. | |
| echo exit code: %errorlevel% | |
| pause |
| import os | |
| import faiss | |
| import numpy as np | |
| #トレーニングフォルダ | |
| model_path = r"G:\venv\RVC\rvc-webui\models\training\models" | |
| #書き出すフォルダ | |
| output_path = r"G:\venv\RVC\subdata" | |
| def make(training_dir, output_path, model_name): | |
| feature_dir = os.path.join(training_dir, "3_feature256") | |
| npys = [] | |
| listdir_res = list(os.listdir(feature_dir)) | |
| for name in sorted(listdir_res): | |
| phone = np.load(os.path.join(feature_dir, name)) | |
| npys.append(phone) | |
| big_npy = np.concatenate(npys, 0) | |
| # np.save(os.path.join(training_dir, "total_fea.npy"), big_npy) | |
| np.save(os.path.join(output_path, model_name + ".npy"), big_npy) | |
| n_ivf = big_npy.shape[0] // 39 | |
| index = faiss.index_factory(256, f"IVF{n_ivf},Flat") | |
| index_ivf = faiss.extract_index_ivf(index) | |
| index_ivf.nprobe = int(np.power(n_ivf, 0.3)) | |
| index.train(big_npy) | |
| index.add(big_npy) | |
| faiss.write_index( | |
| index, | |
| # os.path.join(training_dir, f"added_IVF{n_ivf}_Flat_nprobe_{index_ivf.nprobe}.index"), | |
| os.path.join(output_path, model_name + ".index"), | |
| ) | |
| print("CreateFeatureFiles.py") | |
| model_name = input("target model name:") | |
| print(model_name) | |
| training_dir = os.path.join(model_path, model_name) | |
| if os.path.exists(training_dir): | |
| make(training_dir, output_path, model_name) | |
| print("done") | |
| else: | |
| print("error: model dir not found") |