From e056ad9ec22cbaa119f7c93cb60b5b8851e80a7e Mon Sep 17 00:00:00 2001 From: Lordmau5 Date: Mon, 10 Apr 2023 12:45:24 +0200 Subject: [PATCH] fix(readme): fix wrong commands in "Before training" (#280) --- README.md | 4 ++-- README_zh_CN.md | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 9fb371f9..46ef10a6 100644 --- a/README.md +++ b/README.md @@ -152,8 +152,8 @@ svc infer source.wav #### Before training - If your dataset has BGM, please remove the BGM using software such as [Ultimate Vocal Remover](https://ultimatevocalremover.com/). `3_HP-Vocal-UVR.pth` or `UVR-MDX-NET Main` is recommended. [^1] -- If your dataset is a long audio file with a single speaker, use `svc split` to split the dataset into multiple files (using `librosa`). -- If your dataset is a long audio file with multiple speakers, use `svc sd` to split the dataset into multiple files (using `pyannote.audio`). Further manual classification may be necessary due to accuracy issues. If speakers speak with a variety of speech styles, set --min-speakers larger than the actual number of speakers. Due to unresolved dependencies, please install `pyannote.audio` manually: `pip install pyannote-audio`. +- If your dataset is a long audio file with a single speaker, use `svc pre-split` to split the dataset into multiple files (using `librosa`). +- If your dataset is a long audio file with multiple speakers, use `svc pre-sd` to split the dataset into multiple files (using `pyannote.audio`). Further manual classification may be necessary due to accuracy issues. If speakers speak with a variety of speech styles, set --min-speakers larger than the actual number of speakers. Due to unresolved dependencies, please install `pyannote.audio` manually: `pip install pyannote-audio`. [^1]: https://ytpmv.info/how-to-use-uvr/ diff --git a/README_zh_CN.md b/README_zh_CN.md index 67ac2a23..8eb16b5b 100644 --- a/README_zh_CN.md +++ b/README_zh_CN.md @@ -123,10 +123,10 @@ svc --model-path source.wav - 如果数据集有 BGM,请用例如[Ultimate Vocal Remover](https://ultimatevocalremover.com/)等软件去除 BGM. 推荐使用`3_HP-Vocal-UVR.pth` 或者 `UVR-MDX-NET Main` . [^1] -- 如果数据集是包含多个歌手的长音频文件, 使用 `svc sd` 将数据集拆分为多个文件 (使用 `pyannote.audio`) +- 如果数据集是包含多个歌手的长音频文件, 使用 `svc pre-sd` 将数据集拆分为多个文件 (使用 `pyannote.audio`) 。为了提高准确率，可能需要手动进行分类。如果歌手的声线多样,请把 --min-speakers 设置为大于实际说话者数量. 如果出现依赖未安装, 请通过 `pip install pyannote-audio`来安装 `pyannote.audio`。 -- 如果数据集是包含单个歌手的长音频文件, 使用 `svc split` 将数据集拆分为多个文件 (使用 `librosa`). +- 如果数据集是包含单个歌手的长音频文件, 使用 `svc pre-split` 将数据集拆分为多个文件 (使用 `librosa`). [^1]: https://ytpmv.info/how-to-use-uvr/