From 8b250310fecfe61a6d9d53e8e5551851f4638d35 Mon Sep 17 00:00:00 2001 From: Axel Chemla--Romeu-Santos Date: Fri, 22 Dec 2023 00:52:17 +0100 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 50cf01d..a8e1c95 100644 --- a/README.md +++ b/README.md @@ -50,7 +50,7 @@ Training a RAVE model usually involves 3 separate steps, namely _dataset prepara You can know prepare a dataset using two methods: regular and lazy. Lazy preprocessing allows RAVE to be trained directly on the raw files (i.e. mp3, ogg), without converting them first. **Warning**: lazy dataset loading will increase your CPU load by a large margin during training, especially on Windows. This can however be useful when training on large audio corpus which would not fit on a hard drive when uncompressed. In any case, prepare your dataset using ```bash -rave preprocess --input_path /audio/folder --output_path /dataset/path (--lazy) +rave preprocess --input_path /audio/folder --output_path /dataset/path --channels X (--lazy) ``` ### Training