From d05a46356ee9f85050a843c1f257cd53c9b08dc6 Mon Sep 17 00:00:00 2001
From: "Aaron (Yinghao) Li" <71044569+yl4579@users.noreply.github.com>
Date: Tue, 5 Dec 2023 02:52:53 -0500
Subject: [PATCH] Update README.md

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index a14887da..83d5c555 100644
--- a/README.md
+++ b/README.md
@@ -79,17 +79,17 @@ The script is modified from `train_second.py` which uses DP, as DDP does not wor
 ```bash
 python train_finetune.py --config_path ./Configs/config_ft.yml
 ```
-Please make sure you have the LibriTTS checkpoint downloaded and unzipped under the folder. The default configuration `config_ft.yml` finetunes on LJSpeech with 1 hour of speech data (around 1k samples) for 50 epochs. This took about 4 hours to finish on four NVidia A100. The quality is slightly worse (similar to NaturalSpeech on LJSpeech) than LJSpeech model trained from scratch with 24 hours of speech data, which took around 2.5 days to finish on four A100. The samples can be found at [#65 (comment)](https://github.com/yl4579/StyleTTS2/discussions/65#discussioncomment-7668393).
+Please make sure you have the LibriTTS checkpoint downloaded and unzipped under the folder. The default configuration `config_ft.yml` finetunes on LJSpeech with 1 hour of speech data (around 1k samples) for 50 epochs. This took about 4 hours to finish on four NVidia A100. The quality is slightly worse (similar to NaturalSpeech on LJSpeech) than LJSpeech model trained from scratch with 24 hours of speech data, which took around 2.5 days to finish on four A100. The samples can be found at [#65 (comment)](https://github.com/yl4579/StyleTTS2/discussions/65#discussioncomment-7668393). 
 
 If you are using a **single GPU** (because the script doesn't work with DDP) and want to save training speed and VRAM, you can do (thank [@korakoe](https://github.com/korakoe) for making the script at [#100](https://github.com/yl4579/StyleTTS2/pull/100)):
 ```bash
 accelerate launch --mixed_precision=fp16 --num_processes=1 train_finetune_accelerate.py --config_path ./Configs/config_ft.yml
 ```
-
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yl4579/StyleTTS2/blob/main/Colab/StyleTTS2_Finetune_Demo.ipynb)
 
 ### Common Issues
-[@Kreevoz](https://github.com/Kreevoz) has made detailed notes on common issues in finetuning, with suggestions in maximizing audio quality: [#81](https://github.com/yl4579/StyleTTS2/discussions/81). Some of these also apply to training from scratch. 
+[@Kreevoz](https://github.com/Kreevoz) has made detailed notes on common issues in finetuning, with suggestions in maximizing audio quality: [#81](https://github.com/yl4579/StyleTTS2/discussions/81). Some of these also apply to training from scratch. [@IIEleven11](https://github.com/IIEleven11) has also made a guideline for fine-tuning: [#128](https://github.com/yl4579/StyleTTS2/discussions/128).
+
 - **Out of memory after `joint_epoch`**: This is likely because your GPU RAM is not big enough for SLM adversarial training run. You may skip that but the quality could be worse. Setting `joint_epoch` a larger number than `epochs` could skip the SLM advesariral training.
 
 ## Inference