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+# Introduction
+
+This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. 
+A transcription is provided for each clip. 
+Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.
+
+The texts were published between 1884 and 1964, and are in the public domain. 
+The audio was recorded in 2016-17 by the [LibriVox](https://librivox.org/) project and is also in the public domain.
+
+The above information is from the [LJSpeech website](https://keithito.com/LJ-Speech-Dataset/).
+
+# VITS
+
+This recipe provides a VITS model trained on the LJSpeech dataset.
+
+Pretrained model can be found [here](https://huggingface.co/Zengwei/icefall-tts-ljspeech-vits-2024-02-28).
+
+For tutorial and more details, please refer to the [VITS documentation](https://k2-fsa.github.io/icefall/recipes/TTS/ljspeech/vits.html).
+
+The training command is given below:
+```
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+./vits/train.py \
+  --world-size 4 \
+  --num-epochs 1000 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --exp-dir vits/exp \
+  --max-duration 500
+```
+
+To inference, use:
+```
+./vits/infer.py \
+  --exp-dir vits/exp \
+  --epoch 1000 \
+  --tokens data/tokens.txt
+```
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+# Introduction
+
+This CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive. 
+The newspaper texts were taken from Herald Glasgow, with permission from Herald & Times Group. Each speaker has a different set of the newspaper texts selected based a greedy algorithm that increases the contextual and phonetic coverage. 
+The details of the text selection algorithms are described in the following paper: [C. Veaux, J. Yamagishi and S. King, "The voice bank corpus: Design, collection and data analysis of a large regional accent speech database,"](https://doi.org/10.1109/ICSDA.2013.6709856).
+
+The above information is from the [CSTR VCTK website](https://datashare.ed.ac.uk/handle/10283/3443).
+
+# VITS
+
+This recipe provides a VITS model trained on the VCTK dataset.
+
+Pretrained model can be found [here](https://huggingface.co/zrjin/icefall-tts-vctk-vits-2023-12-05), note that this model was pretrained on the Edinburgh DataShare VCTK dataset.
+
+For tutorial and more details, please refer to the [VITS documentation](https://k2-fsa.github.io/icefall/recipes/TTS/vctk/vits.html).
+
+The training command is given below:
+```
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+./vits/train.py \
+  --world-size 4 \
+  --num-epochs 1000 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --exp-dir vits/exp \
+  --tokens data/tokens.txt
+  --max-duration 350
+```
+
+To inference, use:
+```
+./vits/infer.py \
+  --epoch 1000 \
+  --exp-dir vits/exp \
+  --tokens data/tokens.txt \
+  --max-duration 500
+```
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