diff --git a/spectra/readme.md b/spectra/readme.md
index 685bde88..37a498ab 100644
--- a/spectra/readme.md
+++ b/spectra/readme.md
@@ -4,19 +4,56 @@ pip install -r requirements.txt
 ```
 If you want to use apex for AMP training, please clone the apex source code from the repository at github.com to install.
 
+## Pre-Training
+To pretrain our model from scratch, please first download our processed pretraining dataset [Spotify-100k](https://space-mm-data.oss-cn-wulanchabu.aliyuncs.com/spotify.tgz) 
+
+> Warning: The spotify.tgz file is 96GB with 960 hours of audio (*.npy). Use download tools like aria2 that can pause and resume to download it efficiently
+
+Then, download pre-trained WavLM and RoBERTa models from huggingface.co (optional), and run `scripts/train-960.sh`
+
+#### PT dataset description
+The `transForV4.pkl` file includes a dataset of 358,268 audio-text pairs, each less than 10 seconds in duration, complete with precise timestamp alignment details. Here's what an example entry looks like:
+
+```
+['/<directory>/spotify-960/0/0/show_002B8PbILr169CdsS9ySTH/0.npy',
+ [0, 405, 18, 627, 19456, 1644, 102, 43264, 4, 3056, 6025, 7325, 3479, 254, 5652, 10162, 4, 2678, 4783, 2],
+ ["it's", 1, 3, 0, 8000],
+ ['the', 3, 4, 6400, 9600],
+ ['mother', 4, 5, 8000, 14400],
+ ['back', 5, 6, 12800, 17600],
+ ['a', 6, 7, 16000, 19200],
+ ['podcast.', 7, 9, 17600, 35200],
+ ['well', 9, 10, 88000, 99200],
+ ['that', 10, 11, 100800, 105600],
+ ['was', 11, 12, 104000, 110400],
+ ['longer', 12, 14, 108800, 115200],
+ ['than', 14, 15, 113600, 118400],
+ ['expected.', 15, 17, 116800, 132800],
+ ['oh', 17, 18, 145600, 148800],
+ ['my', 18, 19, 147200, 152000],
+ -1]
+```
+
+Each dataset entry consists of four parts:
+1. **Audio File Path**: The location of the audio file in .npy format.
+2. **Text IDs**: Tokens generated by RoBERTa corresponding to the text.
+3. **Previous Turn Index**: The last element indicating the reference to the previous turn's ID, with `-1` indicating no prior reference.
+4. **Audio-Text Alignment**: Detailed information on how text aligns with audio segments. For instance, the word "it's" aligns with text IDs [1:3] and audio segment [0:8000], where the sample rate of 16000 Hz means this segment represents approximately 0.5 seconds of audio.
+
+
 ## Fine-tuning
-We provide the pre-trained checkpoint of our model at [huggingface.co](https://huggingface.co/publicstaticvo/SPECTRA-base). To reproduce our result in the paper, please first download the pre-processed fine-tuning data (be available soon), then run `scripts/finetune.sh`
-### Datasets
-Here are the processed fine-tuning data datasets: 
-[**MOSI**](https://space-mm-data.oss-cn-wulanchabu.aliyuncs.com/downstreamv2/mosi.tgz), 
+We provide the pre-trained checkpoint of our model at [huggingface.co](https://huggingface.co/publicstaticvo/SPECTRA-base). To reproduce our result in the paper, please first download the pre-processed fine-tuning data: [**MOSI**](https://space-mm-data.oss-cn-wulanchabu.aliyuncs.com/downstreamv2/mosi.tgz), 
 [**MOSEI**](https://space-mm-data.oss-cn-wulanchabu.aliyuncs.com/downstreamv2/mosei.tgz), 
 [**IEMOCAP**](https://space-mm-data.oss-cn-wulanchabu.aliyuncs.com/downstreamv2/iemocap.tgz), and 
 [**MINTREC**](https://space-mm-data.oss-cn-wulanchabu.aliyuncs.com/downstreamv2/mintrec.tgz). 
-These are all composed by pickles and can be used directly. 
+These are all composed by pickles and can be used directly. Then run 
+```
+scripts/finetune.sh
+```
 
-> Due to the large data size of SpokenWOZ and Spotify-100k (tens of GBs), please obtain from the original repo."
+> Since SpokenWOZ data is large and constantly updated, please obtain it from the [source](https://spokenwoz.github.io/SpokenWOZ-github.io/).
 
-### Usage
+#### FT dataset description
 To access the training, validation, and test files in the datasets, you can use the following command to extract the mosi.tgz file:
 
 ```
@@ -30,19 +67,4 @@ Once extracted, you'll find .pkl files for training, validation, and testing. Ea
 4. History Audio Features (if applicable): If present, this field contains historical audio feature data.
 5. History Text Token IDs (if applicable): Similar to the above, this includes historical text token IDs, if available.
 
-We hope this information helps you in utilizing the dataset effectively. Should you have any questions or need further assistance, please feel free to reach out.
-
-## Pre-Train
-To pretrain our model from scratch, please first download our processed pretraining dataset (be available soon), then download pre-trained WavLM and RoBERTa models from huggingface.co (optional), and run `scripts/train-960.sh`
-
-<!--
-```commandline
-python run_dst.py --model spectra --model_type roberta \
-    --data_dir ./data \
-    --model_dir /PATH/OF/YOUR/PRETRAINED/SPECTRA/MODEL \
-    --output_dir ./result \
-    --dataset_config ./data/spokenwoz_config.json \
-    --per_gpu_train_batch_size 2 \
-    --accum 4
-```
--->
+We hope these information helps you in utilizing the dataset effectively. Should you have any questions or need further assistance, please feel free to reach out.