Update README.md

README.md

@@ -59,11 +59,63 @@ The model is available for use in the NeMo toolkit [4], and can be used as a pre
 
 
 
-## Training Datasets
-The Low Frame-rate Speech Codec is trained on a total of 28.7k hrs of speech data from 105 languages. For training our model we have used [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0)  and an English subset of MLS dataset.  The Common Voice derived training set comprises 105 languages, totaling 2.7 million utterances, and 3.2k hours
+
+## Training, Testing, and Evaluation Datasets:
+The Low Frame-rate Speech Codec is trained on a total of 28.7k hrs of speech data from 105 languages.
+
+For training our model we have used [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0)  and an English subset of MLS dataset.  The Common Voice derived training set comprises 105 languages, totaling 2.7 million utterances, and 3.2k hours
 of audio from about one-hundred thousand speakers. The [MLS English](https://www.openslr.org/94/) training dataset consists of 6.2 million utterances and 25.5k hours of audio from 4329 speakers. =
 
 
+### Training Datasets
+The Low Frame-rate Speech Codec is trained on a total of 28.7k hrs of speech data from 105 languages.
+
+  - [MLS English](https://www.openslr.org/94/) [25.5k]
+  
+      - Data Collection Method: by Human
+      
+      - Labeling Method: Automated
+  
+  -  [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0)[3.2k]
+  
+      - Data Collection Method: by Human
+      
+      - Labeling Method: by Human
+  
+  
+
+### Evaluation Datasets
+
+  - [MLS English](https://www.openslr.org/94/)
+  
+      - Data Collection Method: by Human
+      
+      - Labeling Method: Automated
+  
+  -  [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0)
+  
+      - Data Collection Method: by Human
+      
+      - Labeling Method: by Human
+
+### Test Datasets
+  
+  - [MLS](https://www.openslr.org/94/)
+
+    - Data Collection Method: by Human
+    
+    - Labeling Method: Automated
+   
+    - Properties: We randomly selected 200 samples from each of the eight languages in the 44kHz MLS dataset. For more details, please refer to [our paper](https://arxiv.org/abs/2409.12117).
+
+  - [DAPS](https://zenodo.org/records/4660670)
+  
+      - Data Collection Method: by Human
+      
+      - Labeling Method: Automated
+   
+      - Properties: To assess our models' performance on studio-quality audio, we utilized the F10 and M10 speakers from the DAPS Clear dataset. These speakers were also employed in the evaluation of the [DAC model](https://arxiv.org/abs/2306.06546).
+
 ## Software Integration
 
 ### Supported Hardware Microarchitecture Compatibility: