Update README.md

README.md

@@ -111,17 +111,29 @@ img {
 | [![Model size](https://img.shields.io/badge/Params-123M-lightgrey#model-badge)](#model-architecture)
 <!-- | [![Language](https://img.shields.io/badge/Language-multilingual-lightgrey#model-badge)](#datasets) -->
 
-[Sortformer](https://arxiv.org/abs/2409.06656)[1] is a novel end-to-end neural model for speaker diarization, trained with unconventional objectives compared to existing end-to-end diarization models.
+This model is a streaming version of Sortformer diarizer. [Sortformer](https://arxiv.org/abs/2409.06656)[1] is a novel end-to-end neural model for speaker diarization, trained with unconventional objectives compared to existing end-to-end diarization models.
 
 <div align="center">
     <img src="sortformer_intro.png" width="750" />
 </div>
 
+Streaming Sortformer approach employs an Arrival-Order Speaker Cache (AOSC) to store frame-level acoustic embeddings of previously observed speakers.
+<div align="center">
+    <img src="streaming_sortformer_ani.gif" width="1400" />
+</div>
+
 Sortformer resolves permutation problem in diarization following the arrival-time order of the speech segments from each speaker. 
 
 ## Model Architecture
 
-Sortformer consists of an L-size (18 layers) [NeMo Encoder for
+Streaming sortformer employs pre-encode layer in the Fast-Conformer to generate speaker-cache. At each step, speaker cache is filtered to only retain the high-quality speaker cache vectors.
+
+<div align="center">
+    <img src="streaming_steps.png" width="1400" />
+</div>
+
+
+Aside from speaker-cache management part, streaming Sortformer follows the architecture of the offline version of Sortformer. Sortformer consists of an L-size (18 layers) [NeMo Encoder for
 Speech Tasks (NEST)](https://arxiv.org/abs/2408.13106)[2] which is based on [Fast-Conformer](https://arxiv.org/abs/2305.05084)[3] encoder. Following that, an 18-layer Transformer[4] encoder with hidden size of 192, 
 and two feedforward layers with 4 sigmoid outputs for each frame input at the top layer. More information can be found in the [Sortformer paper](https://arxiv.org/abs/2409.06656)[1].
 
@@ -130,9 +142,7 @@ and two feedforward layers with 4 sigmoid outputs for each frame input at the to
 </div>
 
 
-<div align="center">
-    <img src="streaming_sortformer_ani.gif" width="450" />
-</div>
+
 
 ## NVIDIA NeMo