Update README.md

README.md

@@ -21,20 +21,25 @@ model-index:
   results: []
 
 ---
+# This speech tagger performs transcription, annotates entities, predict speaker emotion 
 
+Model is suitable for voiceAI applications, real-time and offline.
 
-## ASR+NL Model Overview
+## Model Details
 
-Recoganize begin and end of English Named Entities, get speaker emotion and also transcribe
+- **Model type**: NeMo ASR
+- **Architecture**: Conformer CTC
+- **Language**: English
+- **Training data**: CommonVoice, Gigaspeech
+- **Performance metrics**: [Metrics]
 
-## NVIDIA NeMo: Training
+## Usage
 
-To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
-```
-pip install nemo_toolkit['all']
-``` 
+To use this model, you need to install the NeMo library:
 
-## How to Use this Model
+```bash
+pip install nemo_toolkit
+```
 
 ### How to run
 
@@ -42,7 +47,7 @@ pip install nemo_toolkit['all']
 import nemo.collections.asr as nemo_asr
 
 # Step 1: Load the ASR model from Hugging Face
-model_name = 'WhissleAI/speech-tagger_en_ner-emotion'
+model_name = 'WhissleAI/speech-tagger_en_ner_emotion'
 asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name)
 
 # Step 2: Provide the path to your audio file