Update README.md

README.md

@@ -149,16 +149,14 @@ It is a "large" versions of Conformer-CTC (around 120M parameters) model.
 
 ## NVIDIA Riva: Deployment
 
-This model can be efficiently (best latency and throughput) deployed with [NVIDIA Riva](https://developer.nvidia.com/riva), a GPU-accelerated speech AI SDK, on-premises, embedded, on the edge or with any cloud provider.
+For the best real-time accuracy, latency, and throughput, deploy the model with [NVIDIA Riva](https://developer.nvidia.com/riva), an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded. 
 
-Additionally, with RIVA you get:
-* Customization to achieve best WER 
-  * Ability to boost specific words (e.g. brand and product names)
-  * External language model, punctuation and captialization
-  * Conformer checkpoints trained on proprietary data
-* Streaming speech recognition mode
+Additionally, Riva provides: 
+* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours 
+* Best in class accuracy via customization with run-time word boosting (e.g., brand and product names), acoustic model training, language model training, and inverse text normalization customizations 
+* Streaming speech recognition, Kubernetes compatible scaling, and Enterprise-grade support 
 
-[Live Riva demo](https://developer.nvidia.com/riva#demos)  
+[Check out Riva live demo.](https://developer.nvidia.com/riva#demos)  
 
 ## NVIDIA NeMo: Training
 
@@ -191,7 +189,9 @@ asr_model.transcribe(['2086-149220-0033.wav'])
 ### Transcribing many audio files
 
 ```shell
-python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \n pretrained_name="nvidia/stt_en_conformer_ctc_large" \n audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
+python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
+ pretrained_name="nvidia/stt_en_conformer_ctc_large" 
+ audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
 ```
 
 ### Input