Update README.md

README.md

@@ -64,21 +64,21 @@ img {
 | [![Model size](https://img.shields.io/badge/Params-114M-lightgrey#model-badge)](#model-architecture)
 | [![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets)
 
-This collection contains large size versions of cache-aware FastConformer-Hybrid (around 114M parameters) with multiple look-ahead support, trained on a large scale english speech. 
-These models are trained for streaming ASR which be used for streaming applications with a variety of latencies (0ms, 80ms, 480s, 1040ms).
+This collection contains large-size versions of cache-aware FastConformer-Hybrid (around 114M parameters) with multiple look-ahead support, trained on a large scale english speech. 
+These models are trained for streaming ASR, which be used for streaming applications with a variety of latencies (0ms, 80ms, 480s, 1040ms).
 
 
 ## Model Architecture
 
-These models are cache-aware versions of Hybrid FastConfomer which are trianed for streaming ASR. You may find more info on cache-aware models here: [Cache-aware Streaming Conformer](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#cache-aware-streaming-conformer).
+These models are cache-aware versions of Hybrid FastConfomer which are trained for streaming ASR. You may find more info on cache-aware models here: [Cache-aware Streaming Conformer](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#cache-aware-streaming-conformer).
 The models are trained with multiple look-aheads which makes the model to be able to support different latencies. 
-To learn on how to switch between different look-aheads, you may read the documentations on the cache-aware models.
+To learn on how to switch between different look-ahead, you may read the documentation on the cache-aware models.
 
 FastConformer [4] is an optimized version of the Conformer model [1], and 
 you may find more information on the details of FastConformer here: [Fast-Conformer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer). 
 
 The model is trained in a multitask setup with joint Transducer and CTC decoder loss. You can find more about Hybrid Transducer-CTC training here: [Hybrid Transducer-CTC](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#hybrid-transducer-ctc). 
-You may also find more on how to switch between the Transducer and CTC decoders in the documentations.
+You may also find more on how to switch between the Transducer and CTC decoders in the documentation.
 
  
 
@@ -130,15 +130,11 @@ You will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recom
 ```
 pip install nemo_toolkit['all']
 '''
-'''
-(if it causes an error): 
-pip install nemo_toolkit[all]
-``` 
 
 ### Simulate Streaming ASR
 
 You may use this script to simulate streaming ASR with these models: [cache-aware streaming simulation](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py). 
-You may use --att_context_size to set the context size otherwise the default which is the first context size in the list (1040ms) is going to be used.
+You may use --att_context_size to set the context size otherwise, the default, which is the first context size in the list (1040ms), is going to be used.
 
 ### Transcribing using Python
 Cache-aware models are designed in a way that the model's predictions are the same in both offline and streaming mode.