Update README.md (#1)

- Update README.md (6b85e86a3ced40934391192b5a23a8233fba4d8d)


Co-authored-by: Vitaly Lavrukhin <vlavrukhin@users.noreply.huggingface.co>

README.md

@@ -179,13 +179,13 @@ img {
 | [![Language](https://img.shields.io/badge/Language-en-lightgrey#model-badge)](#datasets)
 
 
-parakeet-rnnt-1.1b is an ASR model that transcribes speech in lower case English alphabet. This model is jointly developed by [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) team and [Suno.ai](https://www.suno.ai/).
-It is a "extra extra large" version of FastConformer Transducer[1] (around 1.1B parameters) model.
+parakeet-rnnt-1.1b is an ASR model that transcribes speech in lower case English alphabet. This model is jointly developed by [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) and [Suno.ai](https://www.suno.ai/) teams.
+It is an XXL version of FastConformer Transducer [1] (around 1.1B parameters) model.
 See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer) for complete architecture details.
 
 ## NVIDIA NeMo: Training
 
-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.
+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']
 ``` 
@@ -221,7 +221,7 @@ python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py
 
 ### Input
 
-This model accepts 16000 Hz Mono-channel Audio (wav files) as input.
+This model accepts 16000 Hz mono-channel audio (wav files) as input.
 
 ### Output
 
@@ -241,7 +241,7 @@ The tokenizers for these models were built using the text transcripts of the tra
 
 The model was trained on 65K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams.
 
-Dataset contains following Public English speech sets (25K hrs)
+Dataset contains following Public English speech sets (25K hours)
 
 - Librispeech 960 hours of English speech
 - Fisher Corpus
@@ -251,9 +251,9 @@ Dataset contains following Public English speech sets (25K hrs)
 - VCTK
 - VoxPopuli (EN)
 - Europarl-ASR (EN)
-- Multilingual Librispeech (MLS EN) - 2,000 hrs subset
+- Multilingual Librispeech (MLS EN) - 2,000 hour subset
 - Mozilla Common Voice (v7.0)
-- People's Speech  - 12,000 hrs subset
+- People's Speech  - 12,000 hour subset
 
 ## Performance