Update README.md

README.md

@@ -36,29 +36,23 @@ tags:
 
 ## Summary
 
-The "whisper-large-v3-tiny-caesar" is an acoustic model based on ["openai/whisper-large-v3"](https://huggingface.co/openai/whisper-large-v3) suitable for Automatic Speech Recognition in code switching conditions between Spanish and Catalan.
+The "whisper-large-v3-tiny-caesar" is an acoustic model based on ["openai/whisper-large-v3"](https://huggingface.co/openai/whisper-large-v3) suitable for Automatic Speech Recognition in code-switching conditions between Spanish and Catalan.
 
 ## Model Description
 
-The "whisper-large-v3-tiny-caesar" is an acoustic model suitable for Automatic Speech Recognition in code switching conditions between Spanish and Catalan. It is the result of finetuning the model ["openai/whisper-large-v3"](https://huggingface.co/openai/whisper-large-v3) with 2 hours of synthetic code switching data in Spanish/Catalan generated by the [Projecte AINA](https://projecteaina.cat/) from Barcelona, Spain.
-
-CAESAR is an acronym with the following meaning:
-
-(CA)talan (ES)panish (A)utomatic (R)ecognition
-
-While "tiny" indicates that this model was finetuned with a very small amount of synthetic data (2 hours only).
+The "whisper-large-v3-tiny-caesar" is an acoustic model suitable for Automatic Speech Recognition in code-switching conditions between Spanish and Catalan. It is the result of fine-tuning the ["openai/whisper-large-v3"](https://huggingface.co/openai/whisper-large-v3) with [CAESAR-TINY](https://huggingface.co/datasets/BSC-LT/CAESAR-TINY), a 2-hour code-switching dataset in Spanish/Catalan.
 
 ## Intended Uses and Limitations
 
-This model can be used for Automatic Speech Recognition (ASR) in code switching conditions between Spanish and Catalan. The model is intended to transcribe audio files to plain text.
+This model can be used for Automatic Speech Recognition (ASR) in code-switching conditions between Spanish and Catalan. The model is intended to transcribe audio files to plain text.
 
 ## How to Get Started with the Model
 
-To see an updated and functional version of this code, please see our our [Notebook](https://colab.research.google.com/drive/1MHiPrffNTwiyWeUyMQvSdSbfkef_8aJC?usp=sharing)
+To see an updated and functional version of this code, please check our [Notebook](https://colab.research.google.com/drive/1MHiPrffNTwiyWeUyMQvSdSbfkef_8aJC?usp=sharing)
 
 ### Installation
 
-In order to use this model, you may install [datasets](https://huggingface.co/docs/datasets/installation) and [transformers](https://huggingface.co/docs/transformers/installation):
+To use this model, you may install [datasets](https://huggingface.co/docs/datasets/installation) and [transformers](https://huggingface.co/docs/transformers/installation):
 
 Create a virtual environment:
 ```bash
@@ -74,7 +68,7 @@ pip install datasets transformers
 ```
 
 ### For Inference
-In order to transcribe audio in Catalan using this model, you can follow this example:
+To transcribe audio in Catalan using this model, you can follow this example:
 
 ```bash
 #Install Prerequisites
@@ -89,7 +83,7 @@ pip install jiwer
 #This code works with GPU
 
 #Notice that: load_metric is no longer part of datasets.
-#you have to remove it and use evaluate's load instead.
+# You have to remove it and use evaluate's load instead.
 #(Note from November 2024)
 
 import torch
@@ -136,7 +130,7 @@ print(WER)
 
 ### Training data
 
-The specific dataset used to create the model is a corpus called CAESAR-tiny which has not been released at the moment.
+The specific dataset used to create the model is a corpus called CAESAR-tiny, which has not been released at the moment.
 
 ### Training procedure
 
@@ -174,7 +168,7 @@ If this model contributes to your research, please cite the work:
 
 ### Author
 
-The fine-tuning process was perform during November (2024) in the [Language Technologies Unit](https://huggingface.co/BSC-LT) of the [Barcelona Supercomputing Center](https://www.bsc.es/) by [Carlos Daniel Hernández Mena](https://huggingface.co/carlosdanielhernandezmena).
+The fine-tuning process was performed during November (2024) in the [Language Technologies Unit](https://huggingface.co/BSC-LT) of the [Barcelona Supercomputing Center](https://www.bsc.es/) by [Carlos Daniel Hernández Mena](https://huggingface.co/carlosdanielhernandezmena).
 
 ### Contact
 For further information, please send an email to <[email protected]>.
@@ -189,4 +183,4 @@ Copyright(c) 2024 by Language Technologies Unit, Barcelona Supercomputing Center
 ### Funding
 This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/).
 
-The training of the model was possible thanks to the compute time provided by [Barcelona Supercomputing Center](https://www.bsc.es/) through MareNostrum 5.
+The training of the model was possible thanks to the computing time provided by [Barcelona Supercomputing Center](https://www.bsc.es/) through MareNostrum 5.