more detailed readme

README.md

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 This project aims to create an Automatic Speech Recognition (ASR) model dedicated for the Tunisian Arabic dialect. The goal is to improve speech recognition technology for underrepresented linguistic communities by transcribing Tunisian dialect speech into written text.
 
 ## Dataset
-All the audio and text data collected to train the model have been provided for free to encourage and support research within the community. Please find the paper [here](https://zenodo.org/record/8342762).
+Part of the audio and text data (The ones we collected) used to train and test the model has been provided to encourage and support research within the community. Please find the dataset [here](https://zenodo.org/record/8370566). This Zenodo record contains labeled and unlabeled Tunisian Arabic audio data, along with textual data for language modelling. 
+The folder also contains a 4-gram language model trained with KenLM on data released within the Zenodo record. The .arpa file is called "outdomain.arpa". 
 
 ## Performance
 
@@ -48,21 +49,19 @@ If you use or refer to this model, please cite :
 This ASR model was trained on :
 * TARIC : The corpus, named TARIC (Tunisian Arabic Railway Interaction Corpus) has a collection of audio recordings and transcriptions from dialogues in the Tunisian Railway Transport Network. - [Taric Corpus](https://aclanthology.org/L14-1385/) -
 * IWSLT : A Tunisian conversational speech - [IWSLT Corpus](https://iwslt.org/2022/dialect)-
-* TunSwitch : Our crowd-collected dataset described in the paper presented below. 
+* TunSwitch : Our crowd-collected dataset described in the paper presented above. 
 
 ## Demo
 Here is a working live demo :  [LINK](https://huggingface.co/spaces/SalahZa/Code-Switched-Tunisian-SpeechToText)
 
 
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-
 ## Inference 
 
 ### 1. Create a CSV test file
-First, you have to create a csv file that follow SpeechBrain's format which contain 4 columns:
+First, you have to create a csv file that follows SpeechBrain's format which contain 4 columns:
 * ID: contain ID to identify each audio sample in the dataset
 * wav: contain the path to the audio file
-* wrd: contain the text transcription of the spoken content in the audio file
+* wrd: contain the text transcription of the spoken content in the audio file if you have it and use your set for evaluation. Put anything if you don't have transcriptions. An example is provided in this folder, the file is called : taric_test.csv
 * duration: the duration of the audio in seconds