better readme

README.md

@@ -1,17 +1,48 @@
-# Tunisian Arabic ASR Model with wav2vec2
+# Overview
 
-This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on Tunisian arabic dialect
+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).
 
 ## Performance
-The following table summarizes the performance of the model on various considered test sets : 
 
-| Dataset      	| CER   	| WER   	|
-|--------------	|-------	|-------	|
-| TARIC        	| 6.22  	| 10.55 	|
-| IWSLT        	| 21.18 	| 39.53 	|
-| TunSwitch TO 	| 9.67  	| 25.54 	|
+The following table summarizes the performance of the model on various considered test sets :
+
+| Dataset | CER     | WER                |
+| :-------- | :------- | :------------------------- |
+| `TARIC` | `6.22%` | `10.55%` |
+| `IWSLT` | `21.18%` | `39.53%` |
+| `TunSwitch TO` | `9.67%` | `25.54%` |
+
+More details about the test sets, and the conditions leading to this performance in the paper.
+
+
+
+## Team
+
+Here are the team members who have contributed to  this project
+
+* [Salah Zaiem](https://fr.linkedin.com/in/salah-zaiem)
+* [Ahmed Amine Ben Aballah](https://www.linkedin.com/in/aabenz/)
+* [Ata Kaboudi](https://www.linkedin.com/in/ata-kaboudi-63365b1a8)
+* [Amir Kanoun](https://tn.linkedin.com/in/ahmed-amir-kanoun)
+
+## Paper
+ More in-depth details and insights are available in a released preprint. Please find the paper [here](https://arxiv.org/abs/2309.11327).
+If you use or refer to this model, please cite : 
+
+```
+@misc{abdallah2023leveraging,
+      title={Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition}, 
+      author={Ahmed Amine Ben Abdallah and Ata Kabboudi and Amir Kanoun and Salah Zaiem},
+      year={2023},
+      eprint={2309.11327},
+      archivePrefix={arXiv},
+      primaryClass={eess.AS}
+}
+```
 
-More details about the test sets, and the conditions leading to this performance in the paper. 
 
 ## Datasets
 This ASR model was trained on :
@@ -19,11 +50,32 @@ This ASR model was trained on :
 * IWSLT : A Tunisian conversational speech - [IWSLT Corpus](https://iwslt.org/2022/dialect)-
 * TunSwitch : Our crowd-collected dataset described in the paper presented below. 
 
+## Demo
+Here is a working live demo :  [LINK](https://huggingface.co/spaces/SalahZa/Code-Switched-Tunisian-SpeechToText)
+
+
 
 
 ## Inference 
-## Install
-```python
-pip install speechbrain transformers
+
+### 1. Create a CSV test file
+First, you have to create a csv file that follow 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
+* duration: the duration of the audio in seconds
+
+
+### 2. Adjust the hyperparams.yaml file
+
+Adjust the path of **test_csv** parameter to your csv file path
+
+
+To run this recipe, do the following:
+```
+> python train_with_wavlm.py semi_wavlm_large_tunisian_ctc/1234/hyperparams.yaml --test_csv = path_to_csv
 ```
 
+If you want to infer on single files, the space demo offers proper easy-to-use inference code.
+
+