Update documentation and dataset viewer

README.md

@@ -58,13 +58,13 @@ configs:
   default: true
   data_files:
   - split: train
-    path: 
-      - "jeli-asr-rmai/train/data-*.arrow"
+    path:
       - "bam-asr-oza/train/data-*.arrow"
+      - "jeli-asr-rmai/train/data-*.arrow" 
   - split: test
     path: 
-      - "jeli-asr-rmai/test/data-*.arrow"
       - "bam-asr-oza/test/data-*.arrow"
+      - "jeli-asr-rmai/test/data-*.arrow" 
 description: |
   The **Jeli-ASR Audio Dataset** is a multilingual dataset converted into the optimized Arrow format, 
   ensuring fast access and compatibility with modern data workflows. It contains audio samples in Bambara 
@@ -84,7 +84,7 @@ This repository contains the **Jeli-ASR** dataset, which is primarily a reviewed
 
 ---
 
-## **Key Changes in Version 1.0.1**
+## **Key Changes in Version 1.0.1 (December 17th)**
 
 Jeli-ASR 1.0.1 introduces several updates and enhancements, focused entirely on the transcription side of the dataset. There have been no changes to the audio files since version 1.0.0. Below are the key updates:
 
@@ -125,44 +125,50 @@ Note that since the two subsets were drawn from the original Jeli-ASR dataset, t
 
 ## **Usage**
 
-The manifest files are specifically created for training Automatic Speech Recognition (ASR) models in NVIDIA NeMo framework, but they can be used with any other framework that supports manifest-based input formats or reformatted for other use cases.
+The data in the main branch are in .arrow format for compatibility with HF's Datasets Library. So you don't need any ajustement to load the dataset directly with datasets:
 
-To use the dataset, simply load the manifest files (`train-manifest.json` and `test-manifest.json`) in your training script. The file paths for the audio files and the corresponding transcriptions are already provided in these manifest files.
+```python
 
-### Downloading the Dataset:
-```bash
+from datasets import load_dataset
 
-# Clone dataset repository maintaining directory structure for quick setup with Nemo
-git clone --depth 1 https://huggingface.co/datasets/RobotsMali/jeli-asr
+# Load the dataset into Hugging Face Dataset object
+dataset = load_dataset("RobotsMali/jeli-asr")
 ```
 
-**OR**
+However, an "archives" branch has been added for improved versioning of the dataset and to facilitate usage for those working outside the typical Hugging Face workflow. Precisely the archives are created from the directory of version 1.0.0 tailored for usage with NVIDIA's NEMO. If you prefer to reconstrcut the dataset from archives you can follow the instructions below.
 
-```python
+### Downloading the Dataset:
 
-from datasets import load_dataset
+You could download the dataset by git cloning this branch:
 
-# Load the dataset into Hugging Face Dataset object
-dataset = load_dataset("RobotsMali/jeli-asr")
+```bash
+# Clone dataset repository maintaining directory structure for quick setup with Nemo
+git clone --depth 1 -b archives https://huggingface.co/datasets/RobotsMali/jeli-asr
 ```
 
-### Finetuning Example in NeMo:
+Or you could download the individual archives that you are interested in, thus avoiding the git overload
 
-```python
-from nemo.collectisr.models import ASRModel
-train_manifest = 'jeli-asr/manifests/train-manifest.json'
-test_manifest = 'jeli-asr/manifests/test-manifest.json'
+```bash
+# Download the audios with wget
+wget https://huggingface.co/datasets/RobotsMali/jeli-asr/resolve/archives/audio-archives/jeli-asr-1.0.0-audios.tar.gz
+# Download the manifests in the same way
+wget https://huggingface.co/datasets/RobotsMali/jeli-asr/resolve/archives/manifests-archives/jeli-asr-1.0.1-manifests.tar.gz
+```
 
-asr_model = ASRModel.from_pretrained("QuartzNet15x5Base-En")
+Finally, untar those files to reconstruct the default Directory structure of jeli-asr 1.0.0:
 
-# Adapt the model's vocab before training
-asr_model.setup_training_data(train_data_config={'manifest_filepath': train_manifest})
-asr_model.setup_validation_data(val_data_config={'manifest_filepath': test_manifest})
+```bash
+# untar the audios
+tar -xvzf jeli-asr-1.0.0-audios.tar.gz
+# untar the manifests
+tar -xvzf jeli-asr-1.0.0-audios.tar.gz
 ```
 
+This approach allow you to combine the data from different versions and restructure your working directory as you with, with more ease and without necessarily having to write code.
+
 ## **Known Issues**
 
-While significantly improved, this dataset may still contain a few Slightly misaligned samples. It has conserved most of the issues of the original dataset such as: 
+While significantly improved, this dataset may still contain some misaligned samples. It has conserved most of the issues of the original dataset such as: 
 
 - Inconsistent transcriptions
 - Non-standardized naming conventions.

VERSIONING.md

@@ -1,4 +1,4 @@
-## **Key Changes in Version 1.0.1**
+## **Key Changes in Version 1.0.1 (December 17th)**
 
 Jeli-ASR 1.0.1 introduces several updates and enhancements, focused entirely on the transcription side of the dataset. There have been no changes to the audio files since version 1.0.0. Below are the key updates:
 
@@ -21,7 +21,7 @@ These changes enhance the dataset's usability for ASR tasks while providing a cl
 
 ---
 
-## **Key Changes in Version 1.0.0**
+## **Key Changes in Version 1.0.0 (December 10th)**
 
 ### **1. Name Change**