Update Documentation and Dataset Viewer

README.md

@@ -49,8 +49,8 @@ configs:
   data_files:
   - split: train
     path: 
-      - "rt-data-collection/train/*.arrow"
       - "oza-mali-pense/train/*.arrow"
+      - "rt-data-collection/train/*.arrow"
       - "bam-asr-oza/train/*.arrow"
       - "jeli-asr-rmai/train/*.arrow"
   - split: test
@@ -85,7 +85,7 @@ description: |
 
 # All Bambara ASR Dataset
 
-This dataset aims to gather all publicly available Bambara ASR datasets. It is primarily composed of the **Jeli-ASR** dataset (available at [RobotsMali/jeli-asr](https://huggingface.co/datasets/RobotsMali/jeli-asr)), along with the **Mali-Pense** data curated and published by Aboubacar Ouattara (available at [oza75/bambara-tts](https://huggingface.co/datasets/oza75/bambara-tts)). Additionally, it includes 1 hour of audio recently collected by the RobotsMali AI4D Lab, featuring children's voices reading some of RobotsMali GAIFE books. This dataset is desihgned for automatic speech recognition (ASR) task primarily.
+This dataset aims to gather all publicly available Bambara ASR datasets. It is primarily composed of the **Jeli-ASR** dataset (available at [RobotsMali/jeli-asr](https://huggingface.co/datasets/RobotsMali/jeli-asr)), along with the **Mali-Pense** data curated and published by Aboubacar Ouattara (available at [oza75/bambara-tts](https://huggingface.co/datasets/oza75/bambara-tts)). Additionally, it includes 1 hour of audio recently collected by the RobotsMali AI4D Lab, featuring children's voices reading some of RobotsMali GAIFE books. This dataset is designed for automatic speech recognition (ASR) task primarily.
 
 ## Important Notes
 
@@ -111,28 +111,46 @@ This dataset aims to gather all publicly available Bambara ASR datasets. It is p
 
 ## **Usage**
 
-You can download and use this dataset with the Datasets library for download the archives to reconstruct the dataset with a specific structure for non HuggingFace workflows.
+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:
+
+```python
+from datasets import load_dataset
+
+# Load the dataset into Hugging Face Dataset object
+dataset = load_dataset("RobotsMali/bam-asr-all")
+```
+
+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.
 
 ### Downloading the Dataset:
 
-```bash
+You could download the dataset by git cloning this branch:
 
+```bash
 # Clone dataset repository maintaining directory structure for quick setup with Nemo
-# To be updated
-git clone --depth 1 https://huggingface.co/datasets/RobotsMali/bam-asr-all
+git clone --depth 1 -b archives https://huggingface.co/datasets/RobotsMali/bam-asr-all
 ```
 
-**OR**
+Or you could download the individual archives that you are interested in, thus avoiding the git overload
 
-```python
+```bash
+# Download the audios with wget
+wget https://huggingface.co/datasets/RobotsMali/bam-asr-all/resolve/archives/audio-archives/bam-asr-all-1.0.0-audios.tar.gz
+# Download the manifests in the same way
+wget https://huggingface.co/datasets/RobotsMali/bam-asr-all/resolve/archives/manifests-archives/bam-asr-all-1.0.1-manifests.tar.gz
+```
 
-from datasets import load_dataset
+Finally, untar those files to reconstruct the default Directory structure of jeli-asr 1.0.0:
 
-# Load the dataset into Hugging Face Dataset object
-dataset = load_dataset("RobotsMali/bam-asr-all")
-# Note: You can also download only a specific subset if you with
+```bash
+# untar the audios
+tar -xvzf bam-asr-all-1.0.0-audios.tar.gz
+# untar the manifests
+tar -xvzf bam-asr-all-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**
 
 This dataset also has most of the issues of Jeli-ASR, including a few misaligned samples. Additionally a few samples from the mali pense subset and all the data from the rt-data-collection subset don't currently have french translations

VERSIONING.md

@@ -0,0 +1,22 @@
+## **Key Changes in Version 1.0.1 (December 17th)**
+
+all-bam-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:
+
+1. **Symbol Removal:**  
+   All non-vocabulary symbols deemed unnecessary for Automatic Speech Recognition (ASR) were removed, including:  
+   `[` `]` `(` `)` `«` `»` `°` `"` `<` `>`  
+
+2. **Punctuation Removal:**  
+   Common punctuation marks were removed to streamline the dataset for ASR use cases. These include:  
+   `:` `,` `;` `.` `?` `!`  
+   The exception is the hyphen (`-`), which remains as it is used in both Bambara and French compound words. While this punctuation removal enhances ASR performance, the previous version with full punctuation may still be better suited for other applications. You can still reconstruct the previous version with the archives.
+
+3. **Bambara Normalization:**  
+   The transcription were normalized using the [Bambara Normalizer](https://pypi.org/project/bambara-normalizer/), a python package designed to normalize Bambara text for different NLP applications.
+
+4. **Optimized Data Format:**  
+   This version introduces `.arrow` files for efficient data storage and retrieval and compatibility with HuggingFace tools.
+
+These changes enhance the dataset's usability for ASR tasks while providing a cleaner transcription format. Let us know if you have feedback or additional use suggestions for the dataset by opening a discussion or a pull request.  
+
+---