Publish Jeli-ASR version 1.0.0

README.md

@@ -3,15 +3,17 @@ language:
 - bm  # ISO 639-1 code for Bambara
 - fr  # ISO 639-1 code for French
 pretty_name: "Jeli-ASR Audio Dataset"
+version: "1.0.0"  # Explicit versioning
 tags:
 - audio
 - transcription
 - multilingual
 - Bambara
 - French
-license: "cc-by-4.0"  # Replace with the appropriate license for your dataset
+license: "cc-by-4.0" 
 task_categories:
 - automatic-speech-recognition
+- text-to-speech
 - translation
 task_ids:
 - audio-language-identification  # Identifying languages in audio
@@ -23,65 +25,153 @@ language_creators:
 source_datasets:
 - jeli-asr  
 size_categories:
-- 10GB<    
+- 10GB<
+- 10K<n<100K
 dataset_info:
   audio_format: "wav"
-  total_audio_files: 11533
-  total_duration_hours: ~30  
+  features:
+  - name: audio
+    dtype: audio
+  - name: duration
+    dtype: float
+  - name: bam
+    dtype: string
+  - name: french
+    dtype: string
+  total_audio_files: 33643 
+  total_duration_hours: ~32
+
+configs:
+- config_name: jeli-asr-rmai
+  data_files:
+  - split: train
+    path: "jeli-asr-rmai/train/*"
+  - split: test
+    path: "jeli-asr-rmai/test/*"
+- config_name: bam-asr-oza
+  data_files:
+  - split: train
+    path: "bam-asr-oza/train/*"
+  - split: test
+    path: "bam-asr-oza/test/*"
+- config_name: jeli-asr
+  default: true
+  data_files:
+  - split: train
+    path: 
+      - "jeli-asr-rmai/train/*"
+      - "bam-asr-oza/train/*"
+  - split: test
+    path: 
+      - "jeli-asr-rmai/test/*"
+      - "bam-asr-oza/test/*"
 description: |
-  The **Jeli Audio Dataset** is a multilingual audio dataset containing audio samples 
-  in Bambara and French. Each audio file is paired with its transcription in Bambara or 
+  The **Jeli-ASR Audio Dataset** is a multilingual audio dataset containing audio samples 
+  in Bambara with semi-expert transcriptions and French translations. Each audio file is paired with its transcription in Bambara or 
   its translation in French (available in manifest files). The dataset is designed for tasks 
-  like automatic speech recognition (ASR) and translation. 
-  Data was recorded in an organized setup in Mali with griots and semi-professionally transcribed, and translated into French.
+  like automatic speech recognition (ASR), text-to-speech synthesis (TTS) and translation. Data was recorded in an organized setup in Mali with griots and semi-professionally transcribed, and translated into French.
 ---
 
-# jeli-asr-data-manifest
+# Jeli-ASR Dataset
 
-This repository contains a resampled version of `jeli-asr` dataset with correponding NeMo data manifests.
+This repository contains the **Jeli-ASR** dataset, which is primarily a reviewed version of Aboubacar Ouattara's **Bambara-ASR** dataset (drawn from jeli-asr and available at [oza75/bambara-asr](https://huggingface.co/datasets/oza75/bambara-asr)) combined with the best data retained from the former version: `jeli-data-manifest`. This dataset features improved data quality for automatic speech recognition (ASR) and translation tasks, with variable length Bambara audio samples, Bambara transcriptions and French translations.
 
-## Directory Structure
+## Important Note
 
-The directory structure is as follows:
+Please note that this dataset is currently in development and is therefore not fixed. The structure, content, and availability of the dataset may change as improvements and updates are made.
+
+---
+
+## **Key Changes in This Version**
+
+### **1. Name Change**
+
+- The Dataset name was changed from `jeli-data-manifest` to `jeli-asr`.
+
+### **2. Mono Channel Conversion**
+
+- All stereo audio files have been converted to mono to ensure consistency across the dataset.
+  - This step was required only for the `jeli-asr-rmai` subset as `oza-bam-asr` was already consistent.
+
+### **3. Removal of Misaligned Samples**
+
+- More than 70% of the data in the previous version contained misaligned samples due to concatenation issues that kind of spread misalignment in the dataset.
+- A filtering process was applied using both **manual classification** and **trained classifiers**:
+  - A **subset** of the data was **manually classified** as **aligned** or **misaligned**.
+  - This subset was used to **train classifiers** (Logistic Regression and XGBoost) to label the remaining samples.
+    **Classifier Performance**:
+  - Best-performing model: **Logistic Regression**
+    - **Accuracy**: 0.84
+    - **F1-score (misaligned - class 0)**: 0.86
+    - **F1-score (aligned - class 1)**: 0.82
+      **Training Details**:
+  - **Balanced training set**: Positive samples (aligned) were supplemented using additional aligned samples from **Oza's Bambara-ASR** dataset.
+  - **Misaligned samples**: No additional samples were needed as they formed a majority.
+  - **Embedding processing**: Manually separated data has been represented as embeddings for training classifiers. The embeddings were obtained by inferring Wav2Vec and BERT, then concatenated for every example and labeled as either aligned or misaligned.
+
+Misaligned samples identified during classification were removed. That subset is currently undergoing further review and may be partially reintegrated in a future version of this dataset.
+
+### **4. Integration of Oza's Bambara-ASR Dataset**
+
+- This version integrates a clean subset from **Oza's Bambara-ASR** dataset making about 90% of the data.
+
+### **5. Lowercased Transcriptions**
+
+- All transcriptions and translations have been converted to lowercase for consistency.
+
+### **6. Silent/Empty File Filtering**
+
+- Silent or empty audio files  with inaudible content were removed.
+
+---
+
+## **Directory Structure**
 
 ```
-jeli-data-manifest/
+jeli-asr/
+|
+├── README.md
+├── metadata.jsonl
+├── manifests/
+│   ├── jeli-asr-rmai-test-manifest.json
+│   ├── jeli-asr-rmai-train-manifest.json
+│   ├── oza-bam-asr-test-manifest.json
+│   └── oza-bam-asr-train-manifest.json
+│   └── train-manifest.json # jeli-asr-rmai-train-manifest.json + oza-bam-asr-train-manifest.json
+│   └── test-manifest.json # jeli-asr-rmai-test-manifest.json + oza-bam-asr-test-manifest.json
 │
-├── audios/
-│   ├── train/
-│   └── test/
+├── scripts/
+│   ├── clean_tsv.py
+│   ├── convert_to_mono_channel.py
+│   ├── create_data_manifest.py
+│   ├── create_manifest_oza_bam_asr.py
+│   ├── filter_silent_and_inaudible.py
+│   └── lower_transcriptions_in_manifests.py
 │
 ├── french-manifests/
-│   ├── train_french_manifest.json
-│   └── test_french_manifest.json
+│   ├── jeli-asr-rmai-test-french-manifest.json
+│   ├── jeli-asr-rmai-train-french-manifest.json
+│   ├── oza-bam-asr-test-french-manifest.json
+│   └── oza-bam-asr-train-french-manifest.json
 │
-├── manifests/
-│   ├── train_manifest.json
-│   └── test_manifest.json
+├── jeli-asr-rmai/
+│   ├── train/
+│   └── test/
 │
-└── scripts/
-    └── create_manifest.py
-    └── clean_tsv.py
+├── bam-asr-oza/
+│   ├── train/
+│   └── test/
 ```
 
-### 1. **audios/**
-This directory contains the audio files (.wav format) of every example in the dataset. The audio files are split into two subdirectories:
-- **train/**: Contains audio files used for training.
-- **test/**: Contains audio files used for testing.
-
-The audio files vary in length and correspond to each entry in the manifest files. They are referenced by file paths in the manifest files.
-
-### 2. **manifests/**
-This directory contains the manifest files used for training speech recognition (ASR) models. There are two JSON files:
-- **train_manifest.json**: Contains file paths, durations, and transcriptions for the training set.
-- **test_manifest.json**: Contains file paths, durations, and transcriptions for the test set.
+### **manifests Directory**
+This directory contains the manifest files used for training speech recognition (ASR) and text-to-speech (TTS) models. Those are JSON files:
 
 Each line in the manifest files is a JSON object with the following structure:
 ```json
 {
-  "audio_filepath": "jeli-data-manifest/audios/train/griots_r19-1609461-1627744.wav",
-  "duration": 18.283,
-  "text": "I kun tɛ kɔrɔta maa min si kakɔrɔ n'ita ye, i ŋɛ t'a ŋɛ ye..."
+  "audio_filepath": "jeli-asr/bam-asr-oza/train/oza75-bam-asr-14.wav", 
+  "duration": 4.888, 
+  "text": "n'o tɛ n'a fɔra den o den ma ko yiriba, i b'a kɔlɔsi a bɛna kɛ mɔgɔjɛmɔgɔ ye don dɔ."
 }
 ```
 - **audio_filepath**: The relative path to the corresponding audio file.
@@ -89,70 +179,102 @@ Each line in the manifest files is a JSON object with the following structure:
 - **text**: The transcription of the audio in Bambara.
 
 ### 3. **french-manifests/**
-This directory contains French equivalent manifest files for the dataset. The structure is similar to the `manifests/` directory but with French transcriptions:
-- **train_french_manifest.json**: Contains the French transcriptions for the training set.
-- **test_french_manifest.json**: Contains the French transcriptions for the test set.
+This directory contains French equivalent manifest files for the dataset. The structure is similar to the `manifests/` directory but with French transcriptions
 
-### 4. **scripts/**
-This directory contains scripts used to process the data and create manifest files:
-- **create_manifest.py**: A script used to create manifest files for training and testing. It re-samples the audio files published as the first version of Jeli-ASR dataset and generates the corresponding JSON manifest files.
-- **clean_tsv.py**: Script to remove some of the most common issues in the .tsv transcription files created during the last revision work on the dataset in January 2023, such as unwanted characters (", <>), consecutive tabs (making some rows incositent) and spacing errors
+---
 
-## Dataset Overview
+## **Scripts Explanation**
 
-The dataset consists of 11,533 audio-transcription pairs:
-- **Training set**: 9,803 examples (85%)
-- **Test set**: 1,730 examples (15%)
+### 1. convert\_to\_mono\_channel.py
 
-Each audio file is paired with a transcription in Bambara in the manifest files, and the corresponding French transcriptions are available in the `french-manifests/` directory.
+- Converts stereo audio files to mono.
+- Ensures consistent audio channel dimensions.
 
-## Usage
+### 2. filter\_silent\_and\_inaudible.py
 
-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 reformated for any other use or framework. 
+- Filters out silent or inaudible audio files.
 
-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.
+### 3. lower\_transcriptions\_in\_manifests.py
 
-Downloading the dataset:
+- Converts all text in the manifest files to lowercase for uniform formatting.
 
-```python
-from datasets import load_dataset
+### 4. clean\_tsv.py
 
-# Clone dataset repository with directory structure
-!git clone https://huggingface.co/datasets/RobotsMali/jeli-data-manifest
+- Script to remove some of the most common issues in the .tsv transcription files created during the last revision work on the dataset in January 2023, such as unwanted characters (", <>), consecutive tabs (making some rows incositent) and spacing errors *(used to create jeli-data-manifest)*.
 
-# Load the dataset into Hugging Face Dataset object
-dataset = load_dataset("jeli-data-manifest/manifests/train_manifest.json")
+### 5. create\_data\_manifest.py
 
-# The directory structure remains intact for additional file access
+- A script used to create manifest files for training and testing. It re-samples the audio files published as the first version of Jeli-ASR dataset and generates the corresponding JSON manifest files *(used to create jeli-data-manifest)*.
+
+### 6. create\_manifest\_oza\_bam\_asr.py
+
+- Create manifest files for the oza75/bambara-asr clean subset .
+
+---
+
+## **Dataset Details**
+
+- **Total Duration**: 32.48 hours
+- **Number of Samples**: 33,643
+  - **Training Set**: 32,180 samples (\~95%)
+  - **Testing Set**: 1,463 samples (\~5%)
+
+### **Subsets**:
+
+- **Oza's Bambara-ASR**: \~29 hours (clean subset).
+- **Jeli-ASR-RMAI**: \~3.5 hours (filtered subset).
+
+Note that since the two subsets were drawn from the original Jeli-ASR dataset, they are just different variation of the same data.
+
+---
 
+## **Usage**
 
-### Example NeMo 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.
+
+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.
+
+### Downloading the Dataset:
+
+```python
+from datasets import load_dataset
+
+# Clone dataset repository maintaining directory structure
+!git clone https://huggingface.co/datasets/RobotsMali/jeli-asr
+
+#  Or
+
+# Load the dataset into Hugging Face Dataset object
+dataset = load_dataset("RobotsMali/jeli-asr")
 ```
 
-Finetuning with Nemo:
+### Finetuning Example in NeMo:
 
 ```python
-from nemo.collections.asr.models import ASRModel
-train_manifest = 'jeli-data-manifest/manifests/train_manifest.json'
-test_manifest = 'jeli-data-manifest/manifests/test_manifest.json'
+from nemo.collectisr.models import ASRModel
+train_manifest = 'jeli-asr/manifests/train-manifest.json'
+test_manifest = 'jeli-asr/manifests/test-manifest.json'
 
 asr_model = ASRModel.from_pretrained("QuartzNet15x5Base-En")
 
 # 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})
 ```
 
-## Issues
-This version was created after some shallow cleaning on the transcriptions and resamplimg work. It has conserved most of the issues of the original dataset such as:
-
-- **Misaligned / Invalid segmentation**
-- **Language / Incorrect transcriptions**
-- **Non-standardized naming conventions**
+## **Known Issues**
 
-## Citation
+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: 
 
-If you use this dataset in your research or project, please give credit to the creators of the original [Jeli-ASR dataset](https://github.com/robotsmali-ai/jeli-asr). 
+- Inconsistent  transcriptions
+- Non-standardized naming conventions.
+- Language and spelling issues
 
 ---
+
+## **Citation**
+
+If you use this dataset in your research or project, please credit the creators of the original datasets.
+
+- **Jeli-ASR dataset**: [Original Jeli-ASR Dataset](https://github.com/robotsmali-ai/jeli-asr). 
+- **Oza's Bambara-ASR dataset**: [oza75/bambara-asr](https://huggingface.co/datasets/oza75/bambara-asr)

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