Update README.md

README.md

@@ -1,4 +1,19 @@
 ---
+license: cc0-1.0
+language:
+- fon
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- low-resource
+- alffa
+- tone-preserved
+pretty_name: Fongbe Speech Dataset (ALFFA + Zenodo)
+task_categories:
+- automatic-speech-recognition
+size_categories:
+- 10K<n<100K
 dataset_info:
   features:
   - name: audio
@@ -33,3 +48,97 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# Fongbe Speech Dataset (Complete & Tone-Preserved)
+
+## Dataset Summary
+
+This dataset is a unified, high-quality collection of Fongbe speech data, specifically curated to preserve the linguistic integrity of this tonal language. It acts as a complete, unsegmented, and tone-accurate assembly of the Fongbe Continuous Speech Recognition corpora, merging:
+
+1.  The foundational **ALFFA Project** data (Train/Test splits, 2016).
+2.  The expanded **Zenodo** release (Validation split, 2022).
+
+### Why Use This Version?
+
+Several derivative Fongbe datasets currently on the Hub have been heavily pre-processed. Those versions often chunk audio into unnatural segments, remove punctuation, or strip critical tone diacritics.
+
+**This repository ensures linguistic accuracy by preserving:**
+*   **Tones:** Retains all diacritics (e.g., `ɖ`, `ɛ`, `ɔ`, `è`, `é`, `ì`, `̌`, `̂`, `ĕ`, `ŏ`) essential for Fongbe semantics.
+*   **Audio Integrity:** Provides full-length original utterances rather than aggressively chopped segments, allowing for better context modeling.
+*   **Harmonized Schema:** Standardizes metadata across diverse source origins for immediate use in `transformers`.
+
+## Dataset Statistics
+
+| Split | Utterances | Total Duration (approx) | Source |
+| :--- | :--- | :--- | :--- |
+| **Train** | 8,234 | ~5.73 hrs | ALFFA GitHub |
+| **Validation** | 3,179 | ~5.11 hrs | Zenodo (6604637) |
+| **Test** | 2,168 | ~1.45 hrs | ALFFA GitHub |
+| **Total** | **13,581** | **~12.30 hrs** | |
+
+### Technical Metadata
+- **Sampling Rate:** 16,000 Hz
+- **Audio Format:** WAV (PCM 16-bit)
+- **Language:** Fongbe (Fon)
+- **Tonal Representation:** Decomposed (NFD) diacritics preserved.
+
+## Dataset Structure
+
+All splits share a harmonized schema for seamless use with the Hugging Face `datasets` and `transformers` libraries.
+
+### Features
+*   `audio`: A dictionary containing the audio array, sampling rate (16kHz), and local path.
+*   `text`: The ground-truth transcription with full tonal markers and punctuation.
+*   `speaker ID`: The unique identifier for the speaker (e.g., `"denise"`, `"mario"`, `"5"`).
+*   `audio filename`: The original filename for provenance tracking.
+
+## Quick Start
+
+You can load this dataset directly using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the full DatasetDict
+dataset = load_dataset("Professor/fongbe-speech-zenodo")
+
+# Access individual splits
+train_data = dataset["train"]
+validation_data = dataset["validation"]
+test_data = dataset["test"]
+
+# View a sample entry
+print(train_data[0])
+```
+
+## Sources & Credits
+This dataset is the result of extensive fieldwork and research by **Fréjus A. A. Laleye** and the **ALFFA (African Languages in the Field)** project team.
+
+### Citation Information
+If you use this dataset, please cite the following works:
+
+**For the Training/Test Data (ALFFA):**
+```bibtex
+@inproceedings{laleye2016FongbeASR,       
+    title={First Automatic Fongbe Continuous Speech Recognition System: Development of Acoustic Models and Language Models},     
+    author={A. A Laleye, Fréjus and Besacier, Laurent and Ezin, Eugène C. and Motamed, Cina},     
+    year={2016},     
+    organization={Federated Conference on Computer Science and Information Systems}
+}
+```
+
+**For the Validation Data (Zenodo):**
+```bibtex
+@dataset{laleye_frejus_2022_6604637,
+  author       = {Laleye, Fréjus A. A.},
+  title        = {Fongbe Speech Dataset},
+  month        = jun,
+  year         = 2022,
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.6604637},
+  url          = {https://doi.org/10.5281/zenodo.6604637}
+}
+```
+
+## Contributions
+Ported to Hugging Face by **Victor Olufemi (Professor)**. This version ensures that low-resource language modeling for Fongbe remains linguistically accurate by preventing the loss of tonal information during preprocessing.