Update README.md

README.md

@@ -80,5 +80,124 @@ tags:
 - RobotsMali
 - afvoices
 - asr
-pretty_name: a
----
+pretty_name: Robots
+---
+
+# 📘 **African Next Voices – Bambara (AfVoices)**
+
+The **AfVoices** dataset is the largest open corpus of spontaneous Bambara speech at its release in late 2025. It contains **423 hours** of segmented audio and **612 hours** of original raw recordings collected across southern Mali. Speech was recorded in natural, conversational settings and annotated using a semi-automated transcription pipeline combining ASR pre-labels and human corrections.
+
+---
+
+## 🔎 **Quick Facts**
+
+| Category                                 | Value                                                                                             |
+| ---------------------------------------- | ------------------------------------------------------------------------------------------------- |
+| **Total raw hours**                      | 612 h (1,777 raw recordings; publicly available on GCS)                                           |
+| **Total segmented hours**                | 423 h (874,762 segments)                                                                          |
+| **Speakers**                             | 512                                                                                               |
+| **Regions**                              | Bamako, Ségou, Sikasso, Bagineda, Bougouni                                                        |
+| **Avg. segment duration**                | ~2 seconds                                                                                        |
+| **Subsets**                              | 159 h human-corrected, 212 h model-annotated, 52 h short (<1s)                                    |
+| **Age distribution**                     | Broad, across young to elderly speakers (90% between 18 and 45)                                   |
+| **Topics**                               | Health, agriculture, Miscellaneous (art, education, history etc.)                                 |
+| **SNR distribution (raw recordings)**    | 71.75% High or Very High SNR                                                                      |
+| **Train / Test split**                   | 155 h / 4 h                                                                                       |
+
+---
+
+## **Motivation**
+
+The **African Next Voices (ANV)** project is a multi-country effort aiming to gather over **9,000 hours of speech** across 18 African languages. Its goal is to build high-quality datasets that empower local communities, support inclusive AI research, and provide strong foundations for ASR in underrepresented languages.
+
+As part of this initiative, **RobotsMali** led the Bambara data collection for Mali. This dataset reflects RobotsMali’s broader mission to advance AI and NLP research malian languages, with a long-term focus on improving education, access, and technology across Mali and the wider Manding linguistic region.
+
+---
+
+## 🎙️ **Characteristics of the Dataset**
+
+### **Data Collection**
+
+* Speech was collected through trained **facilitators** who guided participants, ensured audio quality, and encouraged natural, topic-focused conversations.
+* All recordings are **spontaneous speech**, not read text.
+* A custom **Flutter mobile app** ([open-source](https://github.com/RobotsMali-AI/Africa-Voice-App)) was used to simplify the process and reduce training time.
+* Geographic focus: **Southern Mali**, to limit extreme accent variation and build a clean baseline corpus.
+
+### **Segmentation and Preprocessing**
+
+* Raw audio was segmented using **Silero VAD**, retaining ~70% of the original duration.
+* Segments range from **240 ms to 30 s**.
+* Voice activity detection helped remove long silences and improve data usability.
+
+### **Transcriptions**
+
+* Pre-transcribed using the ASR model **soloni-114m-tdt-ctc-v0**.
+* Human annotators corrected the transcripts.
+* A second model (**soloni-114m-tdt-ctc-v2**) was trained using the corrected transcripts and used to regenerate improved labels.
+* Two automatic transcription variants exist for each sample: **v1** (from soloni-v0) and **v2** (from soloni-v2).
+
+### **Acoustic Event Tags**
+
+The following tags appear in transcriptions:
+
+| Tag       | Meaning                                                       |
+| --------- | ------------------------------------------------------------- |
+| `[um]`    | Vocalized pauses, filler sounds                               |
+| `[cs]`    | Code-switched or foreign word                                 |
+| `[noise]` | Background noise (applause, coughing, children, etc.)         |
+| `[?]`     | Inaudible or overlapped speech                                |
+| `[pause]` | Long silence (>5 seconds or >3 seconds at segment boundaries); due to VAD segmentation this tag is rarely used |
+
+---
+
+## 📂 **Subsets**
+
+### **1. Human-corrected (159 h, 260k samples)**
+
+* Fully reviewed and corrected by annotators.
+* Only subset with a definitive `text` field containing the validated transcription.
+
+### **2. Model-annotated (212 h, 355k samples)**
+
+* Includes automatic labels: `v1` (soloni-v0) and `v2` (soloni-v2).
+* No human review.
+
+### **3. Short subset (52 h, 259k samples)**
+
+* Segments <1 second (formulaic expressions, discourse markers).
+* Excluded from human annotation for optimization purposes.
+* Automatically labeled (v1 & v2).
+
+---
+
+## ⚠️ **Limitations**
+
+* **Clean dataset vs real-world noise:**
+  Over 70% of recordings can be categorized as relatively clean speech. Models trained solely on this dataset may underperform in noisy street or radio environments typical in Mali. See this [report](https://zenodo.org/records/17672774) if you are interested in learning more about the strengths and weaknesses of RobotsMali's ASR models.
+
+* **Reduced code-switching:**
+  French terms were often replaced by `[cs]` or normalized into Bambara phonology. This improves model stability but reduces realism for natural bilingual speech.
+
+* **Geographic homogeneity:**
+  Focused on the southern region to control accent variability. Broader dialectal coverage might require additional data.
+
+* **Simplified linguistic conditions:**
+  Overlaps, multi-speaker settings, and conversational chaos are minimized—again improving training stability at the cost of deployment realism.
+
+---
+
+## 📑 **Citation**
+
+```bibtex
+@article{diarra2025afvoices,
+  title={Dealing with the Hard Facts of Low-Resource African NLP},
+  author={Diarra, Yacouba and Coulibaly, Nouhoum Souleymane and Kamaté, Panga Azazia and Tall, Madani Amadou and Koné, Emmanuel Élisé and Dembélé, Aymane and Leventhal, Michael},
+  journal={Preprint},
+  note = {arxiv coming soon}
+  year={2025},
+}
+```
+
+---
+
+You may want to download the original 612 hours dataset with its associated metadata for research purposes or to create a derivative. You will find the codes and manifest files to download those files from Google Cloud Storage in this repository: [RobotsMali-AI/afvoices](https://github.com/RobotsMali-AI/afvoices). Do not hesitate to open an issue for Help or suggestions 🤗