Datasets:
File size: 8,806 Bytes
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license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- bm
tags:
- bambara
- African-Next-Voices
- ANV
- RobotsMali
- afvoices
- asr
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. We release all the data processing code on [GitHub](https://github.com/RobotsMali-AI/afvoices).
---
## 🔎 **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
@misc{diarra2025dealinghardfactslowresource,
title={Dealing with the Hard Facts of Low-Resource African NLP},
author={Yacouba Diarra and Nouhoum Souleymane Coulibaly and Panga Azazia Kamaté and Madani Amadou Tall and Emmanuel Élisé Koné and Aymane Dembélé and Michael Leventhal},
year={2025},
eprint={2511.18557},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2511.18557},
}
```
---
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 🤗 |