Update README.md

README.md

@@ -1,38 +1,132 @@
----
-license: mit
-configs:
-- config_name: default
-  data_files:
-  - split: test
-    path: data/test-*
-dataset_info:
-  features:
-  - name: audio
-    dtype: audio
-  - name: duration
-    dtype: float64
-  - name: reference
-    dtype: string
-  - name: RobotsMali/stt-bm-quartznet15x5-v0
-    dtype: string
-  - name: RobotsMali/stt-bm-quartznet15x5-v1
-    dtype: string
-  - name: RobotsMali/soloba-ctc-0.6b-v0
-    dtype: string
-  - name: RobotsMali/soloba-ctc-0.6b-v1
-    dtype: string
-  - name: RobotsMali/soloni-114m-tdt-ctc-v0
-    dtype: string
-  - name: RobotsMali/soloni-114m-tdt-ctc-v1
-    dtype: string
-  - name: RobotsMali/stt-bm-quartznet15x5-v2
-    dtype: string
-  - name: soloni-114m-tdt-ctc-v2
-    dtype: string
-  splits:
-  - name: test
-    num_bytes: 20690456
-    num_examples: 45
-  download_size: 19975985
-  dataset_size: 20690456
----
+---
+license: mit
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test-*
+dataset_info:
+  features:
+  - name: audio
+    dtype: audio
+  - name: duration
+    dtype: float64
+  - name: reference
+    dtype: string
+  - name: RobotsMali/stt-bm-quartznet15x5-v0
+    dtype: string
+  - name: RobotsMali/stt-bm-quartznet15x5-v1
+    dtype: string
+  - name: RobotsMali/soloba-ctc-0.6b-v0
+    dtype: string
+  - name: RobotsMali/soloba-ctc-0.6b-v1
+    dtype: string
+  - name: RobotsMali/soloni-114m-tdt-ctc-v0
+    dtype: string
+  - name: RobotsMali/soloni-114m-tdt-ctc-v1
+    dtype: string
+  - name: RobotsMali/stt-bm-quartznet15x5-v2
+    dtype: string
+  - name: soloni-114m-tdt-ctc-v2
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 20690456
+    num_examples: 45
+  download_size: 19975985
+  dataset_size: 20690456
+---
+# Nyana-Eval Dataset
+
+## Dataset Description
+
+**Nyana-Eval** is a compact, stratified evaluation subset for benchmarking Automatic Speech Recognition (ASR) models in Bambara. It consists of **45 audio recordings** totaling approximately **3.03 minutes** (0.05 hours), carefully selected to represent real-world linguistic and acoustic challenges in low-resource Bambara speech. This dataset is derived from the larger [RobotsMali/Bam_ASR_Eval_500](https://huggingface.co/datasets/RobotsMali/Bam_ASR_Eval_500) corpus and is optimized for quick, reproducible ASR testing.
+
+Nyana-Eval is ideal for:
+- Rapid evaluation of Bambara ASR models (e.g., WER/CER computation on diverse conditions).
+- Human-assisted qualitative analysis (e.g., semantic fidelity, code-switching handling).
+- Addressing gaps in low-resource settings: dialectal variations, noise, proper names, and code-mixing with French.
+
+**Key Statistics**:
+- **Total Samples**: 45 (balanced: 15 per source subset).
+- **Total Duration**: ~3.03 minutes (average ~4.0 seconds per sample).
+- **Audio Format**: Mono-channel WAV files at 16 kHz sampling rate.
+- **Languages**: Primary: Bambara (Bamana); Secondary: French code-switching (~15% of samples).
+- **License**: MIT License (open for research, commercial use with attribution).
+- **Tags**: `audio`, `speech`, `asr`, `bambara`, `bamanan`, `low-resource`, `evaluation`, `human-evaluation`, `african-languages`.
+
+Compiled by Robots Mali AI4D Lab, this dataset powers the human-comparative analysis in the [Bambara ASR Models Evaluation Report].
+
+## Dataset Structure
+
+Nyana-Eval is a single-split dataset (default: `test`), with each entry including raw audio, duration, transcriptions (reference) and models transcriptions.
+
+### Features/Columns
+
+| Column          | Type     | Description                                                                 | Example Value |
+|-----------------|----------|-----------------------------------------------------------------------------|---------------|
+| `audio`        | Audio   | Raw audio waveform (array + sampling rate: 16 kHz) or file path.            | `{"path": "1.1.wav", "array": [...], "sampling_rate": 16000}` |
+| `duration`     | Float64 | Length of the audio clip in seconds (range: 0.62s – 15s).                   | 3.45         |
+| `references`   | String  | Bambara text                                                                | "nɔgɔ ye a ka tɔɔrɔ ye" |
+| '8 * models transcriptions' | String  | Bambara text  transcription                                    |  |
+
+### Splits
+- **Default Split**: Full 45 samples (`test` for evaluation).
+- **Subsets by Source**: Balanced 15 samples each from the three parent subsets (see Sources below).
+
+To load in Python (via Hugging Face Datasets):
+```python
+from datasets import load_dataset
+dataset = load_dataset("RobotsMali/nyana-eval", split="test")
+print(len(dataset))  # Output: 45
+print(dataset[0])    # Example: {'audio': ..., 'duration': 3.45, 'transcription': 'adama dusukasilen ye a sigi'}
+```
+
+## Sources and Compilation
+
+Nyana-Eval is a **balanced subsample (15 per subset)** from the full 500-sample [RobotsMali/Bam_ASR_Eval_500](https://huggingface.co/datasets/RobotsMali/Bam_ASR_Eval_500) corpus (~36.69 minutes total). Selection criteria ensured diversity: voice variety (age/gender/accents), acoustic challenges (noise/volume/overlaps), and linguistic phenomena (code-switching, proper names, proverbs).
+
+**Parent Subsets Breakdown** (15 samples each in Nyana-Eval):
+- **Ref. 1: RobotsMali/kunkado (Hugging Face)** – 15 audios (~1.96 minutes scaled).  
+  Semi-supervised interviews and spontaneous discourse. Source: [RobotsMali/kunkado](https://huggingface.co/datasets/RobotsMali/kunkado). Focus: Dialectal variations and natural flow.
+
+- **Ref. 2: jeli-ASR street interviews subset** – 15 audios (~0.31 minutes scaled).  
+  Griot traditions and urban interviews. Emphasizes oral storytelling, cultural terms, and hesitations.
+
+- **Ref. 3: Extracts from An Bɛ Kalan app (Robots Mali)** – 15 audios (~3.34 minutes scaled).  
+  User-generated readings from the Bambara learning app. Captures learner speech with occasional errors or pauses.
+
+## Metadata
+
+### General Metadata
+- **Creator**: Robots Mali AI4D Lab
+- **Version**: 1.0 (November 2025).
+- **Creation Date**: Derived November 2025 from Bam_ASR_Eval_500.
+- **Update Frequency**: Static (expansions via parent dataset).
+- **Download Size**: ~25 MB (audios + metadata).
+- **Ethical Notes**: Ethically sourced/anonymized; focuses on public-domain cultural speech. For research; cite Robots Mali.
+
+### Linguistic Metadata
+- **Dialect Coverage**: Urban Bamana (Bamako-influenced) with rural elements.
+- **Phonetic Coverage**: Tones, nasals, contractions; with OOD proper names (e.g., "Sunjata," "Traoré").
+- **Challenges Represented**:
+  - Code-switching: samples (e.g., "Segou ville").
+  - Noise/Overlaps: (e.g., low-volume interviews, multi-speaker).
+  - Human Eval Focus: 45 samples scored 0-3 + bonuses for fidelity, names, switching, robustness.
+
+### Evaluation Metadata
+- **WER Baselines**: From report – Soloni-v2: 36.07%; QuartzNet-v0: 65.42% (greedy decoding).
+- **Human Scores**: Aggregated 0-135 scale; e.g., Soloni-v2: 53 (top performer).
+- **Stats per Subset** (scaled to 15 samples):
+  | Subset      | Samples | Avg. Duration (s) | Avg. Transcription Length (chars) | % Challenging (Noise/Switching) |
+  |-------------|---------|-------------------|----------------------------------|--------------------------------|
+  | kunkado    | 15     | 7.87             | 48                               | 30%                           |
+  | jeli-ASR   | 15     | 1.24             | 32                               | 50%                           |
+  | An Bɛ Kalan| 15     | 13.44            | 56                               | 20%                           |
+
+## Related Resources
+- **Parent Dataset**: [RobotsMali/Bam_ASR_Eval_500](https://huggingface.co/datasets/RobotsMali/Bam_ASR_Eval_500) (full 500 samples).
+- **Models**: Test with [RobotsMali ASR models](https://huggingface.co/RobotsMali/models)
+- **App**: Collect similar data via [An Bɛ Kalan](https://play.google.com/store/apps/details?id=com.robotsmali.anbekalan).
+
+This README is self-contained; explore the attached report PDF for detailed human annotations and model rankings on these exact 45 samples!