Update README.md

README.md

@@ -15,72 +15,372 @@ size_categories:
 - 1K<n<10K
 ---
 
-# SUKUMA Voice Dataset
+---
+dataset_info:
+  features:
+  - name: audio
+    dtype: audio
+  - name: text
+    dtype: string
+  - name: gender
+    dtype: string
+  - name: voice
+    dtype: string
+  - name: filename
+    dtype: string
+  - name: record_id
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 3257
+  - name: test
+    num_examples: 362
+  - name: test_indistribution_synthesis
+    num_examples: 362
+  - name: test_outdistribution_synthesis
+    num_examples: 362
+  download_size: 6725563759
+  dataset_size: 6935373055
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+  - split: test_indistribution_synthesis
+    path: data/test_indistribution_synthesis-*
+  - split: test_outdistribution_synthesis
+    path: data/test_outdistribution_synthesis-*
+license: cc-by-4.0
+language:
+- suk
+task_categories:
+- automatic-speech-recognition
+- text-to-speech
+- speech-synthesis
+tags:
+- sukuma
+- low-resource
+- african-languages
+- bantu
+- tanzania
+- speech-corpus
+- speech-evaluation
+- tts-evaluation
+size_categories:
+- 1K<n<10K
+pretty_name: Sukuma Voices
+---
 
-This dataset contains Sukuma language voice data from multiple sources for speech-to-text and text-to-speech and speech evaluation tasks.
+# Sukuma Voices Dataset 🎙️
 
-## Dataset Details
+<p align="center">
+  <img src="https://img.shields.io/badge/Language-Sukuma-blue" alt="Language">
+  <img src="https://img.shields.io/badge/Samples-4,343-green" alt="Samples">
+  <img src="https://img.shields.io/badge/Duration-19.56%20hours-orange" alt="Duration">
+  <img src="https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey" alt="License">
+</p>
 
-- **train**: 3,257 samples
-- **test**: 362 samples
-- **test_indistribution_synthesis**: 362 samples
-- **test_outdistribution_synthesis**: 362 samples
+**The first publicly available speech corpus for Sukuma (Kisukuma)**, a Bantu language spoken by approximately 10 million people in northern Tanzania. This dataset supports speech-to-text, text-to-speech, and speech evaluation tasks.
 
+---
+
+## Dataset Description
+
+**Sukuma Voices** addresses the critical gap in speech technology resources for one of Africa's most severely under-resourced languages. The dataset includes both human recordings and TTS-synthesized audio for comprehensive model evaluation.
+
+### Dataset Summary
+
+| Metric | Value |
+|--------|-------|
+| Total Samples | 4,343 |
+| Total Duration | 19.56 hours |
+| Average Duration | 10.25 ± 4.15 seconds |
+| Duration Range | 1.40 - 30.36 seconds |
+| Total Words | 140,325 |
+| Unique Vocabulary | 21,366 |
+| Average Words/Sample | 20.4 |
+| Speaking Rate | 121.6 WPM |
+
+### Supported Tasks
+
+- **Automatic Speech Recognition (ASR)**: Converting Sukuma speech to text
+- **Text-to-Speech (TTS)**: Synthesizing natural-sounding Sukuma speech
+- **Speech Evaluation**: Comparing human vs. synthesized speech quality
+- **Cross-lingual Speech Processing**: Research between Swahili and Sukuma
+
+### Languages
+
+- **Sukuma** (ISO 639-3: `suk`) - A Bantu language of the Niger-Congo family
+
+---
 
 ## Dataset Structure
 
-The dataset contains three subsets:
+### Splits Overview
 
-- **Train**: Base SUKUMA voice data with human recordings
-- **Test**: Base SUKUMA voice data with human recordings
-- **test_indistribution_synthesis**: TTS-generated audio for in-distribution evaluation
-- **test_outdistribution_synthesis**: TTS-generated audio for out-of-distribution evaluation
+| Split | Samples | Description |
+|-------|---------|-------------|
+| `train` | 3,257 | Human recordings for training |
+| `test` | 362 | Human recordings for evaluation |
+| `test_indistribution_synthesis` | 362 | TTS-generated audio (in-distribution) |
+| `test_outdistribution_synthesis` | 362 | TTS-generated audio (out-of-distribution) |
+
+### Split Details
+
+| Split | Purpose | Audio Source |
+|-------|---------|--------------|
+| **train** | Model training | Human recordings |
+| **test** | ASR/TTS evaluation on natural speech | Human recordings |
+| **test_indistribution_synthesis** | Evaluate TTS quality on seen text patterns | TTS-generated |
+| **test_outdistribution_synthesis** | Evaluate TTS generalization | TTS-generated |
 
 ### Features
 
 All subsets contain the following features:
-- `audio`: Audio feature with speech samples
-- `text`: Text content in Sukuma language
-- `gender`: Speaker gender
-- `voice`: Voice identifier
-- `filename`: Original filename
-- `record_id`: Unique record identifier
+
+| Feature | Type | Description |
+|---------|------|-------------|
+| `audio` | Audio | Speech samples (16kHz for ASR, 24kHz for TTS) |
+| `text` | String | Text content in Sukuma language |
+| `gender` | String | Speaker gender |
+| `voice` | String | Voice identifier |
+| `filename` | String | Original filename |
+| `record_id` | String | Unique record identifier |
+
+### Example Instance
+
+```python
+{
+    "audio": {
+        "array": [...],
+        "sampling_rate": 16000
+    },
+    "text": "Umunhu ngwunuyo agabhalelaga chiza abhanhu bhakwe.",
+    "gender": "female",
+    "voice": "speaker_01",
+    "filename": "sukuma_001.wav",
+    "record_id": "suk_00001"
+}
+```
+
+### Example Sentences
+
+| Language | Text |
+|----------|------|
+| **Sukuma** | Umunhu ngwunuyo agabhalelaga chiza abhanhu bhakwe, kunguyo ya kikalile kakwe akagubhatogwa na gubhambilija abho bhali mumakoye. |
+| **English** | This person raises his people well, because of his good behavior, of loving people and helping his colleagues who are in trouble, in their lives. |
+
+---
 
 ## Usage
 
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load all splits
+dataset = load_dataset("sartifyllc/SUKUMA_VOICE")
+
+# Access specific splits
+train_data = dataset["train"]
+test_data = dataset["test"]
+test_synth_in = dataset["test_indistribution_synthesis"]
+test_synth_out = dataset["test_outdistribution_synthesis"]
+
+print(f"Train samples: {len(train_data)}")
+print(f"Test samples: {len(test_data)}")
+```
+
+### ASR Training Example
+
+```python
+from datasets import load_dataset
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+# Load dataset
+dataset = load_dataset("sartifyllc/SUKUMA_VOICE")
+
+# Load model
+processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3")
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3")
+
+# Process a sample
+sample = dataset["test"][0]
+input_features = processor(
+    sample["audio"]["array"], 
+    sampling_rate=16000, 
+    return_tensors="pt"
+).input_features
+
+# Generate transcription
+predicted_ids = model.generate(input_features)
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+print(f"Predicted: {transcription}")
+print(f"Reference: {sample['text']}")
+```
+
+### TTS Evaluation Example
+
 ```python
 from datasets import load_dataset
+from jiwer import wer
 
-# Load the full dataset
+# Load human and synthesized test sets
 dataset = load_dataset("sartifyllc/SUKUMA_VOICE")
+human_test = dataset["test"]
+synth_test = dataset["test_indistribution_synthesis"]
 
-# Access individual subsets
-original_data = dataset["train"]
-original_data = dataset["test"]
-indist_data = dataset["test_indistribution_synthesis"] 
-outdist_data = dataset["test_outdistribution_synthesis"]
-
-# Example: Load first sample from original subset
-sample = dataset["original"][0]
-audio_array = sample["audio"]["array"]
-sample_rate = sample["audio"]["sampling_rate"]
-text = sample["text"]
+# Compare WER between human and synthesized speech
+# (using your trained ASR model)
+human_wer = evaluate_asr(model, human_test)
+synth_wer = evaluate_asr(model, synth_test)
+
+print(f"Human Speech WER: {human_wer:.2%}")
+print(f"Synthesized Speech WER: {synth_wer:.2%}")
 ```
 
-## Evaluation Use Cases
+---
+
+## Dataset Creation
+
+### Source Data
+
+The dataset was curated from audio recordings and textual transcriptions of the **Sukuma New Testament 2000 translation**, sourced from the Bible.com platform.
+
+#### Why Biblical Text?
+
+1. **Standardized orthographic conventions** ensuring transcription consistency
+2. **Diverse linguistic structures** encompassing narrative, dialogue, and theological discourse
+3. **Cultural relevance** to Sukuma-speaking communities
+4. **Availability** of both audio recordings and verified textual transcriptions
+
+### Synthesis Pipeline
+
+The `test_indistribution_synthesis` and `test_outdistribution_synthesis` splits were generated using our [Sukuma-TTS](https://huggingface.co/Mollel/Sukuma-TTS) model, fine-tuned on Orpheus 3B with LoRA.
+
+### Annotations
+
+The data was rigorously annotated to ensure phonetic and orthographic consistency, with validation by native Sukuma speakers.
+
+---
+
+## Baseline Results
+
+### ASR Performance (Whisper Large V3)
+
+| Metric | Human Speech | Synthesized Speech |
+|--------|--------------|-------------------|
+| Final WER | 25.19% | 32.60% |
+| Min WER | 22.01% | 29.97% |
+| WER Reduction | 82.94% | 78.93% |
+
+**Key Findings:**
+- Strong correlation between human and synthetic learning curves (Pearson's r = 0.997)
+- Performance gap narrows as training progresses (from 9.97 to 8.11 WER points)
+- Synthetic speech captures essential acoustic-phonetic characteristics despite ~28% relative performance gap
+
+### TTS Performance (Orpheus 3B v0.1)
+
+| Metric | Score |
+|--------|-------|
+| **Mean Opinion Score (MOS)** | 3.9 ± 0.15 |
+| Human Recording MOS | 4.6 ± 0.1 |
+
+*Evaluated by native Sukuma speakers using a 5-point Likert scale.*
+
+---
+
+## Considerations for Using the Data
+
+### Known Limitations
+
+1. **Domain Specificity**: Data is primarily from biblical texts, which may not fully represent everyday conversational Sukuma
+2. **Diacritic Variations**: Sukuma has two written forms (with and without diacritics); this dataset focuses on the non-diacritic version
+3. **Single Source**: Limited speaker diversity from a single recording source
 
-This dataset supports various TTS evaluation scenarios:
-- **Quality Assessment**: Compare original vs synthesized audio
-- **In-distribution Evaluation**: Test on similar data distribution
-- **Out-of-distribution Evaluation**: Test generalization capabilities
+### Linguistic Challenges
 
-## Source Datasets
+- Sukuma is a **tonal language** with complex phonological features
+- The language lacks standardized orthographic conventions across written materials
+- Diacritic and non-diacritic text representations can affect vocabulary size and evaluation metrics
 
-This merged dataset combines:
-- `Mollel/SUKUMA_VOICE` (original subset)
-- `Mollel/sukuma-tts-evaluation` (indistribution_synthesis subset) 
-- `Mollel/sukuma-tts-syevaluation` (outdistribution_synthesis subset)
+### Personal and Sensitive Information
+
+The dataset contains religious text (Bible readings) and does not include personal or sensitive information about individuals.
+
+---
 
 ## Citation
 
-If you use this dataset, please cite the original sources and this merged version.
+If you use this dataset, please cite:
+
+```bibtex
+@inproceedings{mgonzo2025sukuma,
+  title={Learning from Scarcity: Building and Benchmarking Speech Technology for Sukuma},
+  author={Mgonzo, Macton and Oketch, Kezia and Etori, Naome and Mang'eni, Winnie and Nyaki, Elizabeth and Mollel, Michael S.},
+  booktitle={Proceedings of the Association for Computational Linguistics},
+  year={2025}
+}
+```
+
+---
+
+## Additional Information
+
+### Authors
+
+| Name | Affiliation | Contact |
+|------|-------------|---------|
+| **Macton Mgonzo** | Brown University | macton_mgonzo@brown.edu |
+| **Kezia Oketch** | University of Notre Dame | |
+| **Naome Etori** | University of Minnesota - Twin Cities | |
+| **Winnie Mang'eni** | Pawa AI | |
+| **Elizabeth Nyaki** | Pawa AI, Sartify Company Limited | |
+| **Michael S. Mollel** | Sartify Company Limited | |
+
+### Dataset Curators
+
+- [Sartify Company Limited](https://www.sartify.com/)
+- [Pawa AI](https://www.pawa-ai.com/)
+
+### Acknowledgments
+
+We would like to express our gratitude to [Sartify Company Limited](https://www.sartify.com/) and [Pawa AI](https://www.pawa-ai.com/) for their instrumental role in initiating this project and for providing the data access necessary to develop and evaluate our models. We also extend our sincere thanks to all the volunteers who generously dedicated their time to the evaluation process.
+
+### Licensing Information
+
+This dataset is released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).
+
+### Contributions
+
+We welcome contributions to expand and improve this dataset! Areas of interest include:
+- Additional Sukuma speech data beyond religious content
+- Conversational and everyday language recordings
+- Multi-speaker recordings
+- Diacritic-annotated transcriptions
+
+### Ethical Considerations
+
+- Consent was obtained from all human participants involved in data annotation
+- Participants were informed about the technology's limitations and potential impacts
+- The authors acknowledge that models trained on this data may inherit biases present in the source material
+
+### Related Resources
+
+- 🤖 **TTS Model:** [Mollel/Sukuma-TTS](https://huggingface.co/Mollel/Sukuma-TTS)
+- 📂 **GitHub:** [sukuma-voices](https://github.com/your-username/sukuma-voices)
+
+---
+
+<p align="center">
+  <i>This dataset represents an important step toward inclusive speech technology for African languages.</i>
+</p>
+
+<p align="center">
+  <a href="https://www.sartify.com/">Sartify</a> •
+  <a href="https://www.pawa-ai.com/">Pawa AI</a> •
+  <a href="https://huggingface.co/Mollel/Sukuma-TTS">TTS Model</a>
+</p>