Update README.md

README.md

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 ---
-dataset_info:
-  features:
-  - name: audio
-    dtype:
-      audio:
-        sampling_rate: 24000
-  - name: text
-    dtype: string
-  - name: english_transcript
-    dtype: string
-  - name: language
-    dtype: string
-  - name: speaker_id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 6468080914.670589
-    num_examples: 25514
-  - name: validation
-    num_bytes: 723684647.92
-    num_examples: 2835
-  download_size: 7145230769
-  dataset_size: 7191765562.59059
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
+language:
+- lg
+- sw
+license: cc0-1.0
+multilinguality:
+- multilingual
+task_categories:
+- text-to-speech
+- automatic-speech-recognition
+pretty_name: Luganda-Swahili Speaker-Clustered TTS Dataset
+size_categories:
+- 10K<n<100K
+tags:
+- tts
+- speech
+- luganda
+- swahili
+- kiswahili
+- low-resource
+- african-languages
+---
+
+# Luganda-Swahili Speaker-Clustered TTS Dataset
+
+## Dataset Summary
+
+A cleaned, speaker-labeled text-to-speech (TTS) dataset covering **Luganda** and **Kiswahili**, derived from the [Luganda-Swahili Speech for Text-to-Speech Synthesis](https://www.kaggle.com/datasets/jocelyndumlao/luganda-swahili-speech-for-text-to-speechsynthesis/data) Kaggle dataset. The source data contains recordings from 6 speakers per language, but speaker identity was not labeled in the original release. Speaker labels in this version were recovered via unsupervised audio clustering (6 clusters per language), and the audio was filtered for corrupt files before merging into a single train/validation split.
+
+- **Languages:** Luganda (`lg`), Kiswahili / Swahili (`sw`)
+- **Total utterances:** 28,349 (after cleaning)
+- **Total duration:** ~32.16 hours (13.10h Kiswahili + 19.06h Luganda)
+- **Speakers:** 6 per language (12 total), speaker identity inferred via clustering — **not** ground-truth speaker metadata
+- **License:** [CC0 1.0 Public Domain](https://creativecommons.org/publicdomain/zero/1.0/)
+
+## Supported Tasks
+
+- **Text-to-Speech (TTS):** `text` / `english_transcript` → `audio`
+- **Automatic Speech Recognition (ASR):** `audio` → `text`
+- **Speaker-conditioned TTS / voice cloning:** using the clustered `speaker_id` field
+
+## Dataset Structure
+
+### Data Instances
+
+Each example contains a single audio clip paired with its transcript in the source language, an English translation, a language tag, and a clustered speaker ID.
+
+```python
+{
+  "audio": {"array": [...], "sampling_rate": 16000, "path": "..."},
+  "text": "...",
+  "english_transcript": "...",
+  "language": "lug",  # or "swa"
+  "speaker_id": "lug_spk5"
+}
+```
+
+### Data Fields
+
+| Field | Type | Description |
+|---|---|---|
+| `audio` | `Audio` | The speech recording |
+| `text` | `string` | Transcript in the source language (Luganda or Kiswahili) |
+| `english_transcript` | `string` | English translation of the transcript |
+| `language` | `string` | Source language code (`lug` or `swa`) |
+| `speaker_id` | `string` | Clustered speaker label (e.g. `lug_spk1`–`lug_spk6`, `swa_spk1`–`swa_spk6`); assigned via unsupervised clustering, not original metadata |
+
+### Data Splits
+
+The dataset was split 90/10 into train/validation, **stratified per speaker** so every speaker is proportionally represented in both splits (rather than a single global random split, which risked dropping low-count speakers entirely from one side).
+
+| Split | Utterances | % |
+|---|---|---|
+| train | 25,514 | 90.0% |
+| validation | 2,835 | 10.0% |
+| **Total** | **28,349** | 100% |
+
+**Per-speaker distribution:**
+
+| Speaker | Train | Validation |
+|---|---|---|
+| lug_spk1 | 2,172 | 241 |
+| lug_spk2 | 1,910 | 212 |
+| lug_spk3 | 3,078 | 342 |
+| lug_spk4 | 1,867 | 208 |
+| lug_spk5 | 3,592 | 399 |
+| lug_spk6 | 2,862 | 318 |
+| swa_spk1 | 969 | 108 |
+| swa_spk2 | 1,412 | 157 |
+| swa_spk3 | 1,785 | 198 |
+| swa_spk4 | 1,525 | 169 |
+| swa_spk5 | 2,709 | 301 |
+| swa_spk6 | 1,633 | 182 |
+
+## Dataset Creation
+
+### Source Data
+
+Raw audio and transcripts were sourced from the [Luganda-Swahili Speech for Text-to-Speech Synthesis](https://www.kaggle.com/datasets/jocelyndumlao/luganda-swahili-speech-for-text-to-speechsynthesis/data) dataset on Kaggle, released under [CC0 1.0 Public Domain](https://creativecommons.org/publicdomain/zero/1.0/).
+
+### Speaker Clustering
+
+The original dataset does not label which of the 6 speakers per language produced each clip. Speaker identity was recovered by:
+1. Extracting audio features from each clip.
+2. Clustering clips per language into 6 groups (k=6) to approximate the 6 known speakers.
+
+Resulting cluster sizes:
+
+**Luganda** (17,201 clips clustered into 6 speakers):
+
+| Speaker | Clips |
+|---|---|
+| lug_spk5 | 3,991 |
+| lug_spk3 | 3,420 |
+| lug_spk6 | 3,180 |
+| lug_spk1 | 2,413 |
+| lug_spk2 | 2,122 |
+| lug_spk4 | 2,075 |
+
+**Kiswahili** (11,149 clips clustered into 6 speakers):
+
+| Speaker | Clips |
+|---|---|
+| swa_spk5 | 3,010 |
+| swa_spk3 | 1,983 |
+| swa_spk6 | 1,816 |
+| swa_spk4 | 1,694 |
+| swa_spk2 | 1,569 |
+| swa_spk1 | 1,077 |
+
+**Note:** these speaker labels are cluster assignments, not verified ground-truth speaker identities. They should be treated as a best-effort approximation useful for speaker-conditioned modeling, not as authoritative metadata.
+
+### Data Cleaning
+
+Corrupt/empty audio files were filtered out prior to clustering and merging:
+
+| Language | Before | After | Removed |
+|---|---|---|---|
+| Kiswahili | 11,149 | 11,148 | 1 |
+| Luganda | 17,201 | 17,201 | 0 |
+
+### Audio Statistics
+
+| | Kiswahili | Luganda |
+|---|---|---|
+| Average duration | 4.23s | 3.99s |
+| Median duration | 4.14s | 3.78s |
+| Min duration | 1.05s | 1.17s |
+| Max duration | 8.73s | 9.72s |
+| Total duration | 13.10h | 19.06h |
+
+## Licensing Information
+
+This dataset is released under [CC0 1.0 Universal (Public Domain Dedication)](https://creativecommons.org/publicdomain/zero/1.0/), matching the license of the source Kaggle dataset.
+
+## Citation
+
+If you use this dataset, please cite the original source:
+
+```bibtex
+@misc{lugswa_tts_kaggle,
+  title  = {Luganda-Swahili Speech for Text-to-Speech Synthesis},
+  author = {Dumlao, Jocelyn},
+  year   = {2024},
+  url    = {https://www.kaggle.com/datasets/jocelyndumlao/luganda-swahili-speech-for-text-to-speechsynthesis/data}
+}
+```
+
+## Acknowledgements
+
+Speaker clustering, corrupt-file filtering, and the stratified train/validation split were performed as part of dataset preparation work under `r-labs`.
 ---