Update README.md

README.md

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 ---
-dataset_info:
-  features:
-  - name: input_ids
-    list: int32
-  - name: labels
-    list: int64
-  - name: attention_mask
-    list: int8
-  splits:
-  - name: train
-    num_bytes: 8688858.0
-    num_examples: 1785
-  download_size: 2957485
-  dataset_size: 8688858.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+language:
+- sw
+license: cc0-1.0
+task_categories:
+- text-to-speech
+pretty_name: Swahili (swa_spk3) SNAC-Tokenized TTS Dataset for Orpheus Fine-Tuning
+size_categories:
+- 1K<n<10K
+tags:
+- tts
+- speech
+- swahili
+- kiswahili
+- snac
+- orpheus
+- audio-tokens
+- low-resource
+- african-languages
+---
+
+# Swahili (swa_spk3) SNAC-Tokenized Dataset for Orpheus-TTS Fine-Tuning
+
+## Dataset Summary
+
+A single-speaker Kiswahili subset, resampled and tokenized for fine-tuning [Orpheus-TTS](https://github.com/canopyai/Orpheus-TTS). It is derived from [`rlabz/swa_lug_tts`](https://huggingface.co/datasets/rlabz/swa_lug_tts) by:
+
+1. Filtering the `train` and `validation` splits down to speaker **`swa_spk3`** only.
+2. Resampling all audio from its original 22,050 Hz to **24,000 Hz**, the sample rate required by [SNAC](https://github.com/hubertsiuzdak/snac) (`snac_24khz`), the neural audio codec Orpheus is trained on.
+3. Encoding each clip with SNAC into discrete audio codes and interleaving them with the text transcript into a single `input_ids` sequence, following the tokenization scheme used in the official [Orpheus fine-tuning notebook](https://github.com/canopyai/Orpheus-TTS).
+
+The result is a training-ready dataset: no further audio processing is needed before feeding it into the Orpheus fine-tuning script.
+
+- **Source speaker subset:** `swa_spk3` from `rlabz/swa_lug_tts`
+- **Language:** Kiswahili (`sw`)
+- **Audio codec / sample rate:** SNAC @ 24kHz
+- **Intended use:** Fine-tuning Orpheus-TTS (Llama-3B-backbone) for a single Kiswahili voice
+- **License:** [CC0 1.0 Public Domain](https://creativecommons.org/publicdomain/zero/1.0/) (inherited from the source dataset)
+
+## Dataset Structure
+
+### Data Splits
+
+| Split | Utterances |
+|---|---|
+| train | 1,785 |
+| validation | 198 |
+
+### Data Fields
+
+> ⚠️ The exact tokenization script used determines the precise field names — adjust this table if your pipeline's output differs.
+
+| Field | Type | Description |
+|---|---|---|
+| `input_ids` | `list[int]` | Interleaved sequence of text token IDs (from the Llama tokenizer) and SNAC audio token IDs, following Orpheus's `<start_of_text> text <end_of_text> <start_of_speech> audio_codes <end_of_speech>` layout. Audio tokens are offset above the text vocabulary (IDs ≥ 128,000) so they occupy a disjoint range from text tokens. |
+| `labels` | `list[int]` | Copy of `input_ids` used for next-token-prediction loss (standard causal LM fine-tuning target). |
+| `attention_mask` | `list[int]` | Standard attention mask, all `1`s for non-padded sequences. |
+| `speaker_id` | `string` | Always `swa_spk3` in this subset. |
+| `language` | `string` | Always `swa`. |
+
+### Data Instance
+
+```python
+{
+  "input_ids": [128259, 264, 1495, ..., 128266, 7, 42, 91, ..., 128257],
+  "labels": [128259, 264, 1495, ..., 128266, 7, 42, 91, ..., 128257],
+  "attention_mask": [1, 1, 1, ...],
+  "speaker_id": "swa_spk3",
+  "language": "swa"
+}
+```
+
+## Dataset Creation
+
+### Source Data
+
+Traces back to 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 (CC0), processed into [`rlabz/swa_lug_tts`](https://huggingface.co/datasets/rlabz/swa_lug_tts) — see that dataset's card for details on corrupt-file filtering, speaker clustering, and the stratified train/validation split.
+
+### Processing Steps
+
+1. **Load** `rlabz/swa_lug_tts` and filter both `train` and `validation` splits to `speaker_id == "swa_spk3"` (1,785 train / 198 validation utterances).
+2. **Resample** the `audio` column from 22,050 Hz to 24,000 Hz via `datasets.Audio(sampling_rate=24000)`, matching SNAC's expected input rate.
+3. **Tokenize** each utterance with SNAC (`snac_24khz`) to produce hierarchical discrete audio codes, then interleave those codes with the text transcript's Llama tokenizer IDs into a single flat `input_ids` sequence, per the Orpheus fine-tuning data format.
+
+### Why a single-speaker subset?
+
+Orpheus fine-tuning for a specific voice is typically done on a single, consistent speaker rather than the full multi-speaker corpus, since mixing speakers in a single-voice fine-tune degrades voice consistency. `swa_spk3` was selected as the target voice for this fine-tune; the other 11 speakers in `rlabz/swa_lug_tts` remain available for separate single-speaker or multi-speaker experiments.
+
+## Intended Use
+
+This dataset is intended as direct input to the [Orpheus-TTS fine-tuning script](https://github.com/canopyai/Orpheus-TTS/tree/main/finetune) to produce a Kiswahili single-voice TTS model. It is not intended as a general-purpose ASR or multi-speaker TTS dataset — for that, use the source [`rlabz/swa_lug_tts`](https://huggingface.co/datasets/rlabz/swa_lug_tts) dataset instead.
+
+## Licensing Information
+
+Released under [CC0 1.0 Universal (Public Domain Dedication)](https://creativecommons.org/publicdomain/zero/1.0/), matching the license of the original Kaggle source and the parent `rlabz/swa_lug_tts` dataset.
+
+## Citation
+
+```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}
+}
+```
+
+Orpheus-TTS:
+
+```bibtex
+@misc{orpheus_tts,
+  title  = {Orpheus-TTS: Towards Human-Sounding Speech},
+  author = {Canopy Labs},
+  year   = {2025},
+  url    = {https://github.com/canopyai/Orpheus-TTS}
+}
+```
+
+## Acknowledgements
+
+Speaker filtering, resampling, and SNAC tokenization were performed as part of Orpheus fine-tuning data preparation under `rlabz`.
 ---