rlabz/quantum-tts-tokenized

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. It is derived from `rlabz/swa_lug_tts` by:

1. 1.Filtering the train and validation splits down to speaker `swa_spk3` only.
2. 2.Resampling all audio from its original 22,050 Hz to 24,000 Hz, the sample rate required by SNAC (snac_24khz), the neural audio codec Orpheus is trained on.
3. 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.

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 (inherited from the source dataset)

Dataset Structure

Data Splits

Data Fields

⚠️ The exact tokenization script used determines the precise field names — adjust this table if your pipeline's output differs.

Data Instance

{
  "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 Kaggle dataset (CC0), processed into `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. 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. 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. 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 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` dataset instead.

Licensing Information

Released under CC0 1.0 Universal (Public Domain Dedication), matching the license of the original Kaggle source and the parent rlabz/swa_lug_tts dataset.

Citation

@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:

@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. ---