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. 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 (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.

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

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 1s for non-padded sequences.
speaker_id	string	Always swa_spk3 in this subset.
language	string	Always swa.

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. 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 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.