Datasets:

shangeth
/

libritts-r-mimi-codes

license: cc-by-4.0
language:
  - en
task_categories:
  - text-to-speech
tags:
  - mimi
  - neural-codec
  - speech-synthesis
  - libritts
  - audio-tokens
pretty_name: LibriTTS-R Mimi Codes
size_categories:
  - 100K<n<1M

LibriTTS-R — Mimi Codes

Pre-extracted Kyutai Mimi neural-codec tokens for LibriTTS-R — a speech-restored version of LibriTTS built specifically for TTS research.

Why LibriTTS-R instead of LibriSpeech?

	LibriSpeech	LibriTTS-R
Purpose	ASR	TTS
Sample rate	16 kHz	24 kHz (Mimi-native, no resampling)
Segmentation	Arbitrary chunks	Sentence-level
Punctuation	Stripped (ALL CAPS)	Preserved
Audio quality	Raw amateur	Speech restoration applied

No resampling is needed — 24 kHz matches Mimi exactly.

Schema

Column	Type	Notes
`id`	string	e.g. `84_121123_000003_000000`
`text`	string	normalized text, mixed-case with punctuation preserved
`speaker_id`	int32	LibriTTS speaker ID
`codes`	`int16[k=8][n_frames]`	Mimi codebook indices @ 12.5 fps
`n_frames`	int32
`k_codebooks`	int32	8

Extraction details

Codec: kyutai/mimi @ 24 kHz, 12.5 fps
Codebooks: all 8 extracted. Slice codes[:k] for fewer.
Source: OpenSLR 141

Splits

HF Split	Source	~Rows
`train_clean_100`	train-clean-100	~33.2k
`train_clean_360`	train-clean-360	~116k
`train_other_500`	train-other-500	~205k
`dev_clean`	dev-clean	~2.7k
`dev_other`	dev-other	~2.9k
`test_clean`	test-clean	~2.6k
`test_other`	test-other	~2.9k

Usage

from datasets import load_dataset
import torch

ds = load_dataset("shangeth/libritts-r-mimi-codes", split="train_clean_100")
ex = ds[0]
codes = torch.tensor(ex["codes"], dtype=torch.long)  # [8, n_frames]
print(ex["text"])  # "He hoped there would be stew for dinner, turnips and carrots."

Citation

@misc{wren2026,
  title  = {Wren: A Family of Small Open-Weight Models for Unified Speech-Text Modelling},
  author = {Shangeth Rajaa},
  year   = {2026},
  url    = {https://github.com/shangeth/wren}
}

@inproceedings{koizumi2023libritts,
  title     = {LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus},
  author    = {Koizumi, Yuma and others},
  booktitle = {Interspeech},
  year      = {2023}
}

License

CC-BY-4.0 (inherited from LibriTTS-R).