Datasets:

shangeth
/

ljspeech-mimi-codes

license: cc0-1.0
language:
  - en
task_categories:
  - text-to-speech
  - automatic-speech-recognition
tags:
  - mimi
  - neural-codec
  - speech-synthesis
  - speech-recognition
  - ljspeech
  - audio-tokens
pretty_name: LJSpeech Mimi Codes
size_categories:
  - 10K<n<100K

LJSpeech — Mimi Codes

Pre-extracted Kyutai Mimi neural-codec tokens for the LJSpeech corpus — 13,100 English utterances from a single female speaker reading public-domain audiobook passages (~24 hours).

This dataset contains codes only, not audio. For waveforms, go to the original LJSpeech release; these codes are designed to be loaded alongside it for training Mimi-based speech models without paying the ~1 hour of GPU extraction cost.

Schema

One row per utterance:

Column	Type	Notes
`id`	string	e.g. `LJ001-0001`
`text`	string	normalized transcript, original mixed-case preserved
`codes`	`int16[k=8][n_frames]`	Mimi codebook indices @ 12.5 fps
`n_frames`	int32	= `codes.shape[1]`
`k_codebooks`	int32	= 8

Extraction details

Codec: kyutai/mimi @ 24 kHz, 12.5 fps
Codebooks: all 8 extracted. Slice codes[:k] if you want fewer (Mimi's codebooks are ordered by importance; the first few capture most of the signal).
Codebook size: 2048 per codebook → values stored as int16
Transcripts: the normalized column from metadata.csv (punctuation preserved, expanded numerics/abbreviations). Original mixed-case is kept — apply .lower() at load time if your model expects lowercase (e.g. to reproduce Wren-TTS training).

Usage

from datasets import load_dataset
import torch

ds = load_dataset("shangeth/ljspeech-mimi-codes", split="train")

ex    = ds[0]
codes = torch.tensor(ex["codes"], dtype=torch.long)   # [8, n_frames]
print(ex["id"], "→", ex["text"][:60])
print("codes:", codes.shape, "duration:", codes.shape[1] / 12.5, "s")

# Use only the first 3 codebooks (e.g. for a smaller model):
codes_3 = codes[:3]

Decode back to audio with the Mimi decoder:

from transformers import MimiModel
mimi = MimiModel.from_pretrained("kyutai/mimi").cuda().eval()
with torch.no_grad():
    wav = mimi.decode(codes.unsqueeze(0).cuda()).audio_values[0].cpu()
# wav is [1, T] @ 24 kHz

Splits

Split	Rows
`train`	~13,100

LJSpeech has no canonical train/val/test split — partition as your task requires.

License

The underlying LJSpeech corpus is in the public domain (CC0). The derived Mimi codes inherit this license. You can use, redistribute, and modify without attribution, though citing the original corpus is encouraged.

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}
}

@misc{ito2017lj,
  title  = {The LJ Speech Dataset},
  author = {Keith Ito and Linda Johnson},
  year   = {2017},
  url    = {https://keithito.com/LJ-Speech-Dataset/}
}

Used to train the Wren series of speech-text multimodal models.