| --- |
| 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](https://huggingface.co/kyutai/mimi) neural-codec tokens for the |
| [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) 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`](https://huggingface.co/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 |
|
|
| ```python |
| 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: |
|
|
| ```python |
| 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. |
|
|
| ## Links |
|
|
| - **Dataset extraction code:** [github.com/shangeth/wren-datasets](https://github.com/shangeth/wren-datasets) |
| - **Wren research project:** [github.com/shangeth/wren](https://github.com/shangeth/wren) |
| - **TTS models trained on these codes:** [github.com/shangeth/wren-tts](https://github.com/shangeth/wren-tts) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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/} |
| } |
| ``` |
|
|
| ## Related |
|
|
| Used to train the [Wren](https://huggingface.co/shangeth/Wren-TTS-360M-v1) series of |
| speech-text multimodal models. |
|
|
