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license: cc-by-4.0
language:
- nl
- fr
- de
- it
- pl
- pt
- es
task_categories:
- text-to-speech
- automatic-speech-recognition
tags:
- mimi
- neural-codec
- multilingual
- mls
- multilingual-librispeech
- audio-tokens
pretty_name: Multilingual LibriSpeech Mimi Codes
size_categories:
- 1M<n<10M
---
# Multilingual LibriSpeech (MLS) — Mimi Codes
Pre-extracted [Kyutai Mimi](https://huggingface.co/kyutai/mimi) neural-codec tokens
for [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech) —
LibriVox audiobooks in 7 non-English languages.
English is intentionally excluded. For English Mimi codes, use:
- [shangeth/librispeech-mimi-codes](https://huggingface.co/datasets/shangeth/librispeech-mimi-codes) — LibriSpeech (~280k rows, 7 splits)
- [shangeth/libritts-r-mimi-codes](https://huggingface.co/datasets/shangeth/libritts-r-mimi-codes) — LibriTTS-R (~360k rows, 7 splits, 24 kHz native)
- [shangeth/vctk-mimi-codes](https://huggingface.co/datasets/shangeth/vctk-mimi-codes) — VCTK (~44k rows, 110 speakers w/ accents)
- [shangeth/jenny-mimi-codes](https://huggingface.co/datasets/shangeth/jenny-mimi-codes) — Jenny TTS (~21k rows, single speaker)
- [shangeth/ljspeech-mimi-codes](https://huggingface.co/datasets/shangeth/ljspeech-mimi-codes) — LJSpeech (~13k rows, single speaker)
## Configs (languages)
One HF dataset config per language:
| Config | Language | ISO | Approx hours (train) |
|---|---|---|---|
| `dutch` | Dutch | nl | ~1.5k |
| `french` | French | fr | ~1.1k |
| `german` | German | de | ~3.3k |
| `italian` | Italian | it | ~250 |
| `polish` | Polish | pl | ~100 |
| `portuguese` | Portuguese | pt | ~160 |
| `spanish` | Spanish | es | ~920 |
## Splits (per config)
| Split | Description |
|---|---|
| `train` | full training set |
| `dev` | development |
| `test` | test |
| `9_hours` | low-resource ~9h training subset |
| `1_hours` | low-resource ~1h training subset |
### Merged `all` config
A virtual `all` config aliases the per-language parquets via glob — no extra
storage, just a YAML entry. Use it to train a single multilingual model:
```python
ds = load_dataset("shangeth/mls-mimi-codes", "all", split="train")
```
Caveat: there is no `language` column, and `speaker_id` is per-language —
so speaker IDs may collide across languages (e.g. German speaker `12345`
is unrelated to French speaker `12345`). If you need clean per-language
speaker bookkeeping, load each language config separately.
## Schema
| Column | Type | Notes |
|---|---|---|
| `id` | string | utterance ID, format `{speaker}_{chapter}_{segment}` |
| `text` | string | transcript, mixed-case as-is from MLS |
| `speaker_id` | int32 | speaker ID (parsed from MLS string) |
| `chapter_id` | int32 | chapter 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`](https://huggingface.co/kyutai/mimi) @ 24 kHz, 12.5 fps
- **Resampling:** MLS audio is 48 kHz opus → resampled to 24 kHz at extraction
- **Codebooks:** all 8 extracted; slice `codes[:k]` for fewer
- **Source:** [`facebook/multilingual_librispeech`](https://huggingface.co/datasets/facebook/multilingual_librispeech)
## Usage
```python
from datasets import load_dataset
import torch
ds = load_dataset("shangeth/mls-mimi-codes", "german", split="dev")
ex = ds[0]
codes = torch.tensor(ex["codes"], dtype=torch.long) # [8, n_frames]
print(ex["id"], "| speaker:", ex["speaker_id"], "|", ex["text"][:60])
# Decode back to 24 kHz audio
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()
```
## 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}
}
@inproceedings{pratap2020mls,
title = {MLS: A Large-Scale Multilingual Dataset for Speech Research},
author = {Pratap, Vineel and Xu, Qiantong and Sriram, Anuroop and Synnaeve, Gabriel and Collobert, Ronan},
booktitle = {Interspeech},
year = {2020}
}
```
## License
CC-BY-4.0 (inherited from MLS / LibriVox).
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