File size: 4,547 Bytes
18b345c ec62098 18b345c ec62098 4b00467 ec62098 4520220 ec62098 4b00467 ec62098 4b00467 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | ---
license: cc-by-4.0
language:
- en
task_categories:
- text-to-speech
- automatic-speech-recognition
tags:
- mimi
- neural-codec
- speech-synthesis
- speech-recognition
- librispeech
- audio-tokens
pretty_name: LibriSpeech Mimi Codes
size_categories:
- 100K<n<1M
---
# LibriSpeech — Mimi Codes
Pre-extracted [Kyutai Mimi](https://huggingface.co/kyutai/mimi) neural-codec tokens for the
[LibriSpeech](https://www.openslr.org/12) corpus — multi-speaker English audiobook readings
from the LibriVox project.
**This dataset contains codes only, not audio.** For waveforms, use any of the LibriSpeech
mirrors (e.g. [openslr/librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr));
these codes let you skip the ~hours of GPU extraction needed to train Mimi-based speech models.
## Schema
One row per utterance:
| Column | Type | Notes |
|---|---|---|
| `id` | string | `{speaker_id}-{chapter_id}-{utterance_id:04d}`, e.g. `103-1240-0000` |
| `text` | string | lowercased transcript |
| `speaker_id` | int32 | LibriSpeech speaker ID |
| `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]` for 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:** sourced from LibriSpeech's `.trans.txt` files, **lowercased** (the raw
release is ALL-UPPER)
## Splits
Each standard LibriSpeech split is a separate HF split (hyphens replaced with underscores):
| HF Split | Upstream | Approx. rows | Notes |
|---|---|---|---|
| `train_clean_100` | `train-clean-100` | ~28.5k | clean read speech, ~100 h |
| `train_clean_360` | `train-clean-360` | ~104.0k | clean read speech, ~360 h |
| `train_other_500` | `train-other-500` | ~148.7k | noisier/accented, ~500 h |
| `dev_clean` | `dev-clean` | ~2.7k | dev set, clean |
| `dev_other` | `dev-other` | ~2.9k | dev set, noisier |
| `test_clean` | `test-clean` | ~2.6k | test set, clean |
| `test_other` | `test-other` | ~2.9k | test set, noisier |
Splits are added incrementally — consult the "Files" tab or `load_dataset(...).splits` for
the exact subset currently available.
## Usage
```python
from datasets import load_dataset
import torch
ds = load_dataset("shangeth/librispeech-mimi-codes", split="train_clean_100")
ex = ds[0]
codes = torch.tensor(ex["codes"], dtype=torch.long) # [8, n_frames]
print(f"{ex['id']} (speaker {ex['speaker_id']}) → {ex['text'][:60]}")
print("codes:", codes.shape, "duration:", codes.shape[1] / 12.5, "s")
# Use only the first 3 codebooks:
codes_3 = codes[:3]
```
Streaming (no full download):
```python
ds = load_dataset("shangeth/librispeech-mimi-codes", split="train_clean_360", streaming=True)
for ex in ds.take(10):
print(ex["id"], len(ex["codes"]), "codebooks")
```
Decode 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
```
## License & Attribution
LibriSpeech is released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).
The derived Mimi codes inherit this license — **attribution is required**. Please cite
both the original corpus and this dataset when redistributing.
## 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)
## Citations
```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{panayotov2015librispeech,
title = {Librispeech: an ASR corpus based on public domain audio books},
author = {Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle = {ICASSP},
year = {2015}
}
```
## Related
Used to train the [Wren](https://huggingface.co/shangeth/Wren-TTS-360M-v1) series of
speech-text multimodal models.
|