---
language:
- en
license: cc-by-4.0
task_categories:
- text-to-speech
- audio-to-audio
pretty_name: Dataset with Mimi Codes
tags:
- audio
- speech
- mimi
- codec
- s2s
---

# Dataset with Mimi Codes

This dataset adds Mimi codec codes to [parler-tts/libritts_r_filtered](https://huggingface.co/datasets/parler-tts/libritts_r_filtered).

## Dataset Description

Each sample contains:
- **audio**: Audio resampled to 24kHz (Mimi's native rate)
- **codes**: 8-layer Mimi codec codes (list of 8 lists of integers)
- **text**: Text transcription (from `text_normalized` column)
- Additional columns preserved from source dataset

## Stats

- **Source**: parler-tts/libritts_r_filtered
- **Splits**: train.clean.360
- **Samples**: 112,326
- **Audio Sample Rate**: 24kHz
- **Codec**: Mimi (kyutai/mimi) with 8 codebooks

## Usage

```python
from datasets import load_dataset

ds = load_dataset("mazesmazes/libritts-mimi", split="train")

# Access audio and codes together
sample = ds[0]
audio = sample["audio"]  # {'array': [...], 'sampling_rate': 24000}
codes = sample["codes"]  # 8 lists of codec indices
text = sample["text"]

# Decode codes back to audio
import torch
from transformers import MimiModel

mimi = MimiModel.from_pretrained("kyutai/mimi")
codes_tensor = torch.tensor(codes).unsqueeze(0)  # (1, 8, seq_len)
with torch.no_grad():
    decoded = mimi.decode(codes_tensor)
    waveform = decoded.audio_values  # (1, 1, samples) at 24kHz
```

## Source Dataset

- [parler-tts/libritts_r_filtered](https://huggingface.co/datasets/parler-tts/libritts_r_filtered)

## License

Same as source dataset.