---
language:
- en
license: mit
task_categories:
- text-to-speech
- audio-to-audio
pretty_name: Jenny TTS with Mimi Codes
tags:
- audio
- speech
- mimi
- codec
- tts
- jenny
---

# Jenny TTS with Mimi Codes

This dataset adds Mimi codec codes to [reach-vb/jenny_tts_dataset](https://huggingface.co/datasets/reach-vb/jenny_tts_dataset).

## Dataset Description

Each sample contains:
- **audio**: Original Jenny TTS audio resampled to 24kHz (Mimi's native rate)
- **codes**: 8-layer Mimi codec codes (list of 8 lists of integers)
- **transcription**: Original text transcription
- **transcription_normalised**: Normalized transcription

## Stats

- **Samples**: 20,978
- **Audio Sample Rate**: 24kHz (resampled from original 48kHz)
- **Codec**: Mimi (kyutai/mimi) with 8 codebooks

## Usage

```python
from datasets import load_dataset

ds = load_dataset("mazesmazes/jenny-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["transcription_normalised"]

# Decode codes back to audio (requires moshi_mlx or transformers)
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

- [reach-vb/jenny_tts_dataset](https://huggingface.co/datasets/reach-vb/jenny_tts_dataset) - Original Jenny TTS recordings

## License

MIT (same as source dataset)