README.md

---
dataset_info:
  source: facebook/multilingual_librispeech
  format: WebDataset tar shards with DAC VAE latents
license: cc-by-4.0
task_categories:
  - automatic-speech-recognition
  - text-to-speech
---

# Multilingual LibriSpeech converted to DAC VAE latents

## Source
[facebook/multilingual_librispeech](https://huggingface.co/datasets/facebook/multilingual_librispeech)

## Format
Each tar shard (~2GB) contains samples with three files per sample:

```
{sample_key}.audio.flac       # Original audio (FLAC, original sample rate)
{sample_key}.dacvae.npy       # DAC VAE latent [T_latent, 128] numpy float32
{sample_key}.metadata.json    # All metadata + duration_seconds + chars_per_second
```

### DAC VAE Latent Format
- **Model**: [mrfakename/dacvae-watermarked](https://huggingface.co/mrfakename/dacvae-watermarked) (Facebook DACVAE)
- **Input sample rate**: 48,000 Hz (audio resampled before encoding)
- **Latent shape**: `[T_latent, 128]` where `T_latent = ceil(audio_samples / 1920)`
- **Latent rate**: 25 frames/second
- **Storage**: numpy float32

### Shard Naming
`{LANG}-{split}-{index:05d}.tar` (e.g., `EN-train-00000.tar`, `DE-train-00001.tar`)

## Loading

### With WebDataset
```python
import webdataset as wds
import numpy as np
import json
import soundfile as sf
import io

url = "https://huggingface.co/datasets/TTS-AGI/mls-dacvae/resolve/main/EN-train-00000.tar"
dataset = wds.WebDataset(url).decode()

for sample in dataset:
    audio_bytes = sample["audio.flac"]
    latent = np.load(io.BytesIO(sample["dacvae.npy"]))  # [T, 128]
    meta = json.loads(sample["metadata.json"])
    print(f"Text: {meta['text']}, Duration: {meta['duration_seconds']}s, CPS: {meta['chars_per_second']}")
```

### Decoding Latents Back to Audio
```python
from dacvae import DACVAE
from huggingface_hub import hf_hub_download
import torch, numpy as np

model = DACVAE.load(hf_hub_download("mrfakename/dacvae-watermarked", "weights.pth")).cuda().eval()
latent = np.load("sample.dacvae.npy")  # [T_latent, 128]
z = torch.from_numpy(latent.T).unsqueeze(0).cuda()  # [1, 128, T_latent]
audio_48k = model.decode(z).squeeze(0).cpu()  # [1, T_audio] at 48kHz
```

## Current Status

**Shards uploaded**: 14

### Progress by Language
| Language | Samples |
|----------|---------|
| DE_train | 11,872 |
| ES_train | 10,912 |
| FR_train | 11,824 |
| IT_train | 11,408 |
| NL_train | 11,696 |
| PL_train | 11,040 |
| PT_train | 10,736 |

## Metadata Fields
Each `metadata.json` contains:
- `dataset`: Source dataset name
- `language`: Language code
- `split`: Data split (train/dev/test)
- `sample_id`: Original sample identifier
- `text`: Transcript
- `duration_seconds`: Audio duration in seconds
- `chars_per_second`: Text characters per second of audio
- `original_sample_rate`: Original audio sample rate
- `dacvae_sample_rate`: 48000 (DAC VAE input rate)
- `latent_frames`: Number of latent time frames
- Plus all original dataset-specific fields

---
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