README.md

---
license: cc-by-nc-4.0
task_categories:
- automatic-speech-recognition
- text-to-speech
language:
- en
---

# LibriTTS (train, English) — DualCodec pre-tokenized

DualCodec (12 Hz) pre-tokenized speech for omni speech–vision MLLM training.
Source: LibriTTS train.

## Contents (train split)

| | |
|---|---|
| **Total samples** | ≈ 356,000 (audio–transcript pairs) |
| **Total audio** | ≈ 556 hours |
| **WebDataset shards** | see repo (`.tar`) |
| **Mean / median duration** | 5.63s / 4.42s |
| **p90 / max duration** | 11.58s / 29.83s |
| **Length cap** | 30s |

*Counts/hours are computed by sampling shards (per-shard sample count × #shards); duration stats from a scanned subset.*

### Duration distribution
| bucket | share |
|---|---|
| 0-5s | 55.7% |
| 5-10s | 29.6% |
| 10-15s | 10.0% |
| 15-20s | 3.5% |
| >20s | 1.2% |

## Format (WebDataset `.tar`)

Each sample shares a key and consists of `{key}.sem.npy`, `{key}.ac.npy`, `{key}.txt`:
- **`.sem.npy`** — DualCodec **semantic** codes, `int16`, shape `(T,)`, vocab 16384.
- **`.ac.npy`** — DualCodec **acoustic** codes, `int16`, shape `(7, T)`, vocab 4096 per codebook.
- **`.txt`** — transcript.

Frame rate is **12 Hz**, so duration in seconds = `T / 12`.

## Length & padding

Codes are stored at their **true variable length** (no padding baked in). Samples
longer than the training grid are handled at load time. During training, sequences
are padded to a fixed **240-frame (20 s) grid** by appending the DualCodec
**encoded-silence column** (semantic code **3716**, with its matching acoustic
column) — i.e. padding is applied as *code-level silence*, not waveform zeros, so
the padded region matches the codec's silence distribution. Samples exceeding the
grid (> 240 frames) are skipped rather than cropped to preserve audio–text alignment.

## Intended use

ASR (speech→text) and TTS (text→speech) pretraining. Semantic + acoustic codes reconstruct waveforms via the DualCodec decoder.