---
license: cc0-1.0
task_categories:
- text-to-speech
- automatic-speech-recognition
language:
- en
tags:
- australian
- australian-english
- australian-accent
- accent
- english-accent
- speech
- voice
- audio
- tts
- text-to-speech
- asr
- speech-recognition
- voice-cloning
- librivox
- audiobook
- public-domain
pretty_name: Australian English Speech (Australian accent audio dataset, LibriVox)
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Australian English Speech

An **Australian accent** speech dataset: 110 hours of Australian
English audio with transcripts, from the Australian titles in the
[LibriVox](https://librivox.org) catalogue, cut into short clips for
**text-to-speech**, **voice cloning** and **speech recognition**.

Open English speech corpora are overwhelmingly American. This one is Australian,
public domain, and free of any redistribution or model-release restrictions.

LibriVox recordings are released into the **public domain**, and the books
themselves are public domain, so this dataset is unencumbered: train on it,
redistribute it, ship models built from it.

## What is in it

| | |
|---|---|
| clips | 61,662 |
| audio | 110.2 hours |
| readers | 214 |
| books | 37 |
| format | mono FLAC, 24 kHz |
| clip length | 3-15 s |

Each row:

| field | meaning |
|---|---|
| `audio` | the clip, 24 kHz mono |
| `text` | transcript |
| `reader` | LibriVox narrator's display name |
| `reader_id` | LibriVox reader id |
| `book` | book title |
| `author` | author(s) |
| `book_id` | LibriVox book id |
| `section` | section number within the book |
| `duration` | clip length in seconds |

```python
from datasets import load_dataset

ds = load_dataset("ablmontazer/australian-english-speech", split="train")
print(ds[0]["text"], ds[0]["audio"]["sampling_rate"])
```

`reader` is a stable speaker label: filter on it to get single-speaker data.

## How it was built

1. Australian titles taken from LibriVox's own catalogue search.
2. Each section downloaded from archive.org.
3. Gentle denoising with **DeepFilterNet 3** (`atten_lim_db=6`). LibriVox audio
   is already clean, so this only shaves the hiss rather than reshaping the
   voice.
4. **Silero VAD** segmentation into 3-15 s clips that start and end on silence.
5. Transcription with **faster-whisper large-v3-turbo**.
6. Clips whose transcript is LibriVox boilerplate (the spoken intro and outro
   that top and tail every section) are dropped.

## Things to know before you train on it

- **Transcripts are machine-generated.** Whisper is good but not perfect, and
  punctuation and capitalisation are inconsistent between clips. For anything
  where exact text matters, re-check against the public-domain source texts.
- **Accent is not verified per reader.** These are the Australian titles in the
  catalogue; LibriVox volunteers are international, so a minority of narrators
  reading Australian books are not themselves Australian. Filter by `reader` if
  you need a guaranteed accent.
- **Per-reader volume is very uneven**, from a few minutes to over ten hours.
  Balance or cap by reader if that matters for your training.
- Audio quality varies with the narrator's home recording setup, and some of
  the older recordings are effectively band-limited.

## Credits

Every recording here was made by a LibriVox volunteer who donated it to the
public domain. The narrators, and the books they read, are listed in
`CREDITS.md`.