---
license: apache-2.0
language:
  - am
tags:
  - omnivoice
  - audio-tokens
  - preprocessed
  - tts
  - amharic
  - webdataset
size_categories:
  - 10K<n<100K
---

# OmniVoice Amharic v4 — Pre-extracted Audio Tokens

Pre-extracted [HiggsAudioV2](https://huggingface.co/eustlb/higgs-audio-v2-tokenizer) audio tokens for training [OmniVoice](https://arxiv.org/abs/2604.00688) Amharic TTS.

Downloading these tokens (~648 MB, ~2 min) **skips the 30+ minute token extraction step** (steps 1-3 of the training pipeline).

## Dataset Stats

| Stat | Value |
|---|---|
| **Total samples** | 81,731 |
| **Total audio** | ~331 hours |
| **Avg sample duration** | ~14.6 seconds |
| **Audio shards** | 164 (`.tar` files) |
| **Text shards** | 164 (`.jsonl` files) |
| **Samples per shard** | 500 (last shard: 231) |
| **Total size** | ~648 MB |
| **Tokenizer** | `eustlb/higgs-audio-v2-tokenizer` |
| **Codebooks** | 8 (vocab size: 1025, mask_id: 1024) |

## Source Datasets

| Dataset | Description |
|---|---|
| [google/WaxalNLP](https://huggingface.co/datasets/google/WaxalNLP) (`amh_asr`) | Largest source — Amharic ASR data |
| [gheero-Leyu/leyu-amharic-addis-ababa-dialect](https://huggingface.co/datasets/gheero-Leyu/leyu-amharic-addis-ababa-dialect) | Addis Ababa dialect only (other dialects excluded to avoid accent issues) |
| [surafelabebe/amharic_clear_audio_tts](https://huggingface.co/datasets/surafelabebe/amharic_clear_audio_tts) | Clean TTS-oriented recordings |
| [chappM/amharic-bdu-asr](https://huggingface.co/datasets/chappM/amharic-bdu-asr) | BDU ASR dataset |

## Filters Applied

- **Duration**: 1–25 seconds
- **Text length**: max 300 characters
- **Script**: Ge'ez (ግዕዝ) script only — Latin transliterations filtered out
- **Dialect**: Only Addis Ababa from Leyu dataset (Gojjam/Wello/Shewa/Gonder excluded)

## File Structure

```
├── data.lst                    # Manifest: paths, sample counts, durations per shard
├── audios/
│   ├── shard-000000.tar        # WebDataset tar: tokenized audio arrays
│   ├── shard-000001.tar
│   ├── ...
│   └── shard-000163.tar
└── txts/
    ├── shard-000000.jsonl      # Matching text/metadata for each audio shard
    ├── shard-000001.jsonl
    ├── ...
    └── shard-000163.jsonl
```

### `data.lst` format
Each line: `<audio_tar_path> <text_jsonl_path> <num_samples> <total_duration_seconds>`

> **Note**: The paths in `data.lst` reference the original Colab extraction path (`/content/omnivoice_amharic_v4/wds_tokens/...`). The training script rewrites these to local paths after download.

## Usage

These tokens are automatically downloaded by [`train_waxal_v4.py`](https://huggingface.co/demeleww/omnivoice-amharic-training/blob/main/train_waxal_v4.py):

```python
from huggingface_hub import snapshot_download

# Download all tokens (~648MB)
snapshot_download(
    repo_id="demeleww/omnivoice-amharic-v4-tokens",
    repo_type="dataset",
    local_dir="/content/omnivoice_amharic_v4/wds_tokens",
    ignore_patterns=[".gitattributes", "README.md"],
)
```

Or run the full training pipeline which handles this automatically:

```python
!pip install -q omnivoice webdataset tqdm huggingface-hub
from huggingface_hub import login
login()
!wget -O /content/train_waxal_v4.py "https://huggingface.co/demeleww/omnivoice-amharic-training/resolve/main/train_waxal_v4.py"
%run /content/train_waxal_v4.py
```

## Related

- **Training script**: [demeleww/omnivoice-amharic-training](https://huggingface.co/demeleww/omnivoice-amharic-training)
- **Base model**: [k2-fsa/OmniVoice](https://huggingface.co/k2-fsa/OmniVoice)
- **OmniVoice paper**: [arxiv 2604.00688](https://arxiv.org/abs/2604.00688)
- **Georgian recipe reference**: [NMikka/omnivoice-finetuned-ka](https://huggingface.co/NMikka/omnivoice-finetuned-ka)