---
language: [ach]
license: cc-by-4.0
multilinguality: monolingual
task_categories: [text-to-speech, automatic-speech-recognition]
tags: [acholi, luo, speech, tts, asr, african-languages, low-resource]
pretty_name: Acholi Speech Data (Pooled)
size_categories: [1K<n<10K]
---

# Acholi Speech Data (Pooled)

A **~29.7-hour** Acholi speech corpus, pooling a crowdsourced ASR config with a
studio-quality TTS config. Part of the
[AfroNet](https://github.com/osinkolu/afronet-tts-data) multi-language TTS data
effort.

## Source

[**WAXAL**](https://huggingface.co/datasets/google/WaxalNLP) (`google/WaxalNLP`),
two configs pooled together:

- `ach_asr` — crowdsourced, image-prompted speech, many speakers. 4,401 clips, 24.8h.
- `ach_tts` — studio-quality read speech. 1,442 clips, 4.9h.

`train`+`validation`+`test` splits are pooled together across both configs
(intentional, same policy already applied to WAXAL's other `_tts` configs used
elsewhere in AfroNet); the `unlabeled` split (untranscribed audio) is excluded,
consistent with AfroNet's no-auto-transcription policy.

All audio is standardized to **16 kHz mono FLAC** (lossless), 1–30 second clips.

## Format

The dataset ships as **WebDataset-style tar shards** (`shards/shard-00000.tar` …, ~1 GB
each, one `{key}.flac` file per clip) plus a single manifest (`manifest.parquet` /
`manifest.jsonl`):

| Column | Description |
|---|---|
| `key`, `shard` | which tar file + entry holds this clip's audio |
| `text` | transcript (native script) |
| `duration` | seconds |
| `source` | always `waxal` |
| `dataset_id` | always `0` |
| `split` | `train` / `val` (250 clips held out for evaluation) |
| `speaker_id` | source-provided speaker ID |
| `gender` | speaker metadata where available |
| `domain` | which WAXAL config the clip came from: `ach_asr` or `ach_tts` |
| `dbfs`, `clip_ratio`, `sil_ratio` | cheap DSP quality proxies: loudness, fraction of clipped samples, fraction of near-silent frames |
| `has_disfluency` | always `false` |

## Usage

```python
from huggingface_hub import hf_hub_download
import pandas as pd, tarfile, io, soundfile as sf

mp = hf_hub_download("Professor/acholi-speech-data", "manifest.parquet", repo_type="dataset")
df = pd.read_parquet(mp)

row = df.iloc[0]
shard_path = hf_hub_download("Professor/acholi-speech-data", f"shards/{row.shard}", repo_type="dataset")
with tarfile.open(shard_path) as tar:
    audio_bytes = tar.extractfile(f"{row.key}.flac").read()
arr, sr = sf.read(io.BytesIO(audio_bytes))
```

The tar shards are also directly readable by the [`webdataset`](https://github.com/webdataset/webdataset)
library for streaming training pipelines.

## Intended use & limitations

Built for **Acholi TTS/ASR research**, in particular as finetuning data for a
multilingual TTS model that doesn't natively support Acholi. At ~30h, this is one
of AfroNet's smaller published languages -- useful as a warm-start/finetuning base,
but thinner than most of the collection. This is a **research aggregation**; usage
should respect WAXAL's own terms.

## License

CC BY 4.0, per the upstream [WAXAL](https://huggingface.co/datasets/google/WaxalNLP) release.

## Acknowledgments

Deep thanks to the **WAXAL** project (Google) and its Acholi-speaking contributors
for the source corpus.

This dataset was pooled by **Victor Olufemi and LyngualLabs** as part of the
[AfroNet](https://github.com/osinkolu/afronet-tts-data) multi-language TTS data effort.