File size: 3,455 Bytes
41a43c2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | ---
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.
|