Datasets:
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language: [ee]
license: cc-by-4.0
multilinguality: monolingual
task_categories: [text-to-speech, automatic-speech-recognition]
tags: [ewe, eve, speech, tts, asr, african-languages, low-resource]
pretty_name: Ewe Speech Data (Pooled)
size_categories: [10K<n<100K]
---
# Ewe Speech Data (Pooled)
A **~103.1-hour** Ewe 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:
- `ewe_asr` — crowdsourced, image-prompted speech, many speakers. 18,855 clips, 99.5h.
- `ewe_tts` — studio-quality read speech. 1,248 clips, 3.6h.
`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: `ewe_asr` or `ewe_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/ewe-speech-data", "manifest.parquet", repo_type="dataset")
df = pd.read_parquet(mp)
row = df.iloc[0]
shard_path = hf_hub_download("Professor/ewe-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 **Ewe TTS/ASR research**, in particular as finetuning data for a
multilingual TTS model that doesn't natively support Ewe. Speech is
predominantly crowdsourced and image-prompted, with many speakers and variable
recording conditions rather than studio-controlled. 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 Ewe-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.
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