Datasets:

Professor
/

ewe-speech-data

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 multi-language TTS data effort.

Source

WAXAL (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

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 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 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 multi-language TTS data effort.