| --- |
| 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. |
|
|
