| --- |
| language: [xog] |
| license: cc-by-4.0 |
| multilinguality: monolingual |
| task_categories: [text-to-speech, automatic-speech-recognition] |
| tags: [lusoga, soga, speech, tts, asr, african-languages, low-resource] |
| pretty_name: Lusoga Speech Data (Pooled) |
| size_categories: [1K<n<10K] |
| --- |
| |
| # Lusoga Speech Data (Pooled) |
|
|
| A **~39.0-hour** Lusoga speech corpus, drawn from a single source (WAXAL) and |
| filtered to only genuinely transcribed audio. 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`), |
| `sog_asr` config — crowdsourced, image-prompted speech collected via Makerere |
| University's "Yogera" app (the same pipeline used for WAXAL's Masaaba data). 6,723 |
| clips, 39.0h, `source` = `waxal`. |
|
|
| `train`+`validation`+`test` splits are pooled together (intentional, same policy |
| already applied to WAXAL's `_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` | always `sog_asr` | |
| | `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/lusoga-speech-data", "manifest.parquet", repo_type="dataset") |
| df = pd.read_parquet(mp) |
| |
| row = df.iloc[0] |
| shard_path = hf_hub_download("Professor/lusoga-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 **Lusoga TTS/ASR research**, in particular as finetuning data for a |
| multilingual TTS model that doesn't natively support Lusoga. Speech is |
| crowdsourced and image-prompted, with many speakers and variable recording |
| conditions rather than studio-controlled. At ~39h, this is AfroNet's smallest |
| published language so far -- 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 Makerere University's "Yogera" |
| initiative, and to Lusoga-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. |
|
|
