Add dataset card

README.md

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+---
+language: [ak, tw, fat]
+license: cc-by-4.0
+multilinguality: monolingual
+task_categories: [text-to-speech, automatic-speech-recognition]
+tags: [akan, twi, fante, speech, tts, asr, african-languages, low-resource]
+pretty_name: Akan Speech Data (Pooled)
+size_categories: [10K<n<100K]
+---
+
+# Akan Speech Data (Pooled)
+
+A **~75.4-hour** Akan speech corpus, pooling a crowdsourced ASR config with two
+single-narrator studio TTS configs for Akan's Twi and Fante dialects. 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`),
+three configs pooled together:
+
+- `aka_asr` — crowdsourced, image-prompted speech, many speakers. 12,751 clips, 69.5h.
+- `twi_tts` — single-narrator studio-quality read speech, Twi dialect. 793 clips, 3.0h.
+- `fat_tts` — single-narrator studio-quality read speech, Fante dialect. 874 clips, 2.9h.
+
+Twi and Fante are Akan dialects (mutually intelligible to a high degree); they're
+pooled here as one "Akan" release rather than split into separate languages. The
+`domain` field in the manifest preserves which config each clip came from
+(`aka_asr`/`twi_tts`/`fat_tts`), so you can filter by dialect/register if needed.
+
+`train`+`validation`+`test` splits are pooled together across all three configs
+(intentional, same policy already applied to WAXAL's other `_tts` configs used
+elsewhere in AfroNet); the `unlabeled` split that exists for `aka_asr` (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: `aka_asr`, `twi_tts`, or `fat_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/akan-speech-data", "manifest.parquet", repo_type="dataset")
+df = pd.read_parquet(mp)
+
+row = df.iloc[0]
+shard_path = hf_hub_download("Professor/akan-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 **Akan TTS/ASR research**, in particular as finetuning data for a
+multilingual TTS model that doesn't natively support Akan. A small slice of this
+corpus (the two `_tts` configs, ~8% of hours) is clean single-speaker studio read
+speech; the large majority (`aka_asr`) is crowdsourced, image-prompted speech with
+many speakers and more variable recording conditions. 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 Akan/Twi/Fante-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.