| --- |
| 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. |
|
|
