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README.md

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+---
+license: cc-by-4.0
+language:
+- am
+- am
+pretty_name: OmniVoice Amharic Clean Speech — 500 Hours of Synthetic Call-Center Speech
+tags:
+- speech
+- asr
+- amharic
+- ethiopia
+- synthetic-speech
+- call-center
+- text-to-speech
+size_categories:
+- 100K<n<1M
+task_categories:
+- automatic-speech-recognition
+- text-to-speech
+---
+
+# OmniVoice Amharic Clean Speech — 500 hours
+
+**179,170 clips · 501.3 hours · Amharic (አማርኛ) · 16 kHz · synthetic, studio-quality channel**
+
+A large-scale **synthetic** Amharic speech corpus generated entirely by **OmniVoice**, the in-house Amharic text-to-speech system of [lab.et](https://lab.et) — the AI lab of Ethiopia. The audio simulates Ethiopian call-center conversations across banking, transport, telecom and everyday service scenarios, rendered with 62 distinct synthetic speaker identities (mixed genders).
+
+## Why this dataset exists
+
+Amharic is spoken by 50+ million people but remains one of the world's most under-resourced languages for speech AI. Real Amharic call-center audio is scarce, expensive and privacy-sensitive. We built OmniVoice to close that gap: a high-fidelity Amharic TTS that lets us generate unlimited, perfectly-transcribed training data for automatic speech recognition and speech interfaces — without touching a single real customer's voice.
+
+This release is the **clean channel** of the corpus: near-field, studio-quality speech. (A telephony-degraded channel variant exists but is not part of this release.)
+
+## Dataset details
+
+| | |
+|---|---|
+| Clips | 179,170 |
+| Total duration | ~501.3 hours |
+| Language | Amharic (`am-ET`) |
+| Sample rate | 16 kHz, mono, 16-bit WAV |
+| Speakers | 62 synthetic identities (via voice cloning) |
+| Generator | OmniVoice Amharic TTS (`omnivoice-amharic-finetuned-v3-clone`) |
+| Domain | call-center dialogues — banking, transport, telecom, services |
+
+## Structure
+
+Parquet shards in `data/`, each row:
+
+| Field | Type | Description |
+|---|---|---|
+| `audio` | Audio | 16 kHz WAV bytes (embedded) |
+| `text` | string | the exact transcript (what the TTS was asked to say) |
+| `duration` | float | clip duration in seconds |
+| `speaker_id` | string | synthetic speaker identity (e.g. `spk025`) |
+| `gender` | string | `male` / `female` |
+| `kind` | string | `base` (short utterance) / `long` (multi-turn) |
+| `source_id` | string | internal generation id |
+
+Load it with one line:
+
+```python
+from datasets import load_dataset
+ds = load_dataset("Lab-et/omnivoice-amharic-clean-500h", split="train")
+```
+
+## Important limitations — read before use
+
+1. **This is synthetic speech.** It was machine-generated by an Amharic TTS system, not spoken by humans. It is excellent for ASR pre-training, domain adaptation and telephony research, but a model trained *only* on this data will not fully generalize to real human speech — combine it with human-recorded corpora (e.g. [Google WAXAL](https://huggingface.co/datasets/google/wax)) for best results.
+2. **Transcripts are exact by construction** (the TTS speaks the text), so there is no annotation noise in the label — but that also means the data carries no real-world disfluencies, accents or code-switching.
+3. **Speaker identities are synthetic** — cloned voices do not correspond to any real person.
+4. **No real customer data** was used at any stage: the dialogue text is synthetic, written by LLMs for training scenarios, then spoken by OmniVoice.
+
+## Ethics & compliance
+
+- No real person's voice or personal data is contained in this dataset.
+- Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — attribution, any purpose, including commercial ASR training.
+- Generated and published by an Ethiopian lab, for the benefit of Ethiopian-language AI.
+
+## About lab.et
+
+[lab.et](https://lab.et) is an artificial intelligence lab in Addis Ababa building AI for Ethiopian languages. Its products are [voice.et](https://voice.et) (speech recognition & synthesis APIs) and [yakal.et](https://yakal.et) (the Ethiopian AI console).
+
+If this corpus helps your research or product, we'd love to hear about it — [lab.et](https://lab.et).
+
+## Citation
+
+```bibtex
+@dataset{labet_omnivoice_amharic_clean_500h,
+  title  = {OmniVoice Amharic Clean Speech — 500 hours of synthetic call-center speech},
+  author = {lab.et},
+  year   = {2026},
+  url    = {https://huggingface.co/datasets/Lab-et/omnivoice-amharic-clean-500h},
+  note   = {Generated with OmniVoice, the in-house Amharic TTS of lab.et}
+}
+```