---
license: cc-by-4.0
language:
- 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
---

# Amharic Corpus — Clean Speech

**146,359 clips · 408.5 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 | 146,359 |
| Total duration | ~408.5 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/amharic-corpus", 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 the AI lab of Ethiopia — an artificial intelligence company in Addis Ababa building technology that works in the languages people here actually speak.

We exist because Ethiopian languages have been left behind by modern AI. Amharic alone is spoken by more than 50 million people, yet it remains one of the world's most under-resourced languages in speech and language technology. So we do the whole thing ourselves: we collect our own datasets, train our own models, and run our own infrastructure — no rented intelligence, no foreign data flows, and Ethiopian data stays in Ethiopia.

**Our products:**
- [**voice.et**](https://voice.et) — production speech AI: automatic speech recognition and natural text-to-speech voices for Amharic, Afaan Oromoo, Tigrinya and more, available as APIs and live voice products.
- [**yakal.et**](https://yakal.et) — the Ethiopian AI console: chat, images, video, voice and an AI site builder in Amharic, Afaan Oromoo and English, prepaid in Birr.

**Our services:** custom speech models tuned to an organization's domain (banking, healthcare, government services), integration into IVR systems, contact centers and telephony, and ethically sourced Ethiopian-language data for research.

**Open by default.** We believe the way to build an Ethiopian AI ecosystem is to fuel it — this corpus is our first major open release, and everything we publish will appear first at [huggingface.co/Lab-et](https://huggingface.co/Lab-et).

This corpus was generated by **OmniVoice**, our in-house Amharic text-to-speech system ([model here](https://huggingface.co/Lab-et/omnivoice-amharic)) — a good example of how our products build our data, and our data builds better products.

## Citation

```bibtex
@dataset{labet_amharic_corpus,
  title  = {Amharic Corpus — 400+ hours of synthetic call-center speech},
  author = {lab.et},
  year   = {2026},
  url    = {https://huggingface.co/datasets/Lab-et/amharic-corpus},
  note   = {Generated with OmniVoice, the in-house Amharic TTS of lab.et}
}
```