---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: audio
    dtype: audio
  - name: dialect
    dtype: string
  - name: speaker_id
    dtype: string
  - name: gender
    dtype: string
  splits:
  - name: train
    num_bytes: 618082476
    num_examples: 2594
  download_size: 360222927
  dataset_size: 618082476
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
language:
- am
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
- text-to-speech
tags:
- amharic
- africa
- ethiopia
- dialect
- speech
- asr
- tts
pretty_name: Leyu Amharic Shewa Dialect
---

# Leyu Amharic - Shewa Dialect Speech Corpus

## Dataset Description

This dataset is a curated parallel speech corpus consisting of audio recordings paired with corresponding text transcripts, focused on the **Shewa dialect** of the Amharic language. It is designed to support speech technology research across multiple tasks, including Automatic Speech Recognition (ASR) and Text-to-Speech (TTS). The corpus captures dialect-specific phonetic variations, accent patterns, and prosodic characteristics that are essential for building dialect-aware and robust speech systems.

All recordings were collected in real-world environments using mobile devices, introducing natural acoustic variability that improves model generalization. Each audio–text pair underwent manual review to ensure transcript accuracy and audio clarity. By emphasizing demographic diversity and regional authenticity, the dataset contributes to the advancement of inclusive and representative speech technologies for low-resource African languages.

## Dataset Summary
| Metadata Field | Value |
|----------------|--------|
| Language | Amharic (am-ET) |
| Dialect | Shewa |
| Audio Format | .wav |
| Total Hours | 102.13 Hours |
| Recording Environment | Mobile / Crowdsourced (Verified) |

## Key Statistics
- **Total Duration:** 102.62 Hours  
- **Speaker Count:** 54 Speakers (Male: 20, Female: 34)
- **Data Quality:** Mobile-recorded and manually verified for transcript alignment.

## Data Collection & Quality Assurance
- Recorded by contributors using mobile devices in real-world environments.
- Manually reviewed to ensure transcript alignment and audio clarity.

## Data Fields
Each sample contains:
- `text` (string): transcript
- `audio` (Audio): waveform/audio file
- `dialect` (string): `shewa`
- `speaker_id` (string): anonymized speaker identifier
- `gender` (string): `male` / `female` / `unknown`

## iCog Blogs

- [Leyu: Crowdsourcing Datasets for Ethiopian Languages](https://leyu.ai/blog/1)
- [Dialects & Socioeconomics: Shaping Inclusive Language Models](https://leyu.ai/blog/3)
- [Progress of Natural Language Processing (NLP) for Ethiopian Languages – Part One](https://leyu.ai/blog/4)
- [Progress of Natural Language Processing (NLP) for Ethiopian Languages – Part Two](https://leyu.ai/blog/22)
- [Data Collection with Purpose: The Leyu Approach](https://leyu.ai/blog/23)---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: audio
    dtype: audio
  - name: dialect
    dtype: string
  - name: speaker_id
    dtype: string
  - name: gender
    dtype: string
  splits:
  - name: train
    num_bytes: 618082476
    num_examples: 2594
  download_size: 360222927
  dataset_size: 618082476
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
language:
- am
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
- text-to-speech
tags:
- amharic
- africa
- ethiopia
- dialect
- speech
- asr
- tts
pretty_name: Leyu Amharic Shewa Dialect
---

# Leyu Amharic - Shewa Dialect Speech Corpus

## Dataset Description

This dataset is a curated parallel speech corpus consisting of audio recordings paired with corresponding text transcripts, focused on the **Shewa dialect** of the Amharic language. It is designed to support speech technology research across multiple tasks, including Automatic Speech Recognition (ASR) and Text-to-Speech (TTS). The corpus captures dialect-specific phonetic variations, accent patterns, and prosodic characteristics that are essential for building dialect-aware and robust speech systems.

All recordings were collected in real-world environments using mobile devices, introducing natural acoustic variability that improves model generalization. Each audio–text pair underwent manual review to ensure transcript accuracy and audio clarity. By emphasizing demographic diversity and regional authenticity, the dataset contributes to the advancement of inclusive and representative speech technologies for low-resource African languages.

## Dataset Summary
| Metadata Field | Value |
|----------------|--------|
| Language | Amharic (am-ET) |
| Dialect | Shewa |
| Audio Format | .wav |
| Total Hours | 102.13 Hours |
| Recording Environment | Mobile / Crowdsourced (Verified) |

## Key Statistics
- **Total Duration:** 102.13 Hours  
- **Speaker Count:** 52 Speakers (Male: 20, Female: 34)
- **Data Quality:** Mobile-recorded and manually verified for transcript alignment.

## Data Collection & Quality Assurance
- Recorded by contributors using mobile devices in real-world environments.
- Manually reviewed to ensure transcript alignment and audio clarity.

## Data Fields
Each sample contains:
- `text` (string): transcript
- `audio` (Audio): waveform/audio file
- `dialect` (string): `shewa`
- `speaker_id` (string): anonymized speaker identifier
- `gender` (string): `male` / `female` / `unknown`

## iCog Blogs

- [Leyu: Crowdsourcing Datasets for Ethiopian Languages](https://leyu.ai/blog/1)
- [Dialects & Socioeconomics: Shaping Inclusive Language Models](https://leyu.ai/blog/3)
- [Progress of Natural Language Processing (NLP) for Ethiopian Languages – Part One](https://leyu.ai/blog/4)
- [Progress of Natural Language Processing (NLP) for Ethiopian Languages – Part Two](https://leyu.ai/blog/22)
- [Data Collection with Purpose: The Leyu Approach](https://leyu.ai/blog/23)