---
language:
- am
license: apache-2.0
task_categories:
- automatic-speech-recognition
tags:
- asr
- amharic
- ethiopian
- telecom
- benchmark
- wer
- cer
- gender-fairness
- gsma
pretty_name: GSMA EthioTelecomBench
size_categories:
- n<1K
configs:
- config_name: WER
  data_files:
  - split: default
    path: data/WER.csv
- config_name: CER
  data_files:
  - split: default
    path: data/CER.csv
- config_name: gerndergap
  data_files:
  - split: default
    path: data/Gender_GAP.csv
---

# GSMA EthioTelecomBench: Amharic ASR Benchmark for Telecom Domain

## Overview

GSMA EthioTelecomBench is a comprehensive benchmark for evaluating Automatic Speech Recognition (ASR) systems on Amharic speech, with a focus on telecom customer service conversations. This dataset contains evaluation results from **12 models** across **7 evaluation splits**.

## Audio Conditions

The benchmark evaluates models across different audio conditions:

| Column Name | Description |
|-------------|-------------|
| Clean Studio | High-quality studio recordings |
| Ambient Room Noise | Standard recordings with background noise |
| Harsh Environment | Heavily degraded audio conditions |
| Distant / Low-Volume Mic | Low-quality microphone recordings |
| Wideband Phone (VoLTE) | Real telecom VoLTE recordings |
| Randomized Phone Channel | Random phone channel conditions |
| Narrowband 2G + Packet Loss | Degraded 2G network with packet loss |

## Text Normalization

All WER and CER metrics are computed **after applying full text normalization** to both reference and predicted transcriptions:

1. **Number-to-Text Conversion**: Arabic numerals are converted to Amharic text (e.g., "123" → "አንድ መቶ ሃያ ሶስት")
2. **Punctuation Removal**: All punctuation marks (including Ge'ez punctuation ፠፡።፣፤፥፦፧፨) are removed
3. **Character Normalization**: Ge'ez character variants are normalized to canonical forms:
   - ሀ/ሐ/ኅ/ኻ/ኃ → ሃ
   - ሠ/ሡ/ሢ/ሣ/ሤ/ሥ/ሦ → ሰ/ሱ/ሲ/ሳ/ሴ/ስ/ሶ
   - ዐ/ዑ/ዒ/ዓ/ዔ/ዕ/ዖ → አ/ኡ/ኢ/አ/ኤ/እ/ኦ
   - ፀ/ፁ/ፂ/ፃ/ፄ/ፅ/ፆ → ጸ/ጹ/ጺ/ጻ/ጼ/ጽ/ጾ
   - And other labialized character normalizations
4. **Whitespace Normalization**: Multiple spaces collapsed to single space

## Evaluated Models

The benchmark includes the following model families:

### Ethio-ASR Models (badrex)
- `badrex/Ethio-ASR-amharic` - Amharic-specific model
- `badrex/Ethio-ASR-multilingual-94M` - 94M parameter multilingual
- `badrex/Ethio-ASR-multilingual-300M` - 300M parameter multilingual
- `badrex/Ethio-ASR-multilingual-600M` - 600M parameter multilingual

### OmniASR Models
- `omniASR_CTC_300M_v2` - CTC-based 300M
- `omniASR_CTC_1B_v2` - CTC-based 1B
- `omniASR_CTC_3B_v2` - CTC-based 3B
- `omniASR_LLM_300M_v2` - LLM-based 300M
- `omniASR_LLM_1B_v2` - LLM-based 1B
- `omniASR_LLM_3B_v2` - LLM-based 3B

### Baseline Models
- `facebook/mms-1b-all` - Meta's Massively Multilingual Speech
- `openai/whisper-small` - OpenAI Whisper Small

## Dataset Files

This dataset contains three CSV tables:

### 1. `WER.csv`
Word Error Rate (WER) scores for all models across all audio conditions. Lower is better.

### 2. `CER.csv`
Character Error Rate (CER) scores for all models across all audio conditions. Lower is better.

### 3. `Gender_GAP.csv`
Gender fairness analysis showing:
- Male WER/CER scores
- Female WER/CER scores
- Gender gap (Female - Male): Positive values indicate higher error rates for female speakers

## Key Findings

### Gender Fairness Analysis

Average WER gender gap (Female - Male) across models shows **female speakers are consistently disadvantaged** across all audio conditions, with gaps ranging from +3.77% to +13.64%.

### Key Observations

1. **Domain Adaptation Matters**: Models fine-tuned on Amharic (Ethio-ASR family) significantly outperform general multilingual models on telecom domain data.

2. **Gender Bias**: Most models show higher error rates for female speakers, indicating a systematic gender bias in ASR performance.

3. **Audio Quality Impact**: Performance degrades significantly on low-quality and over-augmented audio, highlighting the need for robust ASR systems.

4. **Model Size vs Performance**: Larger models (3B parameters) generally perform better, but domain-specific smaller models can be competitive.

## Usage

```python
from datasets import load_dataset

# Load WER scores
wer_data = load_dataset("SAARAI/GSMAEthioTelecomBench", data_files="data/WER.csv", split="train")

# Load CER scores
cer_data = load_dataset("SAARAI/GSMAEthioTelecomBench", data_files="data/CER.csv", split="train")

# Load Gender Gap analysis
gender_data = load_dataset("SAARAI/GSMAEthioTelecomBench", data_files="data/Gender_GAP.csv", split="train")
```