Datasets:

SaarAI
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GSMAEthioTelecomBench

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.

Text Normalization

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

Number-to-Text Conversion: Arabic numerals are converted to Amharic text (e.g., "123" → "አንድ መቶ ሃያ ሶስት")
Punctuation Removal: All punctuation marks (including Ge'ez punctuation ፠፡።፣፤፥፦፧፨) are removed
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
Whitespace Normalization: Multiple spaces collapsed to single space

Dataset Description

This benchmark evaluates ASR models on various challenging conditions:

Split	Description
`augmented`	Standard test set with data augmentation
`low_audio`	Low-quality audio samples
`over_augmented`	Heavily augmented audio
`over_augmented_telecom`	Heavily augmented telecom-specific audio
`telecom`	Real telecom customer service recordings
`telecom_random`	Random subset of telecom recordings
`train`	Training set evaluation (for reference)

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 Splits

This dataset contains three tables as different splits:

1. `WER` Split

Word Error Rate (WER) scores for all models across all evaluation splits. Lower is better.

2. `CER` Split

Character Error Rate (CER) scores for all models across all evaluation splits. Lower is better.

3. `Gender_GAP` Split

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

Best Performing Models by Split

Split	Best Model	WER (%)
augmented	badrex/Ethio-ASR-amharic	38.72
low_audio	omniASR_LLM_3B_v2	27.26
over_augmented	badrex/Ethio-ASR-amharic	42.08
over_augmented_telecom	badrex/Ethio-ASR-amharic	74.74
telecom	badrex/Ethio-ASR-amharic	45.13
telecom_random	badrex/Ethio-ASR-amharic	46.66
train	omniASR_LLM_3B_v2	27.29

Gender Fairness Analysis

Average WER gender gap (Female - Male) across models:

Split	Avg Gap (%)	Interpretation
augmented	+3.80	Female disadvantaged
low_audio	+4.06	Female disadvantaged
over_augmented	+3.77	Female disadvantaged
over_augmented_telecom	+13.64	Female disadvantaged
telecom	+6.89	Female disadvantaged
telecom_random	+6.62	Female disadvantaged
train	+4.69	Female disadvantaged

Key Observations

Domain Adaptation Matters: Models fine-tuned on Amharic (Ethio-ASR family) significantly outperform general multilingual models on telecom domain data.
Gender Bias: Most models show higher error rates for female speakers, indicating a systematic gender bias in ASR performance.
Audio Quality Impact: Performance degrades significantly on low-quality and over-augmented audio, highlighting the need for robust ASR systems.
Model Size vs Performance: Larger models (3B parameters) generally perform better, but domain-specific smaller models can be competitive.

Usage

from datasets import load_dataset

# Load WER scores
wer_data = load_dataset("SAARAI/EthioTelecomBench", split="WER")

# Load CER scores
cer_data = load_dataset("SAARAI/EthioTelecomBench", split="CER")

# Load Gender Gap analysis
gender_data = load_dataset("SAARAI/EthioTelecomBench", split="Gender_GAP")

Citation

If you use this benchmark, please cite:

@misc{ethiotelebench2024,
  title={EthioTelecomBench: A Benchmark for Amharic ASR in Telecom Domain},
  author={SAARAI},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/SAARAI/EthioTelecomBench}
}

License

This benchmark is released under the Apache 2.0 License.

EthioTelecomBench: Amharic ASR Benchmark for Telecom Domain

Overview