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Add comprehensive README with benchmark findings

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  1. README.md +26 -26
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- # EthioTelecomBench: Amharic ASR Benchmark for Telecom Domain
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Overview
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- 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**.
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  ## Text Normalization
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@@ -114,34 +135,13 @@ Average WER gender gap (Female - Male) across models:
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  from datasets import load_dataset
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  # Load WER scores
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- wer_data = load_dataset("SAARAI/EthioTelecomBench", split="WER")
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  # Load CER scores
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- cer_data = load_dataset("SAARAI/EthioTelecomBench", split="CER")
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  # Load Gender Gap analysis
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- gender_data = load_dataset("SAARAI/EthioTelecomBench", split="Gender_GAP")
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  ```
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- ## Citation
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- If you use this benchmark, please cite:
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-
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- ```bibtex
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- @misc{ethiotelebench2024,
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- title={EthioTelecomBench: A Benchmark for Amharic ASR in Telecom Domain},
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- author={SAARAI},
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- year={2024},
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- publisher={Hugging Face},
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- url={https://huggingface.co/datasets/SAARAI/EthioTelecomBench}
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- }
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- ```
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-
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- ## License
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-
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- This benchmark is released under the Apache 2.0 License.
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-
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- ## Links
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-
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- - [Interactive Error Visualization](https://ethio-asr-error-viz.vercel.app/)
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- - [Source Repository](https://github.com/IsraelAbebe/Ethio-ASR)
 
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+ ---
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+ language:
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+ - am
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+ license: apache-2.0
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+ task_categories:
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+ - automatic-speech-recognition
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+ tags:
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+ - asr
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+ - amharic
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+ - ethiopian
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+ - telecom
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+ - benchmark
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+ - wer
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+ - cer
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+ - gender-fairness
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+ - gsma
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+ pretty_name: GSMA EthioTelecomBench
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+ size_categories:
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+ - n<1K
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+ ---
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+
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+ # GSMA EthioTelecomBench: Amharic ASR Benchmark for Telecom Domain
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  ## Overview
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+ 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**.
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  ## Text Normalization
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  from datasets import load_dataset
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  # Load WER scores
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+ wer_data = load_dataset("SAARAI/GSMAEthioTelecomBench", data_files="data/WER.csv", split="train")
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  # Load CER scores
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+ cer_data = load_dataset("SAARAI/GSMAEthioTelecomBench", data_files="data/CER.csv", split="train")
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  # Load Gender Gap analysis
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+ gender_data = load_dataset("SAARAI/GSMAEthioTelecomBench", data_files="data/Gender_GAP.csv", split="train")
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  ```
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