Add comprehensive README with benchmark findings

README.md

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+# EthioTelecomBench: Amharic ASR Benchmark for Telecom Domain
+
+## Overview
+
+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:
+
+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
+
+## 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
+
+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/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:
+
+```bibtex
+@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.
+
+## Links
+
+- [Interactive Error Visualization](https://ethio-asr-error-viz.vercel.app/)
+- [Source Repository](https://github.com/IsraelAbebe/Ethio-ASR)