Update constants.py
Browse files- constants.py +18 -0
constants.py
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@@ -11,6 +11,24 @@ EVAL_REQUESTS_PATH = Path("eval_requests")
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banner_url = "https://huggingface.co/datasets/reach-vb/random-images/resolve/main/asr_leaderboard.png"
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BANNER = f'<div style="display: flex; justify-content: space-around;"><img src="{banner_url}" alt="Banner" style="width: 40vw; min-width: 300px; max-width: 600px;"> </div>'
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TITLE = "<html> <head> <style> h1 {text-align: center;} </style> </head> <body> <h1> 🤗 Open Automatic Speech Recognition Leaderboard </b> </body> </html>"
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INTRODUCTION_TEXT = "📐 Results on [EdAcc Dataset](https://huggingface.co/datasets/edinburghcstr/edacc) split by accents and gender. \
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banner_url = "https://huggingface.co/datasets/reach-vb/random-images/resolve/main/asr_leaderboard.png"
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BANNER = f'<div style="display: flex; justify-content: space-around;"><img src="{banner_url}" alt="Banner" style="width: 40vw; min-width: 300px; max-width: 600px;"> </div>'
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EXPLANATION = """
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## Why EdAcc Matters for ASR Evaluation
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The EdAcc dataset is specifically designed to evaluate the robustness of Automatic Speech Recognition (ASR) models across diverse accents and demographics. This leaderboard helps you:
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* **Assess Accent Fairness**: Compare model performance across 30+ different accents and speaker demographics
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* **Evaluate Real-World Robustness**: Understand how ASR models perform beyond standard benchmarks
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* **Make Informed Choices**: Select models that work well for your target demographics
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### How to Read the Results
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* **Average WER ⬇️**: Lower Word Error Rate (WER) is better
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* **Average per Gender**: Average WER for each gender
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* **Average per Accent**: Average WER for each accent
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* **RTFx ⬆️**: Real-Time Factor - higher means faster processing
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Use the column filter to focus on specific demographics or view all results together.
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"""
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TITLE = "<html> <head> <style> h1 {text-align: center;} </style> </head> <body> <h1> 🤗 Open Automatic Speech Recognition Leaderboard </b> </body> </html>"
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INTRODUCTION_TEXT = "📐 Results on [EdAcc Dataset](https://huggingface.co/datasets/edinburghcstr/edacc) split by accents and gender. \
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