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from pathlib import Path |
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DIR_OUTPUT_REQUESTS = Path("requested_models") |
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EVAL_REQUESTS_PATH = Path("eval_requests") |
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banner_url = "https://huggingface.co/datasets/reach-vb/random-images/resolve/main/phoneme_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> 🤗 Phoneme Detection Leaderboard </b> </body> </html>" |
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INTRODUCTION_TEXT = """📐 The 🤗 Phoneme Detection Leaderboard ranks and evaluates phoneme recognition models |
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on the Hugging Face Hub. |
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\nWe report the Average [PER](https://en.wikipedia.org/wiki/Phoneme_error_rate) (⬇️ lower the better) and Average Duration. Models are ranked based on their Average PER, from lowest to highest. Check the 📈 Metrics tab to understand how the models are evaluated. |
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\nIf you want results for a model that is not listed here, you can submit a request for it to be included ✉️✨. |
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\nThe leaderboard includes phoneme recognition evaluation across multiple datasets.""" |
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CITATION_TEXT = """@misc{phoneme-detection-leaderboard, |
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title = {Phoneme Detection Leaderboard}, |
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author = {Your Name and Contributors}, |
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year = 2024, |
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publisher = {Hugging Face}, |
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howpublished = "\\url{https://huggingface.co/spaces/your-org/phoneme-detection-leaderboard}" |
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} |
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""" |
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METRICS_TAB_TEXT = """ |
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Here you will find details about the phoneme recognition metrics and datasets reported in our leaderboard. |
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## Metrics |
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Models are evaluated using the Phoneme Error Rate (PER) metric. The PER metric |
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is used to assess the accuracy of a phoneme recognition system. Models are ranked in the leaderboard based |
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on their PER, lowest to highest. |
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### Phoneme Error Rate (PER) |
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Phoneme Error Rate is used to measure the **accuracy** of automatic phoneme recognition systems. It calculates the percentage |
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of phonemes in the system's output that differ from the reference (correct) phoneme sequence. **A lower PER value indicates higher accuracy**. |
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The PER is calculated using sequence alignment between predicted and reference phoneme sequences, taking into account: |
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- Substitutions (S): predicted phoneme differs from reference |
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- Deletions (D): reference phoneme missing in prediction |
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- Insertions (I): predicted phoneme not in reference |
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``` |
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PER = (S + D + I) / N * 100 |
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``` |
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Where N is the total number of reference phonemes. |
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## How to reproduce our results |
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The Phoneme Detection Leaderboard is an effort to benchmark open source phoneme recognition models. |
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Along with the Leaderboard we're open-sourcing the codebase used for running these evaluations. |
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P.S. We'd love to know which other models you'd like us to benchmark next. Contributions are more than welcome! ♥️ |
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## Benchmark datasets |
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Evaluating Phoneme Recognition systems requires diverse datasets with phonetic transcriptions. We use multiple datasets to obtain robust evaluation scores for each model. |
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| Dataset | Description | Language | License | |
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|---------|-------------|----------|---------| |
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| phoneme_asr | General phoneme recognition dataset | English | Open | |
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| kids_phoneme_md | Children's speech phoneme dataset | English | Open | |
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For more details on the individual datasets and how models are evaluated, refer to our documentation. |
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""" |
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LEADERBOARD_CSS = """ |
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#leaderboard-table th .header-content { |
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white-space: nowrap; |
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} |
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#phoneme-table th .header-content { |
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white-space: nowrap; |
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} |
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#phoneme-table th:hover { |
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background-color: var(--table-row-focus); |
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} |
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""" |
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