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--- |
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title: Post-ASR LLM N-Best Transcription Correction |
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emoji: 🏢 |
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colorFrom: yellow |
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colorTo: yellow |
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sdk: gradio |
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sdk_version: 5.21.0 |
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app_file: app.py |
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pinned: false |
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license: mit |
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short_description: Generative Error Correction (GER) Task Baseline, WER |
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--- |
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# Post-ASR Text Correction WER Leaderboard |
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This application displays a baseline Word Error Rate (WER) leaderboard for the test data in the [GenSEC-LLM/SLT-Task1-Post-ASR-Text-Correction](https://huggingface.co/datasets/GenSEC-LLM/SLT-Task1-Post-ASR-Text-Correction) dataset. |
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## Dataset Sources |
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The leaderboard shows WER metrics for multiple speech recognition sources as columns: |
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- CHiME4 |
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- CORAAL |
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- CommonVoice |
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- LRS2 |
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- LibriSpeech (Clean and Other) |
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- SwitchBoard |
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- Tedlium-3 |
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- OVERALL (aggregate across all sources) |
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## Baseline Methods |
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The leaderboard displays three baseline approaches: |
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1. **No LM Baseline**: Uses the 1-best ASR output without any correction (input1) |
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2. **N-gram Ranking**: Ranks the N-best hypotheses using a simple n-gram statistics approach and chooses the best one |
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3. **Subwords Voting Correction**: Uses a voting-based method to correct the transcript by combining information from all N-best hypotheses |
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## Metrics |
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The leaderboard displays as rows: |
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- **Number of Examples**: Count of examples in the test set for each source |
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- **Word Error Rate (No LM)**: WER between reference and 1-best ASR output |
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- **Word Error Rate (N-gram Ranking)**: WER between reference and n-gram ranked best hypothesis |
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- **Word Error Rate (Subwords Voting Correction)**: WER between reference and the voting-corrected N-best hypothesis |
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Lower WER values indicate better transcription accuracy. |
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## Table Structure |
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The leaderboard is displayed as a table with: |
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- **Rows**: Different metrics (example counts and WER values for each method) |
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- **Columns**: Different data sources (CHiME4, CORAAL, CommonVoice, etc.) and OVERALL |
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Each cell shows the corresponding metric for that specific data source. The OVERALL column shows aggregate metrics across all sources. |
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## Technical Details |
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### N-gram Ranking |
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This method scores each hypothesis in the N-best list using: |
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- N-gram statistics (4-grams) |
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- Text length |
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- N-gram variety |
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The hypothesis with the highest score is selected. |
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### Subwords Voting Correction |
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This method uses a simple voting mechanism: |
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- Groups hypotheses of the same length |
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- For each word position, chooses the most common word across all hypotheses |
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- Constructs a new transcript from these voted words |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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