test

app.py

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+import gradio as gr
+import pandas as pd
+from datasets import load_dataset
+import jiwer
+import numpy as np
+
+# Load the dataset
+def load_data():
+    dataset = load_dataset("GenSEC-LLM/SLT-Task1-Post-ASR-Text-Correction")
+    return dataset
+
+# Calculate WER for a group of examples
+def calculate_wer(examples):
+    if not examples:
+        return 0.0
+    
+    hypotheses = [ex["hypothesis_concatenated"].split('.')[0].strip() for ex in examples]
+    transcriptions = [ex["transcription"].strip() for ex in examples]
+    
+    wer = jiwer.wer(transcriptions, hypotheses)
+    return wer
+
+# Get WER metrics by source and split
+def get_wer_metrics(dataset):
+    results = []
+    
+    # Get unique sources
+    train_sources = set([ex["source"] for ex in dataset["train"]])
+    test_sources = set([ex["source"] for ex in dataset["test"]])
+    all_sources = sorted(list(train_sources.union(test_sources)))
+    
+    # Calculate WER for each source in train split
+    for source in all_sources:
+        train_examples = [ex for ex in dataset["train"] if ex["source"] == source]
+        train_count = len(train_examples)
+        train_wer = calculate_wer(train_examples) if train_count > 0 else np.nan
+        
+        test_examples = [ex for ex in dataset["test"] if ex["source"] == source]
+        test_count = len(test_examples)
+        test_wer = calculate_wer(test_examples) if test_count > 0 else np.nan
+        
+        results.append({
+            "Source": source,
+            "Train Count": train_count,
+            "Train WER": train_wer,
+            "Test Count": test_count,
+            "Test WER": test_wer
+        })
+    
+    # Add overall metrics
+    train_wer = calculate_wer(dataset["train"])
+    test_wer = calculate_wer(dataset["test"])
+    
+    results.append({
+        "Source": "OVERALL",
+        "Train Count": len(dataset["train"]),
+        "Train WER": train_wer,
+        "Test Count": len(dataset["test"]),
+        "Test WER": test_wer
+    })
+    
+    return pd.DataFrame(results)
+
+# Format the dataframe for display
+def format_dataframe(df):
+    df["Train WER"] = df["Train WER"].apply(lambda x: f"{x:.4f}" if not pd.isna(x) else "N/A")
+    df["Test WER"] = df["Test WER"].apply(lambda x: f"{x:.4f}" if not pd.isna(x) else "N/A")
+    return df
+
+# Main function to create the leaderboard
+def create_leaderboard():
+    try:
+        dataset = load_data()
+        metrics_df = get_wer_metrics(dataset)
+        formatted_df = format_dataframe(metrics_df)
+        return formatted_df
+    except Exception as e:
+        return pd.DataFrame({"Error": [str(e)]})
+
+# Create the Gradio interface
+with gr.Blocks(title="ASR Text Correction Leaderboard") as demo:
+    gr.Markdown("# ASR Text Correction Baseline WER Leaderboard")
+    gr.Markdown("Word Error Rate (WER) metrics for GenSEC-LLM/SLT-Task1-Post-ASR-Text-Correction dataset")
+    
+    with gr.Row():
+        refresh_btn = gr.Button("Refresh Leaderboard")
+    
+    with gr.Row():
+        leaderboard = gr.DataFrame(create_leaderboard())
+    
+    refresh_btn.click(create_leaderboard, outputs=leaderboard)
+
+if __name__ == "__main__":
+    demo.launch()