import gradio as gr import pandas as pd from datasets import load_dataset from jiwer import wer, cer import os from datetime import datetime import re # Load the Bambara ASR dataset print("Loading dataset...") dataset = load_dataset("sudoping01/bambara-asr-benchmark", name="default")["train"] references = {row["id"]: row["text"] for row in dataset} # Load or initialize the leaderboard leaderboard_file = "leaderboard.csv" if not os.path.exists(leaderboard_file): pd.DataFrame(columns=["submitter", "WER", "CER", "timestamp"]).to_csv(leaderboard_file, index=False) else: print(f"Loaded existing leaderboard with {len(pd.read_csv(leaderboard_file))} entries") def normalize_text(text): """ Normalize text for WER/CER calculation: - Convert to lowercase - Remove punctuation - Replace multiple spaces with single space - Strip leading/trailing spaces """ if not isinstance(text, str): text = str(text) # Convert to lowercase text = text.lower() # Remove punctuation, keeping spaces text = re.sub(r'[^\w\s]', '', text) # Normalize whitespace text = re.sub(r'\s+', ' ', text).strip() return text def calculate_metrics(predictions_df): """Calculate WER and CER for predictions.""" results = [] for _, row in predictions_df.iterrows(): id_val = row["id"] if id_val not in references: print(f"Warning: ID {id_val} not found in references") continue reference = normalize_text(references[id_val]) hypothesis = normalize_text(row["text"]) # Print detailed info for first few entries if len(results) < 5: print(f"ID: {id_val}") print(f"Reference: '{reference}'") print(f"Hypothesis: '{hypothesis}'") # Skip empty strings if not reference or not hypothesis: print(f"Warning: Empty reference or hypothesis for ID {id_val}") continue # Split into words for jiwer reference_words = reference.split() hypothesis_words = hypothesis.split() if len(results) < 5: print(f"Reference words: {reference_words}") print(f"Hypothesis words: {hypothesis_words}") # Calculate metrics try: # Make sure we're not comparing identical strings if reference == hypothesis: print(f"Warning: Identical strings for ID {id_val}") # Force a small difference if the strings are identical # This is for debugging - remove in production if needed if len(hypothesis_words) > 0: # Add a dummy word to force non-zero WER hypothesis_words.append("dummy_debug_token") hypothesis = " ".join(hypothesis_words) # Calculate WER and CER sample_wer = wer(reference, hypothesis) sample_cer = cer(reference, hypothesis) if len(results) < 5: print(f"WER: {sample_wer}, CER: {sample_cer}") results.append({ "id": id_val, "reference": reference, "hypothesis": hypothesis, "wer": sample_wer, "cer": sample_cer }) except Exception as e: print(f"Error calculating metrics for ID {id_val}: {str(e)}") if not results: raise ValueError("No valid samples for WER/CER calculation") # Calculate average metrics avg_wer = sum(item["wer"] for item in results) / len(results) avg_cer = sum(item["cer"] for item in results) / len(results) return avg_wer, avg_cer, results def process_submission(submitter_name, csv_file): try: # Read and validate the uploaded CSV df = pd.read_csv(csv_file) print(f"Processing submission from {submitter_name} with {len(df)} rows") if len(df) == 0: return "Error: Uploaded CSV is empty.", None if set(df.columns) != {"id", "text"}: return f"Error: CSV must contain exactly 'id' and 'text' columns. Found: {', '.join(df.columns)}", None if df["id"].duplicated().any(): dup_ids = df[df["id"].duplicated()]["id"].unique() return f"Error: Duplicate IDs found: {', '.join(map(str, dup_ids[:5]))}", None # Check if IDs match the reference dataset missing_ids = set(references.keys()) - set(df["id"]) extra_ids = set(df["id"]) - set(references.keys()) if missing_ids: return f"Error: Missing {len(missing_ids)} IDs in submission. First few missing: {', '.join(map(str, list(missing_ids)[:5]))}", None if extra_ids: return f"Error: Found {len(extra_ids)} extra IDs not in reference dataset. First few extra: {', '.join(map(str, list(extra_ids)[:5]))}", None # Calculate WER and CER try: avg_wer, avg_cer, detailed_results = calculate_metrics(df) # Debug information print(f"Calculated metrics - WER: {avg_wer:.4f}, CER: {avg_cer:.4f}") print(f"Processed {len(detailed_results)} valid samples") # Check for suspiciously low values if avg_wer < 0.001: print("WARNING: WER is extremely low - likely an error") return "Error: WER calculation yielded suspicious results (near-zero). Please check your submission CSV.", None except Exception as e: print(f"Error in metrics calculation: {str(e)}") return f"Error calculating metrics: {str(e)}", None # Update the leaderboard leaderboard = pd.read_csv(leaderboard_file) timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") new_entry = pd.DataFrame( [[submitter_name, avg_wer, avg_cer, timestamp]], columns=["submitter", "WER", "CER", "timestamp"] ) leaderboard = pd.concat([leaderboard, new_entry]).sort_values("WER") leaderboard.to_csv(leaderboard_file, index=False) return f"Submission processed successfully! WER: {avg_wer:.4f}, CER: {avg_cer:.4f}", leaderboard except Exception as e: print(f"Error processing submission: {str(e)}") return f"Error processing submission: {str(e)}", None # Create the Gradio interface with gr.Blocks(title="Bambara ASR Leaderboard") as demo: gr.Markdown( """ # Bambara ASR Leaderboard Upload a CSV file with 'id' and 'text' columns to evaluate your ASR predictions. The 'id's must match those in the dataset. [View the dataset here](https://huggingface.co/datasets/MALIBA-AI/bambara_general_leaderboard_dataset). - **WER**: Word Error Rate (lower is better). - **CER**: Character Error Rate (lower is better). """ ) with gr.Row(): submitter = gr.Textbox(label="Submitter Name or Model Name", placeholder="e.g., MALIBA-AI/asr") csv_upload = gr.File(label="Upload CSV File", file_types=[".csv"]) submit_btn = gr.Button("Submit") output_msg = gr.Textbox(label="Status", interactive=False) leaderboard_display = gr.DataFrame( label="Leaderboard", value=pd.read_csv(leaderboard_file), interactive=False ) submit_btn.click( fn=process_submission, inputs=[submitter, csv_upload], outputs=[output_msg, leaderboard_display] ) # Print startup message print("Starting Bambara ASR Leaderboard app...") # Launch the app if __name__ == "__main__": demo.launch(share=True)