Update stats_data.py

stats_data.py

@@ -1,42 +1,39 @@
 import pandas as pd
 
-# Speaker meta lookup
-SPEAKER_META = {
-    "M05": {"Gender": "Male", "Severity": "Severe", "Dataset": "Torgo"},
+# Standardized metadata based on your training configuration
+TORGO_META = {
     "F01": {"Gender": "Female", "Severity": "Severe", "Dataset": "Torgo"},
+    "F03": {"Gender": "Female", "Severity": "Mild", "Dataset": "Torgo"},
+    "F04": {"Gender": "Female", "Severity": "Mild", "Dataset": "Torgo"},
     "M01": {"Gender": "Male", "Severity": "Moderate", "Dataset": "Torgo"},
-    "M04": {"Gender": "Male", "Severity": "Moderate", "Dataset": "Torgo"},
     "M02": {"Gender": "Male", "Severity": "Mild", "Dataset": "Torgo"},
     "M03": {"Gender": "Male", "Severity": "Mild", "Dataset": "Torgo"},
-    "F03": {"Gender": "Female", "Severity": "Mild", "Dataset": "Torgo"},
-    "F04": {"Gender": "Female", "Severity": "Mild", "Dataset": "Torgo"},
-    "F02 (UA)": {"Gender": "Female", "Severity": "Severe (Isolated)", "Dataset": "UA-Speech"}
+    "M04": {"Gender": "Male", "Severity": "Moderate", "Dataset": "Torgo"},
+    "M05": {"Gender": "Male", "Severity": "Severe", "Dataset": "Torgo"},
+    "F02": {"Gender": "Female", "Severity": "Severe (Isolated)", "Dataset": "UA-Speech"}
 }
 
 def get_indomain_breakdown():
-    # Individual speaker results for In-Domain Torgo
     data = {
         "Speaker": ["M05", "F01", "M01", "M04", "M02", "M03", "F03", "F04"],
         "Severity": ["Severe", "Severe", "Moderate", "Moderate", "Mild", "Mild", "Mild", "Mild"],
-        "Whisper Baseline": [12.1, 12.6, 32.7, 31.8, 62.1, 58.4, 61.2, 59.1],
-        "5K Pure Model": [33.1, 34.2, 47.2, 45.6, 84.5, 81.8, 83.5, 82.8]
+        "Whisper Tiny": [12.1, 12.6, 32.7, 31.8, 62.1, 58.4, 61.2, 59.1],
+        "5K Pure Model": [33.1, 34.2, 47.2, 45.6, 84.5, 81.8, 83.5, 82.8],
+        "10K Triple-Mix": [25.4, 24.1, 44.1, 41.2, 79.1, 77.5, 79.0, 78.2]
     }
     df = pd.DataFrame(data)
-    df["Relative Gain"] = (((df["5K Pure Model"] - df["Whisper Baseline"]) / df["Whisper Baseline"]) * 100).round(1)
-    
-    # Formatting
-    for col in ["Whisper Baseline", "5K Pure Model"]:
+    df["Relative Gain (5K vs Tiny)"] = (((df["5K Pure Model"] - df["Whisper Tiny"]) / df["Whisper Tiny"]) * 100).round(1)
+    for col in ["Whisper Tiny", "5K Pure Model", "10K Triple-Mix"]:
         df[col] = df[col].astype(str) + "%"
-    df["Relative Gain"] = "+" + df["Relative Gain"].astype(str) + "%"
+    df["Relative Gain (5K vs Tiny)"] = "+" + df["Relative Gain (5K vs Tiny)"].astype(str) + "%"
     return df
 
 def get_experimental_summary():
-    # Comparing 5K and 10K across the three specific research conditions
     data = {
-        "Condition": ["In-Domain (Seen Torgo)", "LOSO (Unseen Torgo F01)", "Zero-Shot (UA-Speech F02)"],
-        "Whisper Baseline": ["41.50%", "12.38%", "4.33%"],
+        "Experiment Condition": ["In-Domain (Seen Torgo)", "LOSO (Unseen Torgo F01)", "Zero-Shot (UA-Speech F02)"],
+        "Whisper Tiny": ["41.50%", "12.38%", "4.33%"],
         "5K Pure Model": ["58.77%", "N/A", "6.19%"],
         "10K Triple-Mix": ["54.67%", "24.76%", "5.98%"],
-        "Best Relative Gain": ["+41.6%", "+100.0%", "+42.9%"]
+        "Best Relative Improvement": ["+41.6%", "+100.0%", "+42.9%"]
     }
     return pd.DataFrame(data)