#!/usr/bin/env python3 """Merge Gemma 4 results into the main sf_results.csv and regenerate figures. Run after eval_multilang.py --run-gemma4 finishes: uv run python scripts/merge_gemma4.py """ import sys from pathlib import Path ROOT = Path(__file__).parent.parent sys.path.insert(0, str(Path(__file__).parent)) from runtime_cache import configure_runtime_cache configure_runtime_cache(ROOT) import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns GEMMA4_CSV = ROOT / 'results_gemma4' / 'sf_results.csv' MAIN_CSV = ROOT / 'analysis' / 'sf_results.csv' FIGURES_DIR = ROOT / 'figures' PAPER_FAMILIES = {'Whisper', 'MMS', 'SeamlessM4T', 'Gemma4'} def paper_rows(df: pd.DataFrame) -> pd.DataFrame: """Rows included in the main paper benchmark.""" return df[df['family'].isin(PAPER_FAMILIES)].copy() def merge() -> pd.DataFrame: if not GEMMA4_CSV.exists(): sys.exit(f'Gemma 4 results not found at {GEMMA4_CSV}') g4 = pd.read_csv(GEMMA4_CSV) print(f'Gemma 4 rows: {len(g4)}') print(g4[['model', 'language', 'sfr_mean', 'sfr_zero_pct']].to_string(index=False)) main = pd.read_csv(MAIN_CSV) print(f'\nExisting rows: {len(main)}') # Drop any existing Gemma 4 rows in main (in case of re-run) gemma_models = g4['model'].unique() main = main[~main['model'].isin(gemma_models)] merged = pd.concat([main, g4], ignore_index=True) merged.to_csv(MAIN_CSV, index=False) print(f'Merged rows: {len(merged)} → saved to {MAIN_CSV}') return merged def make_heatmap(df: pd.DataFrame) -> None: df = paper_rows(df) pivot = df.pivot_table( index='model', columns='language', values='sfr_mean', aggfunc='first') if pivot.empty: return short_names = ( pivot.index .str.replace('openai/whisper-large-v3-turbo', 'Whisper turbo') .str.replace('openai/whisper-', 'Whisper ') .str.replace('facebook/mms-1b-all', 'MMS 1B') .str.replace('facebook/seamless-m4t-v2-large', 'SeamlessM4T v2') .str.replace('unsloth/gemma-4-E2B-it', 'Gemma 4 E2B') ) # Sort: Whisper family first (by size), then MMS, Seamless, Gemma order = ['tiny', 'base', 'small', 'medium', 'large', 'turbo', 'MMS', 'Seamless', 'Gemma'] def sort_key(name): for i, k in enumerate(order): if k.lower() in name.lower(): return i return 99 sorted_pairs = sorted(zip(short_names.tolist(), pivot.index.tolist()), key=lambda x: sort_key(x[0])) sorted_short, sorted_model = zip(*sorted_pairs) pivot = pivot.loc[list(sorted_model)] fig, ax = plt.subplots(figsize=(max(10, len(pivot.columns) * 1.4), max(6, len(pivot) * 0.55))) sns.heatmap( pivot.values, xticklabels=pivot.columns.tolist(), yticklabels=list(sorted_short), annot=True, fmt='.1f', cmap='RdYlGn', vmin=0, vmax=100, linewidths=0.5, ax=ax, cbar_kws={'label': 'Script Fidelity (%)'}, ) ax.set_title('Script Fidelity (%) by Model and Language — FLEURS Test Sets') ax.set_xlabel('Language') ax.set_ylabel('Model') plt.tight_layout() FIGURES_DIR.mkdir(exist_ok=True) for ext in ('pdf', 'png'): out = FIGURES_DIR / f'sfr_heatmap.{ext}' fig.savefig(out, bbox_inches='tight', dpi=150) print(f'Saved: {out}') plt.close(fig) def make_scatter(df: pd.DataFrame) -> None: df = paper_rows(df) languages = sorted(df['language'].unique()) n = len(languages) ncols = 5 nrows = int(np.ceil(n / ncols)) fig, axes = plt.subplots(nrows, ncols, figsize=(17, 7.5), sharex=True, sharey=False) axes = np.array(axes).reshape(-1) zone_colors = { 'collapse': '#d73027', 'mixed': '#fdae61', 'high': '#1a9850', } for ax, lang in zip(axes, languages): sub = df[df['language'] == lang].dropna(subset=['wer_pct', 'sfr_mean']) if sub.empty: ax.set_title(lang) continue point_colors = [ zone_colors['collapse'] if v < 10 else zone_colors['mixed'] if v <= 90 else zone_colors['high'] for v in sub['sfr_mean'] ] ax.scatter(sub['sfr_mean'], sub['wer_pct'], color=point_colors, s=55, zorder=5, edgecolor='black', linewidth=0.25) ax.axvline(10, color=zone_colors['collapse'], linestyle='--', linewidth=1) ax.axvline(90, color=zone_colors['high'], linestyle='--', linewidth=1) ax.set_xlabel('Script Fidelity (%)') ax.set_ylabel('WER (%)') ax.set_title(lang.capitalize()) ax.set_xlim(-5, 105) for ax in axes[n:]: ax.axis('off') handles = [ plt.Line2D([0], [0], marker='o', color='w', label='SFR < 10%', markerfacecolor=zone_colors['collapse'], markeredgecolor='black', markersize=7), plt.Line2D([0], [0], marker='o', color='w', label='10-90%', markerfacecolor=zone_colors['mixed'], markeredgecolor='black', markersize=7), plt.Line2D([0], [0], marker='o', color='w', label='> 90%', markerfacecolor=zone_colors['high'], markeredgecolor='black', markersize=7), ] fig.legend(handles=handles, loc='lower center', ncol=3, frameon=False) plt.suptitle('WER vs Script Fidelity - FLEURS test sets', y=0.98) plt.tight_layout(rect=(0, 0.05, 1, 0.95)) for ext in ('pdf', 'png'): out = FIGURES_DIR / f'wer_vs_sfr_scatter.{ext}' fig.savefig(out, bbox_inches='tight', dpi=150) print(f'Saved: {out}') plt.close(fig) def make_georgian_detail(df: pd.DataFrame) -> None: df = paper_rows(df) sub = df[df['language'] == 'georgian'].dropna(subset=['wer_pct', 'sfr_mean']).copy() if sub.empty: return order = [ 'openai/whisper-tiny', 'openai/whisper-base', 'openai/whisper-small', 'openai/whisper-medium', 'openai/whisper-large-v2', 'openai/whisper-large-v3', 'openai/whisper-large-v3-turbo', 'facebook/mms-1b-all', 'facebook/seamless-m4t-v2-large', 'unsloth/gemma-4-E2B-it', ] labels = { 'openai/whisper-tiny': 'Whisper tiny', 'openai/whisper-base': 'Whisper base', 'openai/whisper-small': 'Whisper small', 'openai/whisper-medium': 'Whisper medium', 'openai/whisper-large-v2': 'Whisper large-v2', 'openai/whisper-large-v3': 'Whisper large-v3', 'openai/whisper-large-v3-turbo': 'Whisper turbo', 'facebook/mms-1b-all': 'MMS-1B', 'facebook/seamless-m4t-v2-large': 'SeamlessM4T-v2', 'unsloth/gemma-4-E2B-it': 'Gemma 4 E2B', } sub['model'] = pd.Categorical(sub['model'], categories=order, ordered=True) sub = sub.sort_values('model') names = [labels[str(m)] for m in sub['model']] colors = [ '#d73027' if v < 10 else '#fdae61' if v <= 90 else '#1a9850' for v in sub['sfr_mean'] ] fig, ax1 = plt.subplots(figsize=(11, 4.8)) x = np.arange(len(sub)) ax1.bar(x, sub['sfr_mean'], color=colors, edgecolor='black', linewidth=0.4) ax1.axhline(10, color='#b2182b', linestyle='--', linewidth=1) ax1.axhline(90, color='#2166ac', linestyle='--', linewidth=1) ax1.set_ylim(0, 105) ax1.set_ylabel('Script Fidelity Rate (%)') ax1.set_xticks(x) ax1.set_xticklabels(names, rotation=35, ha='right') ax2 = ax1.twinx() ax2.plot(x, sub['wer_pct'], color='black', marker='o', linewidth=1.5) ax2.set_ylabel('WER (%)') ax2.set_ylim(0, max(420, float(sub['wer_pct'].max()) * 1.1)) ax1.set_title('Georgian SFR and WER by model') fig.tight_layout() for ext in ('pdf', 'png'): out = FIGURES_DIR / f'georgian_collapse_detail.{ext}' fig.savefig(out, bbox_inches='tight', dpi=150) print(f'Saved: {out}') plt.close(fig) def print_summary(df: pd.DataFrame) -> None: df = paper_rows(df) pivot = df.pivot_table( index='model', columns='language', values='sfr_mean', aggfunc='first') print('\n=== SF% summary ===') print(pivot.round(1).to_string()) # Collapse pairs: SFR < 10% collapse = df[df['sfr_mean'] < 10][['model', 'language', 'sfr_mean', 'wer_pct']] print(f'\n=== Collapse pairs (SFR < 10%): {len(collapse)} ===') print(collapse.sort_values('sfr_mean').to_string(index=False)) if __name__ == '__main__': merged = merge() make_heatmap(merged) make_scatter(merged) make_georgian_detail(merged) print_summary(merged)