Endpointing Model Benchmark Report
Model: /data/smart-turn-v3.2-gpu.onnx
Generated: 2026-01-07 17:59:39 UTC
Accuracy Results
Total Samples: 31,527
Unique Languages: 🇸🇦 Arabic, 🇧🇩 Bengali, 🇩🇰 Danish, 🇩🇪 German, 🇬🇧 🇺🇸 English, 🇫🇮 Finnish, 🇫🇷 French, 🇮🇳 Hindi, 🇮🇩 Indonesian, 🇮🇹 Italian, 🇯🇵 Japanese, 🇰🇷 Korean, 🇮🇳 Marathi, 🇳🇱 Dutch, 🇳🇴 Norwegian, 🇵🇱 Polish, 🇵🇹 Portuguese, 🇷🇺 Russian, 🇪🇸 Spanish, 🇹🇷 Turkish, 🇺🇦 Ukrainian, 🇻🇳 Vietnamese, 🇨🇳 Chinese
Unique Datasets: chirp3_1, chirp3_2, chirp3_3_short, human_5, human_convcollector_1, liva_1, midcentury_1, mundo_1, orpheus_endfiller_1, orpheus_grammar_1, orpheus_midfiller_1, rime_2
Overall Performance
| Metric | Sample Count | Accuracy (%) | Precision | Recall | F1 | FPR (%) | FNR (%) |
|---|---|---|---|---|---|---|---|
| Overall | 31,527 | 93.71 | 0.931 | 0.944 | 0.937 | 3.51 | 2.78 |
Performance by Language
| Language | Sample Count | Accuracy (%) | Precision | Recall | F1 | FPR (%) | FNR (%) |
|---|---|---|---|---|---|---|---|
| 🇰🇷 Korean | 889 | 97.64 | 0.977 | 0.975 | 0.976 | 1.12 | 1.24 |
| 🇯🇵 Japanese | 834 | 97.12 | 0.974 | 0.969 | 0.971 | 1.32 | 1.56 |
| 🇹🇷 Turkish | 966 | 97.00 | 0.967 | 0.973 | 0.970 | 1.66 | 1.35 |
| 🇳🇱 Dutch | 1,398 | 96.92 | 0.966 | 0.975 | 0.970 | 1.79 | 1.29 |
| 🇩🇪 German | 1,322 | 96.60 | 0.957 | 0.976 | 0.966 | 2.19 | 1.21 |
| 🇵🇹 Portuguese | 1,398 | 95.49 | 0.948 | 0.960 | 0.954 | 2.58 | 1.93 |
| 🇮🇩 Indonesian | 971 | 95.47 | 0.939 | 0.971 | 0.955 | 3.09 | 1.44 |
| 🇫🇮 Finnish | 1,010 | 95.25 | 0.950 | 0.954 | 0.952 | 2.48 | 2.28 |
| 🇵🇱 Polish | 974 | 95.17 | 0.946 | 0.952 | 0.949 | 2.57 | 2.26 |
| 🇺🇦 Ukrainian | 929 | 95.05 | 0.943 | 0.952 | 0.947 | 2.69 | 2.26 |
| 🇮🇹 Italian | 782 | 95.01 | 0.949 | 0.951 | 0.950 | 2.56 | 2.43 |
| 🇫🇷 French | 1,252 | 94.73 | 0.941 | 0.956 | 0.949 | 3.04 | 2.24 |
| 🇬🇧 🇺🇸 English | 7,820 | 94.71 | 0.940 | 0.953 | 0.946 | 2.98 | 2.31 |
| 🇷🇺 Russian | 1,468 | 94.41 | 0.937 | 0.958 | 0.947 | 3.41 | 2.18 |
| 🇩🇰 Danish | 779 | 93.58 | 0.930 | 0.944 | 0.937 | 3.59 | 2.82 |
| 🇳🇴 Norwegian | 1,014 | 93.00 | 0.929 | 0.934 | 0.932 | 3.65 | 3.35 |
| 🇮🇳 Hindi | 1,284 | 92.76 | 0.930 | 0.931 | 0.931 | 3.66 | 3.58 |
| 🇪🇸 Spanish | 1,783 | 91.53 | 0.908 | 0.920 | 0.914 | 4.54 | 3.93 |
| 🇨🇳 Chinese | 929 | 90.53 | 0.899 | 0.918 | 0.908 | 5.27 | 4.20 |
| 🇸🇦 Arabic | 947 | 89.12 | 0.869 | 0.925 | 0.896 | 7.07 | 3.80 |
| 🇮🇳 Marathi | 774 | 88.11 | 0.870 | 0.901 | 0.885 | 6.85 | 5.04 |
| 🇧🇩 Bengali | 1,000 | 85.10 | 0.847 | 0.849 | 0.848 | 7.50 | 7.40 |
| 🇻🇳 Vietnamese | 1,004 | 82.47 | 0.814 | 0.840 | 0.826 | 9.56 | 7.97 |
Performance by Dataset
| Dataset | Sample Count | Accuracy (%) | Precision | Recall | F1 | FPR (%) | FNR (%) |
|---|---|---|---|---|---|---|---|
| midcentury_1 | 1,044 | 98.85 | 0.992 | 0.984 | 0.988 | 0.38 | 0.77 |
| rime_2 | 394 | 98.22 | 0.982 | 0.976 | 0.979 | 0.76 | 1.02 |
| human_5 | 402 | 97.01 | 0.977 | 0.955 | 0.966 | 1.00 | 1.99 |
| orpheus_endfiller_1 | 181 | 95.58 | 0.988 | 0.924 | 0.955 | 0.55 | 3.87 |
| chirp3_1 | 16,254 | 94.80 | 0.943 | 0.954 | 0.948 | 2.89 | 2.31 |
| liva_1 | 3,831 | 94.49 | 0.934 | 0.958 | 0.946 | 3.39 | 2.11 |
| orpheus_grammar_1 | 163 | 92.02 | 0.919 | 0.929 | 0.924 | 4.29 | 3.68 |
| chirp3_3_short | 104 | 91.35 | 0.933 | 0.875 | 0.903 | 2.88 | 5.77 |
| chirp3_2 | 8,428 | 90.76 | 0.898 | 0.918 | 0.908 | 5.17 | 4.07 |
| human_convcollector_1 | 90 | 90.00 | 0.837 | 0.947 | 0.889 | 7.78 | 2.22 |
| orpheus_midfiller_1 | 140 | 87.86 | 0.859 | 0.873 | 0.866 | 6.43 | 5.71 |
| mundo_1 | 496 | 87.70 | 0.871 | 0.882 | 0.877 | 6.45 | 5.85 |