--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-si-bank-v3 results: [] datasets: - IshanSuga/sinhala-bank-speech language: - si --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-si-bank-v3 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [sinhala-bank-speech](https://huggingface.co/datasets/IshanSuga/sinhala-bank-speech) dataset. It achieves the following results on the evaluation set: - Loss: 0.7054 - Wer Ortho: 64.1791 - Wer: 47.4041 ## Model description The [IshanSuga/whisper-small-si-bank-v3](https://huggingface.co/IshanSuga/whisper-small-si-bank-v3) model is an Automatic Speech Recognition (ASR) system fine-tuned on Sinhala speech data, specifically tailored for banking-related conversations. Built upon OpenAI’s Whisper-Small architecture, this model leverages transfer learning to enhance transcription accuracy for Sinhala audio. ### Key Features: Pretrained Foundation: Based on [OpenAI’s Whisper-Small](https://huggingface.co/openai/whisper-small), a robust ASR model trained on a large multilingual dataset. Fine-tuned for Sinhala: Adapted using domain-specific Sinhala banking conversations to improve transcription quality in financial settings. ## Intended uses & limitations Use Cases: Banking Chatbots & Assistants – Enables automated customer service and inquiry handling. Speech-to-Text Applications – Converts Sinhala speech into text for documentation and accessibility. Financial Call Center Analytics – Helps in analyzing and transcribing customer interactions. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 250 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:| | No log | 0.4 | 2 | 2.4227 | 196.5174 | 222.3476 | | No log | 0.8 | 4 | 2.4223 | 184.0796 | 225.9594 | | No log | 1.2 | 6 | 2.4041 | 178.1095 | 230.6998 | | No log | 1.6 | 8 | 2.3475 | 200.0 | 235.8916 | | No log | 2.0 | 10 | 2.2372 | 204.4776 | 230.6998 | | No log | 2.4 | 12 | 2.1041 | 226.8657 | 223.7020 | | No log | 2.8 | 14 | 2.0246 | 243.7811 | 231.6027 | | No log | 3.2 | 16 | 1.9415 | 251.7413 | 225.0564 | | No log | 3.6 | 18 | 1.8591 | 261.6915 | 219.8646 | | No log | 4.0 | 20 | 1.7851 | 254.2289 | 222.3476 | | No log | 4.4 | 22 | 1.7161 | 180.5970 | 219.4131 | | No log | 4.8 | 24 | 1.6398 | 179.6020 | 219.6388 | | 2.0087 | 5.2 | 26 | 1.5743 | 162.1891 | 224.1535 | | 2.0087 | 5.6 | 28 | 1.5253 | 161.1940 | 229.5711 | | 2.0087 | 6.0 | 30 | 1.4871 | 148.2587 | 234.3115 | | 2.0087 | 6.4 | 32 | 1.4461 | 138.8060 | 182.3928 | | 2.0087 | 6.8 | 34 | 1.4145 | 126.8657 | 156.2077 | | 2.0087 | 7.2 | 36 | 1.3807 | 114.9254 | 148.0813 | | 2.0087 | 7.6 | 38 | 1.3431 | 127.8607 | 153.0474 | | 2.0087 | 8.0 | 40 | 1.2989 | 102.4876 | 144.2438 | | 2.0087 | 8.4 | 42 | 1.2561 | 96.5174 | 127.3138 | | 2.0087 | 8.8 | 44 | 1.2180 | 99.0050 | 119.1874 | | 2.0087 | 9.2 | 46 | 1.1857 | 93.0348 | 116.9300 | | 2.0087 | 9.6 | 48 | 1.1512 | 94.5274 | 91.1964 | | 1.1238 | 10.0 | 50 | 1.1099 | 86.5672 | 99.5485 | | 1.1238 | 10.4 | 52 | 1.0849 | 83.5821 | 113.0926 | | 1.1238 | 10.8 | 54 | 1.0564 | 83.0846 | 81.9413 | | 1.1238 | 11.2 | 56 | 1.0203 | 85.0746 | 104.7404 | | 1.1238 | 11.6 | 58 | 0.9934 | 82.5871 | 97.9684 | | 1.1238 | 12.0 | 60 | 0.9693 | 84.0796 | 82.1670 | | 1.1238 | 12.4 | 62 | 0.9399 | 83.0846 | 80.8126 | | 1.1238 | 12.8 | 64 | 0.8985 | 80.0995 | 71.3318 | | 1.1238 | 13.2 | 66 | 0.8841 | 78.1095 | 68.3973 | | 1.1238 | 13.6 | 68 | 0.8707 | 77.6119 | 71.1061 | | 1.1238 | 14.0 | 70 | 0.8469 | 77.1144 | 69.5260 | | 1.1238 | 14.4 | 72 | 0.8612 | 77.1144 | 101.8059 | | 1.1238 | 14.8 | 74 | 0.8603 | 73.6318 | 57.7878 | | 0.4443 | 15.2 | 76 | 0.8440 | 75.1244 | 57.5621 | | 0.4443 | 15.6 | 78 | 0.8534 | 72.6368 | 82.1670 | | 0.4443 | 16.0 | 80 | 0.8377 | 74.6269 | 60.0451 | | 0.4443 | 16.4 | 82 | 0.8370 | 71.1443 | 67.7201 | | 0.4443 | 16.8 | 84 | 0.8740 | 71.1443 | 69.5260 | | 0.4443 | 17.2 | 86 | 0.8431 | 71.1443 | 58.4650 | | 0.4443 | 17.6 | 88 | 0.8481 | 75.6219 | 62.7540 | | 0.4443 | 18.0 | 90 | 0.8321 | 75.1244 | 66.8172 | | 0.4443 | 18.4 | 92 | 0.8490 | 69.6517 | 55.3047 | | 0.4443 | 18.8 | 94 | 0.8685 | 73.1343 | 66.1400 | | 0.4443 | 19.2 | 96 | 0.8263 | 72.6368 | 59.1422 | | 0.4443 | 19.6 | 98 | 0.8659 | 74.6269 | 58.9165 | | 0.1597 | 20.0 | 100 | 0.8714 | 72.6368 | 60.9481 | | 0.1597 | 20.4 | 102 | 0.8245 | 74.1294 | 53.9503 | | 0.1597 | 20.8 | 104 | 0.8476 | 70.1493 | 51.4673 | | 0.1597 | 21.2 | 106 | 0.8953 | 72.6368 | 57.1106 | | 0.1597 | 21.6 | 108 | 0.8871 | 70.6468 | 55.5305 | | 0.1597 | 22.0 | 110 | 0.8335 | 69.1542 | 53.0474 | | 0.1597 | 22.4 | 112 | 0.9095 | 71.6418 | 80.1354 | | 0.1597 | 22.8 | 114 | 0.9014 | 97.0149 | 83.9729 | | 0.1597 | 23.2 | 116 | 0.7924 | 77.1144 | 53.9503 | | 0.1597 | 23.6 | 118 | 0.8353 | 66.6667 | 48.7585 | | 0.1597 | 24.0 | 120 | 0.9749 | 69.6517 | 63.6569 | | 0.1597 | 24.4 | 122 | 0.8675 | 71.1443 | 53.9503 | | 0.1597 | 24.8 | 124 | 0.7980 | 69.6517 | 50.3386 | | 0.1073 | 25.2 | 126 | 0.8297 | 69.6517 | 59.5937 | | 0.1073 | 25.6 | 128 | 0.9054 | 120.3980 | 79.0068 | | 0.1073 | 26.0 | 130 | 0.8254 | 67.6617 | 51.6930 | | 0.1073 | 26.4 | 132 | 0.8260 | 67.1642 | 49.8871 | | 0.1073 | 26.8 | 134 | 0.8305 | 68.1592 | 49.6614 | | 0.1073 | 27.2 | 136 | 0.8382 | 66.6667 | 54.4018 | | 0.1073 | 27.6 | 138 | 0.8302 | 68.1592 | 58.6907 | | 0.1073 | 28.0 | 140 | 0.7815 | 71.6418 | 55.9819 | | 0.1073 | 28.4 | 142 | 0.7478 | 67.6617 | 48.9842 | | 0.1073 | 28.8 | 144 | 0.7830 | 76.6169 | 57.7878 | | 0.1073 | 29.2 | 146 | 0.7150 | 67.1642 | 48.7585 | | 0.1073 | 29.6 | 148 | 0.7300 | 68.6567 | 48.3070 | | 0.0584 | 30.0 | 150 | 0.7099 | 70.6468 | 51.2415 | | 0.0584 | 30.4 | 152 | 0.7285 | 66.6667 | 47.6298 | | 0.0584 | 30.8 | 154 | 0.7717 | 68.6567 | 60.7223 | | 0.0584 | 31.2 | 156 | 0.8301 | 67.6617 | 54.6275 | | 0.0584 | 31.6 | 158 | 0.7430 | 68.1592 | 56.4334 | | 0.0584 | 32.0 | 160 | 0.6981 | 67.6617 | 47.8555 | | 0.0584 | 32.4 | 162 | 0.7349 | 66.6667 | 55.3047 | | 0.0584 | 32.8 | 164 | 0.7662 | 63.6816 | 49.6614 | | 0.0584 | 33.2 | 166 | 0.8003 | 66.6667 | 55.0790 | | 0.0584 | 33.6 | 168 | 0.7360 | 63.6816 | 48.0813 | | 0.0584 | 34.0 | 170 | 0.7166 | 67.1642 | 49.8871 | | 0.0584 | 34.4 | 172 | 0.7899 | 72.6368 | 57.5621 | | 0.0584 | 34.8 | 174 | 0.7921 | 81.0945 | 58.6907 | | 0.0069 | 35.2 | 176 | 0.7251 | 67.6617 | 45.3725 | | 0.0069 | 35.6 | 178 | 0.7142 | 71.1443 | 51.9187 | | 0.0069 | 36.0 | 180 | 0.7564 | 74.6269 | 61.1738 | | 0.0069 | 36.4 | 182 | 0.7012 | 70.1493 | 54.4018 | | 0.0069 | 36.8 | 184 | 0.6962 | 65.1741 | 43.7923 | | 0.0069 | 37.2 | 186 | 0.7316 | 66.6667 | 50.3386 | | 0.0069 | 37.6 | 188 | 0.7482 | 67.1642 | 46.9526 | | 0.0069 | 38.0 | 190 | 0.6728 | 60.6965 | 40.6321 | | 0.0069 | 38.4 | 192 | 0.6576 | 63.1841 | 45.8239 | | 0.0069 | 38.8 | 194 | 0.7134 | 67.1642 | 52.1445 | | 0.0069 | 39.2 | 196 | 0.6794 | 69.1542 | 50.3386 | | 0.0069 | 39.6 | 198 | 0.6344 | 67.1642 | 47.8555 | | 0.0062 | 40.0 | 200 | 0.6440 | 60.6965 | 42.6637 | | 0.0062 | 40.4 | 202 | 0.7188 | 66.1692 | 49.8871 | | 0.0062 | 40.8 | 204 | 0.7213 | 64.1791 | 44.9210 | | 0.0062 | 41.2 | 206 | 0.6740 | 64.6766 | 42.2122 | | 0.0062 | 41.6 | 208 | 0.6472 | 61.6915 | 41.5350 | | 0.0062 | 42.0 | 210 | 0.6664 | 60.6965 | 43.3409 | | 0.0062 | 42.4 | 212 | 0.7102 | 59.7015 | 44.2438 | | 0.0062 | 42.8 | 214 | 0.7214 | 60.6965 | 47.4041 | | 0.0062 | 43.2 | 216 | 0.6949 | 63.6816 | 50.1129 | | 0.0062 | 43.6 | 218 | 0.6944 | 62.6866 | 42.4379 | | 0.0062 | 44.0 | 220 | 0.6759 | 64.1791 | 44.0181 | | 0.0062 | 44.4 | 222 | 0.6834 | 65.6716 | 45.5982 | | 0.0062 | 44.8 | 224 | 0.7508 | 67.6617 | 53.2731 | | 0.0042 | 45.2 | 226 | 0.7720 | 72.6368 | 67.9458 | | 0.0042 | 45.6 | 228 | 0.7001 | 65.6716 | 50.5643 | | 0.0042 | 46.0 | 230 | 0.6872 | 66.6667 | 45.3725 | | 0.0042 | 46.4 | 232 | 0.7127 | 67.6617 | 46.2754 | | 0.0042 | 46.8 | 234 | 0.7334 | 65.6716 | 48.3070 | | 0.0042 | 47.2 | 236 | 0.7485 | 62.1891 | 45.3725 | | 0.0042 | 47.6 | 238 | 0.7175 | 64.1791 | 49.8871 | | 0.0042 | 48.0 | 240 | 0.7251 | 63.6816 | 50.1129 | | 0.0042 | 48.4 | 242 | 0.6949 | 62.6866 | 48.3070 | | 0.0042 | 48.8 | 244 | 0.6684 | 60.6965 | 44.2438 | | 0.0042 | 49.2 | 246 | 0.6712 | 62.1891 | 48.0813 | | 0.0042 | 49.6 | 248 | 0.6939 | 66.1692 | 50.1129 | | 0.0034 | 50.0 | 250 | 0.7054 | 64.1791 | 47.4041 | ### Framework versions - Transformers 4.48.3 - Pytorch 2.5.1+cu124 - Datasets 3.3.1 - Tokenizers 0.21.0