Whisper Large v3 Turbo Persian Audiodata2
This model is a fine-tuned version of openai/whisper-large-v3-turbo on an unknown dataset. It achieves the following results on the evaluation set: Loss: 1.1979 Wer: 42.4096
Modellquelle
- Art
- LoRA
- Dateigröße
- 50,0 MB
- Basismodell
- openai/whisper-large-v3-turbo
- Version
- vadapter_model
- Aktualisiert
- 14. Sept. 2026
- Downloads
- 10
- Quelle
- Hugging Face
Quellenbeschreibung
whisper-large-v3-turbo-persian-audiodata2
This model is a fine-tuned version of openai/whisper-large-v3-turbo on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1979
- Wer: 42.4096
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.0420 | 1.0 | 5 | 1.2280 | 44.8996 |
| 1.9990 | 2.0 | 10 | 1.1840 | 45.0602 |
| 1.8969 | 3.0 | 15 | 1.1200 | 43.2932 |
| 1.7966 | 4.0 | 20 | 1.0419 | 43.7751 |
| 1.5880 | 5.0 | 25 | 0.9716 | 42.4096 |
| 1.4181 | 6.0 | 30 | 0.9191 | 38.8755 |
| 1.2844 | 7.0 | 35 | 0.8705 | 36.0643 |
| 1.0876 | 8.0 | 40 | 0.8371 | 38.0723 |
| 0.9473 | 9.0 | 45 | 0.8437 | 41.0442 |
| 0.9232 | 10.0 | 50 | 0.8159 | 38.7149 |
| 0.9232 | 26.0 | 52 | 0.8091 | 38.7952 |
| 0.9232 | 27.0 | 54 | 0.8043 | 39.3574 |
| 0.5928 | 28.0 | 56 | 0.8029 | 40.1606 |
| 0.5928 | 29.0 | 58 | 0.7987 | 38.3133 |
| 0.4714 | 30.0 | 60 | 0.8038 | 37.0281 |
| 0.4714 | 31.0 | 62 | 0.8098 | 36.3052 |
| 0.4714 | 32.0 | 64 | 0.8083 | 36.7871 |
| 0.4555 | 33.0 | 66 | 0.8136 | 38.7149 |
| 0.4555 | 34.0 | 68 | 0.8217 | 39.4378 |
| 0.4328 | 35.0 | 70 | 0.8310 | 38.6345 |
| 0.4328 | 36.0 | 72 | 0.8371 | 37.5904 |
| 0.4328 | 37.0 | 74 | 0.8482 | 37.9116 |
| 0.4061 | 38.0 | 76 | 0.8510 | 37.9116 |
| 0.4061 | 39.0 | 78 | 0.8545 | 40.0803 |
| 0.2561 | 40.0 | 80 | 0.8577 | 40.4819 |
| 0.2561 | 41.0 | 82 | 0.8642 | 40.8032 |
| 0.2561 | 42.0 | 84 | 0.8782 | 39.5181 |
| 0.3224 | 43.0 | 86 | 0.8928 | 40.9639 |
| 0.3224 | 44.0 | 88 | 0.9002 | 41.7671 |
| 0.2982 | 45.0 | 90 | 0.9020 | 40.6426 |
| 0.2982 | 46.0 | 92 | 0.9061 | 42.1687 |
| 0.2982 | 47.0 | 94 | 0.9262 | 41.4458 |
| 0.2625 | 48.0 | 96 | 0.9401 | 41.6867 |
| 0.2625 | 49.0 | 98 | 0.9421 | 40.1606 |
| 0.2262 | 50.0 | 100 | 0.9564 | 43.8554 |
| 0.2262 | 51.0 | 102 | 0.9636 | 45.7028 |
| 0.3451 | 21.0 | 105 | 0.9795 | 40.8835 |
| 0.2854 | 22.0 | 110 | 1.0011 | 46.1847 |
| 0.2876 | 23.0 | 115 | 0.9650 | 42.2490 |
| 0.2733 | 24.0 | 120 | 0.9405 | 39.9197 |
| 0.2690 | 25.0 | 125 | 1.0017 | 41.6867 |
| 0.2461 | 26.0 | 130 | 0.9712 | 39.0361 |
| 0.2338 | 27.0 | 135 | 0.9927 | 39.9197 |
| 0.2520 | 28.0 | 140 | 1.0054 | 41.8474 |
| 0.2184 | 29.0 | 145 | 0.9881 | 40.0 |
| 0.2064 | 30.0 | 150 | 0.9915 | 40.5622 |
| 0.1959 | 31.0 | 155 | 1.0218 | 40.6426 |
| 0.1867 | 32.0 | 160 | 1.0504 | 40.4819 |
| 0.1774 | 33.0 | 165 | 1.0113 | 39.7590 |
| 0.1821 | 34.0 | 170 | 1.0357 | 39.9197 |
| 0.1561 | 35.0 | 175 | 1.0310 | 40.4819 |
| 0.1429 | 36.0 | 180 | 1.0668 | 42.4096 |
| 0.1307 | 37.0 | 185 | 1.0383 | 41.8474 |
| 0.1147 | 38.0 | 190 | 1.0794 | 41.0442 |
| 0.1106 | 39.0 | 195 | 1.0610 | 40.8835 |
| 0.1093 | 40.0 | 200 | 1.0934 | 41.6867 |
| 0.0982 | 41.0 | 205 | 1.0682 | 40.6426 |
| 0.0879 | 42.0 | 210 | 1.0832 | 39.7590 |
| 0.0855 | 43.0 | 215 | 1.1129 | 40.8835 |
| 0.0716 | 44.0 | 220 | 1.1060 | 42.4096 |
| 0.0799 | 45.0 | 225 | 1.0980 | 41.0442 |
| 0.0729 | 46.0 | 230 | 1.1165 | 41.2048 |
| 0.0690 | 47.0 | 235 | 1.1068 | 40.4016 |
| 0.0622 | 48.0 | 240 | 1.1066 | 40.2410 |
| 0.0604 | 49.0 | 245 | 1.0999 | 42.0884 |
| 0.0532 | 50.0 | 250 | 1.1072 | 42.0884 |
| 0.0507 | 51.0 | 255 | 1.1105 | 41.7671 |
| 0.0449 | 52.0 | 260 | 1.1121 | 42.2490 |
| 0.0402 | 53.0 | 265 | 1.1475 | 41.6867 |
| 0.0374 | 54.0 | 270 | 1.1638 | 41.4458 |
| 0.0366 | 55.0 | 275 | 1.1283 | 42.4096 |
| 0.0349 | 56.0 | 280 | 1.1476 | 41.8474 |
| 0.0331 | 57.0 | 285 | 1.1761 | 41.2851 |
| 0.0293 | 58.0 | 290 | 1.1688 | 41.5261 |
| 0.0317 | 59.0 | 295 | 1.1367 | 41.2048 |
| 0.0272 | 60.0 | 300 | 1.1569 | 41.2048 |
| 0.0268 | 61.0 | 305 | 1.1896 | 42.2490 |
| 0.0251 | 62.0 | 310 | 1.1667 | 42.4900 |
| 0.0243 | 63.0 | 315 | 1.1719 | 42.1687 |
| 0.0252 | 64.0 | 320 | 1.1886 | 44.1767 |
| 0.0244 | 65.0 | 325 | 1.1842 | 42.5703 |
| 0.0223 | 66.0 | 330 | 1.1770 | 42.0080 |
| 0.0210 | 67.0 | 335 | 1.1844 | 42.4096 |
| 0.0205 | 68.0 | 340 | 1.1825 | 42.2490 |
| 0.0201 | 69.0 | 345 | 1.1772 | 42.5703 |
| 0.0200 | 70.0 | 350 | 1.1825 | 41.7671 |
| 0.0204 | 71.0 | 355 | 1.1822 | 41.6867 |
| 0.0195 | 72.0 | 360 | 1.1885 | 42.9719 |
| 0.0195 | 73.0 | 365 | 1.1874 | 42.8112 |
| 0.0187 | 74.0 | 370 | 1.1843 | 42.3293 |
| 0.0183 | 75.0 | 375 | 1.1847 | 43.4538 |
| 0.0182 | 76.0 | 380 | 1.1908 | 42.5703 |
| 0.0170 | 77.0 | 385 | 1.1942 | 42.3293 |
| 0.0170 | 78.0 | 390 | 1.1903 | 42.3293 |
| 0.0176 | 79.0 | 395 | 1.1954 | 42.6506 |
| 0.0174 | 80.0 | 400 | 1.1910 | 42.8112 |
| 0.0179 | 81.0 | 405 | 1.1938 | 42.7309 |
| 0.0173 | 82.0 | 410 | 1.1978 | 43.0522 |
| 0.0162 | 83.0 | 415 | 1.1926 | 44.0964 |
| 0.0165 | 84.0 | 420 | 1.1940 | 43.3735 |
| 0.0172 | 85.0 | 425 | 1.1941 | 43.3735 |
| 0.0167 | 86.0 | 430 | 1.1949 | 42.8916 |
| 0.0162 | 87.0 | 435 | 1.1968 | 43.9357 |
| 0.0164 | 88.0 | 440 | 1.1966 | 43.0522 |
| 0.0164 | 89.0 | 445 | 1.1975 | 42.8112 |
| 0.0169 | 90.0 | 450 | 1.1993 | 42.5703 |
| 0.0164 | 91.0 | 455 | 1.1969 | 42.7309 |
| 0.0154 | 92.0 | 460 | 1.1978 | 42.3293 |
| 0.0159 | 93.0 | 465 | 1.1974 | 42.3293 |
| 0.0163 | 94.0 | 470 | 1.1999 | 41.7671 |
| 0.0161 | 95.0 | 475 | 1.1958 | 42.0884 |
| 0.0153 | 96.0 | 480 | 1.1976 | 42.0884 |
| 0.0162 | 97.0 | 485 | 1.1961 | 42.6506 |
| 0.0167 | 98.0 | 490 | 1.1988 | 42.1687 |
| 0.0159 | 99.0 | 495 | 1.1985 | 42.6506 |
| 0.0164 | 100.0 | 500 | 1.1979 | 42.4096 |
Framework versions
- PEFT 0.19.1
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
Quellen
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1 QuelleVerifiziert 14. Sept.
Modellartefakte
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Modellartefakte
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safetensors · 50,0 MB · SHA-256 fc73ba7894ce…8db0 · Hugging Face
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2 Auszüge--- library_name: peft license: mit base_model: openai/whisper-large-v3-turbo tags: - base_model:adapter:openai/whisper-large-v3-turbo - lora - transformers metrics: - wer model-index: - name: whisper-large-v3-turbo-persian-audiodata2 results: [] --- <!-- 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-large-v3-turbo-persian-audiodata2 This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1979 - Wer: 42.4096 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 2.0420 | 1.0 | 5 | 1.2280 | 44.8996 | | 1.9990 | 2.0 | 10 | 1.1840 | 45.0602 | | 1.8969 | 3.0 | 15 | 1.1200 | 43.2932 | | 1.7966 | 4.0 | 20 | 1.0419 | 43.7751 | | 1.5880 | 5.0 | 25 | 0.9716 | 42.4096 | | 1.4181 | 6.0 | 30 | 0.9191 | 38.8755 | | 1.2844 | 7.0 | 35 | 0.8705 | 36.0643 | | 1.0876 | 8.0 | 40 | 0.8371 | 38.0723 | | 0.9473 | 9.0 | 45 | 0.8437 | 41.0442 | | 0.9232 | 10.0 | 50 | 0.8159 | 38.7149 | | 0.9232 | 26.0 | 52 | 0.8091 | 38.7952 | | 0.9232 | 27.0 | 54 | 0.8043 | 39.3574 | | 0.5928 | 28.0 | 56 | 0.8029 | 40.1606 | | 0.5928 | 29.0 | 58 | 0.7987 | 38.3133 | | 0.4714 | 30.0 | 60 | 0.8038 | 37.0281 | | 0.4714 | 31.0 | 62 | 0.8098 | 36.3052 | | 0.4714 | 32.0 | 64 | 0.8083 | 36.7871 | | 0.4555 | 33.0 | 66 | 0.8136 | 38.7149 | | 0.4555 | 34.0 | 68 | 0.8217 | 39.4378 | | 0.4328 | 35.0 | 70 | 0.8310 | 38.6345 | | 0.4328 | 36.0 | 72 | 0.8371 | 37.5904 | | 0.4328 | 37.0 | 74 | 0.8482...
Source context: 10 downloads · 0 likes · Library peft · Repo tivanai2020/whisper-large-v3-turbo-persian-audiodata2
