Model save

README.md

@@ -0,0 +1,179 @@
+---
+library_name: transformers
+license: apache-2.0
+base_model: openai/whisper-tiny
+tags:
+- generated_from_trainer
+datasets:
+- jv_id_asr_split
+metrics:
+- wer
+model-index:
+- name: whisper-tiny-javanese-openslr-v4
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: jv_id_asr_split
+      type: jv_id_asr_split
+      config: jv_id_asr_source
+      split: None
+      args: jv_id_asr_source
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.15829025876023428
+---
+
+<!-- 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-tiny-javanese-openslr-v4
+
+This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the jv_id_asr_split dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1869
+- Wer: 0.1583
+
+## 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: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- 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: linear
+- lr_scheduler_warmup_ratio: 0.1
+- training_steps: 50000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
+|:-------------:|:------:|:-----:|:---------------:|:------:|
+| 1.1788        | 0.0540 | 500   | 0.9671          | 0.6590 |
+| 0.8015        | 0.1081 | 1000  | 0.6977          | 0.5305 |
+| 0.6498        | 0.1621 | 1500  | 0.5725          | 0.6670 |
+| 0.5828        | 0.2161 | 2000  | 0.5094          | 0.4829 |
+| 0.5226        | 0.2702 | 2500  | 0.4642          | 0.3860 |
+| 0.4955        | 0.3242 | 3000  | 0.4341          | 0.3915 |
+| 0.4616        | 0.3782 | 3500  | 0.4128          | 0.3540 |
+| 0.4474        | 0.4323 | 4000  | 0.3900          | 0.3614 |
+| 0.4387        | 0.4863 | 4500  | 0.3736          | 0.3563 |
+| 0.4154        | 0.5403 | 5000  | 0.3606          | 0.3274 |
+| 0.419         | 0.5944 | 5500  | 0.3495          | 0.3144 |
+| 0.3799        | 0.6484 | 6000  | 0.3398          | 0.2922 |
+| 0.3802        | 0.7024 | 6500  | 0.3290          | 0.3044 |
+| 0.3611        | 0.7565 | 7000  | 0.3225          | 0.2823 |
+| 0.3548        | 0.8105 | 7500  | 0.3168          | 0.2733 |
+| 0.346         | 0.8645 | 8000  | 0.3105          | 0.2660 |
+| 0.3547        | 0.9186 | 8500  | 0.3063          | 0.2708 |
+| 0.3211        | 0.9726 | 9000  | 0.3019          | 0.2827 |
+| 0.2718        | 1.0267 | 9500  | 0.2990          | 0.2660 |
+| 0.2859        | 1.0807 | 10000 | 0.2980          | 0.2587 |
+| 0.2917        | 1.1348 | 10500 | 0.3269          | 0.2519 |
+| 0.3117        | 1.1888 | 11000 | 0.3214          | 0.2575 |
+| 0.3204        | 1.2428 | 11500 | 0.3168          | 0.2646 |
+| 0.2962        | 1.2969 | 12000 | 0.3087          | 0.2410 |
+| 0.2961        | 1.3509 | 12500 | 0.3057          | 0.2385 |
+| 0.2887        | 1.4049 | 13000 | 0.2987          | 0.2281 |
+| 0.2981        | 1.4590 | 13500 | 0.2953          | 0.2322 |
+| 0.2994        | 1.5130 | 14000 | 0.2909          | 0.2322 |
+| 0.2818        | 1.5670 | 14500 | 0.2848          | 0.2200 |
+| 0.2851        | 1.6211 | 15000 | 0.2830          | 0.2166 |
+| 0.275         | 1.6751 | 15500 | 0.2770          | 0.2129 |
+| 0.2689        | 1.7291 | 16000 | 0.2760          | 0.2119 |
+| 0.2796        | 1.7832 | 16500 | 0.2678          | 0.2002 |
+| 0.2717        | 1.8372 | 17000 | 0.2653          | 0.2001 |
+| 0.2661        | 1.8912 | 17500 | 0.2626          | 0.2014 |
+| 0.2612        | 1.9453 | 18000 | 0.2573          | 0.1953 |
+| 0.2532        | 1.9993 | 18500 | 0.2554          | 0.1954 |
+| 0.1993        | 2.0534 | 19000 | 0.2527          | 0.1949 |
+| 0.2009        | 2.1074 | 19500 | 0.2505          | 0.1897 |
+| 0.1929        | 2.1615 | 20000 | 0.2484          | 0.1927 |
+| 0.2011        | 2.2155 | 20500 | 0.2454          | 0.1895 |
+| 0.1828        | 2.2695 | 21000 | 0.2444          | 0.1892 |
+| 0.1823        | 2.3236 | 21500 | 0.2437          | 0.1845 |
+| 0.186         | 2.3776 | 22000 | 0.2407          | 0.1839 |
+| 0.1898        | 2.4316 | 22500 | 0.2390          | 0.1828 |
+| 0.1789        | 2.4857 | 23000 | 0.2363          | 0.1790 |
+| 0.1765        | 2.5397 | 23500 | 0.2353          | 0.1797 |
+| 0.1808        | 2.5937 | 24000 | 0.2320          | 0.1797 |
+| 0.1771        | 2.6478 | 24500 | 0.2291          | 0.1777 |
+| 0.183         | 2.7018 | 25000 | 0.2276          | 0.1788 |
+| 0.178         | 2.7558 | 25500 | 0.2250          | 0.1754 |
+| 0.1829        | 2.8099 | 26000 | 0.2231          | 0.1755 |
+| 0.183         | 2.8639 | 26500 | 0.2216          | 0.1790 |
+| 0.1812        | 2.9179 | 27000 | 0.2198          | 0.1729 |
+| 0.1697        | 2.9720 | 27500 | 0.2186          | 0.1727 |
+| 0.1317        | 3.0260 | 28000 | 0.2173          | 0.1728 |
+| 0.1298        | 3.0801 | 28500 | 0.2159          | 0.1690 |
+| 0.1272        | 3.1341 | 29000 | 0.2161          | 0.1686 |
+| 0.1389        | 3.1881 | 29500 | 0.2148          | 0.1706 |
+| 0.1379        | 3.2422 | 30000 | 0.2139          | 0.1693 |
+| 0.1312        | 3.2962 | 30500 | 0.2133          | 0.1714 |
+| 0.1212        | 3.3502 | 31000 | 0.2116          | 0.1706 |
+| 0.1265        | 3.4043 | 31500 | 0.2103          | 0.1666 |
+| 0.1261        | 3.4583 | 32000 | 0.2095          | 0.1706 |
+| 0.127         | 3.5123 | 32500 | 0.2079          | 0.1673 |
+| 0.1346        | 3.5664 | 33000 | 0.2061          | 0.1683 |
+| 0.1283        | 3.6204 | 33500 | 0.2046          | 0.1652 |
+| 0.1244        | 3.6744 | 34000 | 0.2040          | 0.1684 |
+| 0.1207        | 3.7285 | 34500 | 0.2026          | 0.1648 |
+| 0.1239        | 3.7825 | 35000 | 0.2022          | 0.1622 |
+| 0.1308        | 3.8365 | 35500 | 0.1998          | 0.1624 |
+| 0.1272        | 3.8906 | 36000 | 0.1997          | 0.1649 |
+| 0.1328        | 3.9446 | 36500 | 0.1988          | 0.1647 |
+| 0.1256        | 3.9986 | 37000 | 0.1971          | 0.1653 |
+| 0.0953        | 4.0527 | 37500 | 0.1974          | 0.1604 |
+| 0.0946        | 4.1068 | 38000 | 0.1979          | 0.1625 |
+| 0.0933        | 4.1608 | 38500 | 0.1964          | 0.1609 |
+| 0.1025        | 4.2148 | 39000 | 0.1962          | 0.1634 |
+| 0.1002        | 4.2689 | 39500 | 0.1957          | 0.1632 |
+| 0.0976        | 4.3229 | 40000 | 0.1949          | 0.1621 |
+| 0.0983        | 4.3769 | 40500 | 0.1936          | 0.1605 |
+| 0.0995        | 4.4310 | 41000 | 0.1935          | 0.1608 |
+| 0.0877        | 4.4850 | 41500 | 0.1930          | 0.1608 |
+| 0.0985        | 4.5390 | 42000 | 0.1928          | 0.1633 |
+| 0.0887        | 4.5931 | 42500 | 0.1917          | 0.1616 |
+| 0.0909        | 4.6471 | 43000 | 0.1918          | 0.1604 |
+| 0.0908        | 4.7011 | 43500 | 0.1910          | 0.1593 |
+| 0.0931        | 4.7552 | 44000 | 0.1902          | 0.1579 |
+| 0.0938        | 4.8092 | 44500 | 0.1890          | 0.1582 |
+| 0.0925        | 4.8632 | 45000 | 0.1889          | 0.1594 |
+| 0.0943        | 4.9173 | 45500 | 0.1882          | 0.1578 |
+| 0.0918        | 4.9713 | 46000 | 0.1879          | 0.1584 |
+| 0.0791        | 5.0253 | 46500 | 0.1877          | 0.1560 |
+| 0.077         | 5.0793 | 47000 | 0.1877          | 0.1587 |
+| 0.0769        | 5.1334 | 47500 | 0.1878          | 0.1597 |
+| 0.0744        | 5.1874 | 48000 | 0.1876          | 0.1585 |
+| 0.0775        | 5.2414 | 48500 | 0.1874          | 0.1595 |
+| 0.069         | 5.2955 | 49000 | 0.1873          | 0.1579 |
+| 0.0761        | 5.3495 | 49500 | 0.1870          | 0.1575 |
+| 0.0711        | 5.4036 | 50000 | 0.1869          | 0.1583 |
+
+
+### Framework versions
+
+- Transformers 4.50.0.dev0
+- Pytorch 2.6.0+cu126
+- Datasets 2.16.0
+- Tokenizers 0.21.1

generation_config.json

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+  "is_multilingual": true,
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+  "return_timestamps": false,
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+}

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