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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: openai/whisper-tiny |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-tiny-hu-V2 |
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results: [] |
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language: |
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- hu |
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--- |
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# képzési információ |
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A modell, egy újragondolt adatbázissal került kiképzésre. |
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Az adatbázisból ki lettek véve: |
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- a numerikus számok, ezért a modell az elhangzott számokat szövegesen fogja leírni |
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- speciális karakterek, ezért ezeket is fonetikusan fogja leírni |
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- mozaikszavak |
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- nagybetűk |
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Ezek miatt a változtatások miatt a WER elszállt kicsit, viszont a normalizált WER, tovább javult. A hipernormalizált WER vélhetően mégjobb lenne (ahhol a tesztataok is át lennének javítva a fentiek szerint). |
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A képzés ezesetben a transformer könyvtár mintascriptjével történt: https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition#whisper-model egyedi 2000 órás adatkészleten, ami most a CV17 train+validate spliteket is tartalmazta. |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# whisper-tiny-hu-2 |
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1076 |
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- Wer: 0.1195 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 7e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 64 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 3.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:------:|:-----:|:---------------:|:------:| |
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| 0.7141 | 0.0904 | 1000 | 0.3530 | 0.3369 | |
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| 0.5144 | 0.1807 | 2000 | 0.2570 | 0.2605 | |
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| 0.4386 | 0.2711 | 3000 | 0.2171 | 0.2269 | |
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| 0.3989 | 0.3614 | 4000 | 0.1997 | 0.2098 | |
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| 0.371 | 0.4518 | 5000 | 0.1867 | 0.1955 | |
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| 0.3478 | 0.5421 | 6000 | 0.1761 | 0.1844 | |
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| 0.3345 | 0.6325 | 7000 | 0.1674 | 0.1742 | |
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| 0.3275 | 0.7228 | 8000 | 0.1614 | 0.1723 | |
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| 0.3116 | 0.8132 | 9000 | 0.1547 | 0.1643 | |
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| 0.2982 | 0.9035 | 10000 | 0.1510 | 0.1599 | |
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| 0.2881 | 0.9939 | 11000 | 0.1456 | 0.1586 | |
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| 0.243 | 1.0842 | 12000 | 0.1433 | 0.1558 | |
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| 0.2407 | 1.1746 | 13000 | 0.1384 | 0.1493 | |
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| 0.2393 | 1.2649 | 14000 | 0.1367 | 0.1491 | |
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| 0.2384 | 1.3553 | 15000 | 0.1339 | 0.1466 | |
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| 0.2327 | 1.4456 | 16000 | 0.1305 | 0.1429 | |
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| 0.2275 | 1.5360 | 17000 | 0.1286 | 0.1422 | |
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| 0.226 | 1.6263 | 18000 | 0.1256 | 0.1395 | |
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| 0.2175 | 1.7167 | 19000 | 0.1239 | 0.1362 | |
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| 0.2164 | 1.8070 | 20000 | 0.1224 | 0.1346 | |
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| 0.2098 | 1.8974 | 21000 | 0.1201 | 0.1346 | |
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| 0.2062 | 1.9878 | 22000 | 0.1174 | 0.1338 | |
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| 0.1648 | 2.0781 | 23000 | 0.1179 | 0.1310 | |
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| 0.1675 | 2.1684 | 24000 | 0.1179 | 0.1305 | |
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| 0.1634 | 2.2588 | 25000 | 0.1165 | 0.1272 | |
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| 0.1632 | 2.3491 | 26000 | 0.1143 | 0.1243 | |
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| 0.1587 | 2.4395 | 27000 | 0.1139 | 0.1241 | |
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| 0.1581 | 2.5298 | 28000 | 0.1124 | 0.1239 | |
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| 0.1571 | 2.6202 | 29000 | 0.1114 | 0.1222 | |
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| 0.1579 | 2.7105 | 30000 | 0.1106 | 0.1219 | |
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| 0.1503 | 2.8009 | 31000 | 0.1091 | 0.1225 | |
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| 0.1549 | 2.8913 | 32000 | 0.1080 | 0.1195 | |
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| 0.152 | 2.9816 | 33000 | 0.1076 | 0.1191 | |
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### Framework versions |
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- Transformers 4.48.0.dev0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
