képzési információ
A modell, egy újragondolt adatbázissal került kiképzésre.
Az adatbázisból ki lettek véve:
- a numerikus számok, ezért a modell az elhangzott számokat szövegesen fogja leírni
- speciális karakterek, ezért ezeket is fonetikusan fogja leírni
- mozaikszavak
- nagybetűk
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).
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.
whisper-tiny-hu-2
This model is a fine-tuned version of openai/whisper-tiny on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1076
- Wer: 0.1195
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: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- 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_steps: 1000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.7141 | 0.0904 | 1000 | 0.3530 | 0.3369 |
| 0.5144 | 0.1807 | 2000 | 0.2570 | 0.2605 |
| 0.4386 | 0.2711 | 3000 | 0.2171 | 0.2269 |
| 0.3989 | 0.3614 | 4000 | 0.1997 | 0.2098 |
| 0.371 | 0.4518 | 5000 | 0.1867 | 0.1955 |
| 0.3478 | 0.5421 | 6000 | 0.1761 | 0.1844 |
| 0.3345 | 0.6325 | 7000 | 0.1674 | 0.1742 |
| 0.3275 | 0.7228 | 8000 | 0.1614 | 0.1723 |
| 0.3116 | 0.8132 | 9000 | 0.1547 | 0.1643 |
| 0.2982 | 0.9035 | 10000 | 0.1510 | 0.1599 |
| 0.2881 | 0.9939 | 11000 | 0.1456 | 0.1586 |
| 0.243 | 1.0842 | 12000 | 0.1433 | 0.1558 |
| 0.2407 | 1.1746 | 13000 | 0.1384 | 0.1493 |
| 0.2393 | 1.2649 | 14000 | 0.1367 | 0.1491 |
| 0.2384 | 1.3553 | 15000 | 0.1339 | 0.1466 |
| 0.2327 | 1.4456 | 16000 | 0.1305 | 0.1429 |
| 0.2275 | 1.5360 | 17000 | 0.1286 | 0.1422 |
| 0.226 | 1.6263 | 18000 | 0.1256 | 0.1395 |
| 0.2175 | 1.7167 | 19000 | 0.1239 | 0.1362 |
| 0.2164 | 1.8070 | 20000 | 0.1224 | 0.1346 |
| 0.2098 | 1.8974 | 21000 | 0.1201 | 0.1346 |
| 0.2062 | 1.9878 | 22000 | 0.1174 | 0.1338 |
| 0.1648 | 2.0781 | 23000 | 0.1179 | 0.1310 |
| 0.1675 | 2.1684 | 24000 | 0.1179 | 0.1305 |
| 0.1634 | 2.2588 | 25000 | 0.1165 | 0.1272 |
| 0.1632 | 2.3491 | 26000 | 0.1143 | 0.1243 |
| 0.1587 | 2.4395 | 27000 | 0.1139 | 0.1241 |
| 0.1581 | 2.5298 | 28000 | 0.1124 | 0.1239 |
| 0.1571 | 2.6202 | 29000 | 0.1114 | 0.1222 |
| 0.1579 | 2.7105 | 30000 | 0.1106 | 0.1219 |
| 0.1503 | 2.8009 | 31000 | 0.1091 | 0.1225 |
| 0.1549 | 2.8913 | 32000 | 0.1080 | 0.1195 |
| 0.152 | 2.9816 | 33000 | 0.1076 | 0.1191 |
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0