---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-base-hu-V2
  results: []
---
# 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.


<!-- 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. -->
<!-- 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-base-hu-V2

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co./openai/whisper-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0880
- Wer: 0.0960

## 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    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 0.551         | 0.0904 | 1000  | 0.2710          | 0.2694 |
| 0.4016        | 0.1807 | 2000  | 0.2009          | 0.2061 |
| 0.3449        | 0.2711 | 3000  | 0.1707          | 0.1770 |
| 0.3147        | 0.3614 | 4000  | 0.1588          | 0.1650 |
| 0.2936        | 0.4518 | 5000  | 0.1472          | 0.1551 |
| 0.2758        | 0.5421 | 6000  | 0.1406          | 0.1479 |
| 0.2663        | 0.6325 | 7000  | 0.1322          | 0.1393 |
| 0.2613        | 0.7228 | 8000  | 0.1283          | 0.1402 |
| 0.2491        | 0.8132 | 9000  | 0.1216          | 0.1319 |
| 0.238         | 0.9035 | 10000 | 0.1192          | 0.1291 |
| 0.2287        | 0.9939 | 11000 | 0.1151          | 0.1276 |
| 0.1798        | 1.0842 | 12000 | 0.1131          | 0.1234 |
| 0.1791        | 1.1746 | 13000 | 0.1113          | 0.1186 |
| 0.1787        | 1.2649 | 14000 | 0.1085          | 0.1186 |
| 0.1771        | 1.3553 | 15000 | 0.1068          | 0.1154 |
| 0.1728        | 1.4456 | 16000 | 0.1046          | 0.1135 |
| 0.1714        | 1.5360 | 17000 | 0.1029          | 0.1152 |
| 0.1706        | 1.6263 | 18000 | 0.1007          | 0.1117 |
| 0.163         | 1.7167 | 19000 | 0.0998          | 0.1074 |
| 0.1613        | 1.8070 | 20000 | 0.0982          | 0.1075 |
| 0.1568        | 1.8974 | 21000 | 0.0967          | 0.1087 |
| 0.1525        | 1.9878 | 22000 | 0.0945          | 0.1045 |
| 0.1063        | 2.0781 | 23000 | 0.0967          | 0.1046 |
| 0.1075        | 2.1684 | 24000 | 0.0951          | 0.1030 |
| 0.1035        | 2.2588 | 25000 | 0.0936          | 0.1015 |
| 0.1056        | 2.3491 | 26000 | 0.0928          | 0.1013 |
| 0.1019        | 2.4395 | 27000 | 0.0921          | 0.1000 |
| 0.1004        | 2.5298 | 28000 | 0.0911          | 0.0986 |
| 0.0992        | 2.6202 | 29000 | 0.0904          | 0.0980 |
| 0.1011        | 2.7105 | 30000 | 0.0898          | 0.0978 |
| 0.095         | 2.8009 | 31000 | 0.0892          | 0.0975 |
| 0.0975        | 2.8913 | 32000 | 0.0885          | 0.0960 |
| 0.0963        | 2.9816 | 33000 | 0.0880          | 0.0962 |


### Framework versions

- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0