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

# 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. -->

# whisper-tiny-hu-2

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/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