---
language:
- ga
- en
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- ymoslem/IWSLT2023-GA-EN
- ymoslem/FLEURS-GA-EN
- ymoslem/BitesizeIrish-GA-EN
- ymoslem/SpokenWords-GA-EN-MTed
- ymoslem/Tatoeba-Speech-Irish
- ymoslem/Wikimedia-Speech-Irish
- ymoslem/EUbookshop-Speech-Irish
metrics:
- bleu
- wer
model-index:
- name: Whisper Larget V3 GA-EN Speech Translation
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - name: Bleu
      type: bleu
      value: 15.23
    - name: Wer
      type: wer
      value: 92.70598829356146
---

<!-- 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 Larget V3 GA-EN Speech Translation

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9885
- Bleu: 15.23
- Chrf: 28.15
- Wer: 92.7060

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Bleu  | Chrf  | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.5918        | 0.0138 | 100  | 0.61  | 8.48  | 2.1791          | 238.2260 |
| 2.476         | 0.0276 | 200  | 0.63  | 10.43 | 2.1702          | 275.7317 |
| 2.2358        | 0.0414 | 300  | 4.76  | 19.98 | 2.0420          | 120.0810 |
| 2.1778        | 0.0552 | 400  | 2.78  | 12.85 | 1.9506          | 86.8528  |
| 1.9779        | 0.0690 | 500  | 4.53  | 18.47 | 1.8609          | 137.1905 |
| 1.9435        | 0.0828 | 600  | 6.67  | 22.37 | 1.7726          | 82.4403  |
| 1.7928        | 0.0966 | 700  | 4.54  | 17.32 | 1.7445          | 133.8586 |
| 1.9004        | 0.1103 | 800  | 1.58  | 12.65 | 1.7290          | 195.2724 |
| 1.7856        | 0.1241 | 900  | 4.84  | 17.5  | 1.6990          | 83.9262  |
| 1.6783        | 0.1379 | 1000 | 8.46  | 24.24 | 1.6329          | 113.5074 |
| 1.6095        | 0.1517 | 1100 | 7.35  | 20.22 | 1.6083          | 102.5214 |
| 1.6328        | 0.1655 | 1200 | 11.46 | 25.29 | 1.5267          | 76.5871  |
| 1.6093        | 0.1793 | 1300 | 6.51  | 17.77 | 1.4947          | 112.4719 |
| 1.5776        | 0.1931 | 1400 | 6.21  | 19.86 | 1.4952          | 90.6348  |
| 1.4767        | 0.2069 | 1500 | 4.86  | 19.57 | 1.4515          | 145.1148 |
| 1.3447        | 0.2207 | 1600 | 6.77  | 19.96 | 1.3974          | 90.5448  |
| 1.3273        | 0.2345 | 1700 | 4.77  | 16.31 | 1.4323          | 152.1837 |
| 1.4253        | 0.2483 | 1800 | 3.95  | 15.66 | 1.3598          | 173.2553 |
| 1.3505        | 0.2621 | 1900 | 11.25 | 23.4  | 1.3517          | 80.3692  |
| 1.2593        | 0.2759 | 2000 | 12.71 | 26.55 | 1.3236          | 77.5777  |
| 1.2483        | 0.2897 | 2100 | 17.88 | 32.0  | 1.2825          | 73.3003  |
| 1.161         | 0.3034 | 2200 | 10.08 | 20.69 | 1.2567          | 115.8937 |
| 1.1597        | 0.3172 | 2300 | 8.61  | 19.54 | 1.2581          | 93.8766  |
| 1.0937        | 0.3310 | 2400 | 12.37 | 25.67 | 1.2577          | 99.0095  |
| 1.0606        | 0.3448 | 2500 | 6.46  | 23.47 | 1.2228          | 172.9401 |
| 1.039         | 0.3586 | 2600 | 9.55  | 21.56 | 1.2186          | 89.7794  |
| 1.0193        | 0.3724 | 2700 | 3.08  | 17.58 | 1.1844          | 281.8100 |
| 1.1153        | 0.3862 | 2800 | 1.1693| 2.69  | 18.38           | 350.2927 |
| 1.012         | 0.4    | 2900 | 1.1233| 3.56  | 14.74           | 194.9122 |
| 0.8936        | 0.4138 | 3000 | 1.1161| 5.21  | 17.38           | 158.3521 |
| 0.8893        | 0.4276 | 3100 | 1.1119| 11.52 | 25.02           | 80.9095  |
| 0.9491        | 0.4414 | 3200 | 1.1213| 5.93  | 20.91           | 174.0207 |
| 0.9233        | 0.4552 | 3300 | 1.0656| 5.54  | 20.95           | 186.2224 |
| 0.8915        | 0.4690 | 3400 | 1.0736| 7.26  | 23.99           | 155.6506 |
| 0.8296        | 0.4828 | 3500 | 1.0461| 6.74  | 21.46           | 146.1054 |
| 0.8163        | 0.4966 | 3600 | 1.0706| 11.35 | 24.11           | 101.8010 |
| 0.8115        | 0.5103 | 3700 | 1.0199| 12.84 | 26.92           | 115.8487 |
| 0.8245        | 0.5241 | 3800 | 1.0163| 12.47 | 24.29           | 101.9361 |
| 0.7988        | 0.5379 | 3900 | 0.9891| 15.29 | 28.54           | 92.7960  |
| 0.769         | 0.5517 | 4000 | 0.9885| 15.23 | 28.15           | 92.7060  |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2+git70dfd51
- Datasets 2.20.0
- Tokenizers 0.19.1