---
language:
- ga
- en
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- ymoslem/IWSLT2023-GA-EN
- ymoslem/FLEURS-GA-EN
- ymoslem/BitesizeIrish-GA-EN
- ymoslem/SpokenWords-GA-EN-MTed
metrics:
- bleu
- wer
model-index:
- name: Whisper Large GA-EN Speech Translation
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - name: Bleu
      type: bleu
      value: 30.16
    - name: Wer
      type: wer
      value: 65.60108059432687
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Large GA-EN Speech Translation

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1716
- Bleu: 31.61
- Chrf: 50.4
- Wer: 63.8001
			
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.03
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Bleu  | Chrf  | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 3.1547        | 0.03   | 100  | 3.75  | 18.71 | 2.4173          | 124.0882 |
| 2.6996        | 0.07   | 200  | 8.16  | 25.45 | 2.1329          | 114.1378 |
| 2.4841        | 0.1    | 300  | 6.4   | 23.6  | 2.0262          | 158.1720 |
| 2.4706        | 0.13   | 400  | 9.16  | 27.67 | 1.9688          | 120.0810 |
| 2.3575        | 0.16   | 500  | 13.66 | 31.5  | 1.8284          | 100.8555 |
| 2.1916        | 0.2    | 600  | 12.97 | 31.8  | 1.7486          | 110.1756 |
| 2.1353        | 0.23   | 700  | 16.7  | 33.52 | 1.7568          | 86.8528  |
| 1.9885        | 0.26   | 800  | 19.34 | 35.35 | 1.6395          | 78.7033  |
| 1.9126        | 0.3    | 900  | 20.21 | 36.28 | 1.5658          | 78.2080  |
| 1.6418        | 0.33   | 1000 | 18.61 | 38.49 | 1.4998          | 86.8528  |
| 1.5782        | 0.36   | 1100 | 22.91 | 40.04 | 1.4716          | 71.0941  |
| 1.4899        | 0.39   | 1200 | 21.55 | 40.92 | 1.4444          | 78.7933  |
| 1.3155        | 0.43   | 1300 | 24.95 | 42.05 | 1.3934          | 70.9140  |
| 1.4144        | 0.46   | 1400 | 28.38 | 46.18 | 1.2791          | 65.8262  |
| 1.1949        | 0.49   | 1500 | 26.95 | 45.84 | 1.2879          | 70.6889  |
| 1.0179        | 0.53   | 1600 | 26.12 | 46.4  | 1.2624          | 69.6983  |
| 1.0935        | 0.56   | 1700 | 28.51 | 48.24 | 1.2076          | 67.4021  |
| 1.061         | 0.59   | 1800 | 27.42 | 48.83 | 1.1812          | 71.4543  |
| 1.0955        | 0.62   | 1900 | 31.32 | 49.91 | 1.1503          | 62.9896  |
| 1.0607        | 0.66   | 2000 | 31.26 | 50.41 | 1.1318          | 62.3143  |
| 1.1135        | 0.6897 | 2100 | 1.2135| 26.57 | 46.18           | 69.7884  |
| 0.9819        | 0.7225 | 2200 | 1.2252| 26.95 | 49.47           | 65.0158  |
| 0.9909        | 0.7553 | 2300 | 1.2072| 30.35 | 46.49           | 63.3048  |
| 0.9521        | 0.7882 | 2400 | 1.2130| 24.76 | 46.44           | 70.6889  |
| 0.8245        | 0.8210 | 2500 | 1.1724| 24.84 | 47.05           | 78.1630  |
| 0.8303        | 0.8539 | 2600 | 1.1812| 27.56 | 47.48           | 70.1036  |
| 0.6934        | 0.8867 | 2700 | 1.1716| 31.61 | 50.4            | 63.8001  |
| 0.7117        | 0.9195 | 2800 | 1.1650| 30.82 | 49.95           | 65.0158  |
| 0.6944        | 0.9524 | 2900 | 1.1516| 31.21 | 49.8            | 63.5750  |
| 0.7132        | 0.9852 | 3000 | 1.1390| 30.16 | 49.77           | 65.6011  |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.0.1+cu118
- Datasets 2.18.0
- Tokenizers 0.19.1