---
language:
- ga
- en
license: apache-2.0
base_model: openai/whisper-medium
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
metrics:
- bleu
- wer
model-index:
- name: Whisper Small 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: 28.6
    - name: Wer
      type: wer
      value: 68.52769022962629
---

<!-- 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 Small GA-EN Speech Translation

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1842
- Bleu: 28.6
- Chrf: 49.54
- Wer: 68.5277

## 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      |
|:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.5585        | 0.0109 | 100  | 2.94  | 16.53 | 2.1737          | 176.2269 |
| 2.5748        | 0.0219 | 200  | 6.93  | 24.84 | 2.0289          | 101.8460 |
| 2.554         | 0.0328 | 300  | 7.16  | 25.31 | 1.8861          | 142.4584 |
| 2.276         | 0.0438 | 400  | 8.9   | 27.72 | 1.8568          | 119.7208 |
| 2.3077        | 0.0547 | 500  | 14.51 | 32.58 | 1.7492          | 88.3836  |
| 2.0852        | 0.0657 | 600  | 16.71 | 34.1  | 1.6548          | 83.6560  |
| 2.1602        | 0.0766 | 700  | 14.93 | 35.2  | 1.6063          | 106.4385 |
| 1.9556        | 0.0876 | 800  | 20.64 | 36.74 | 1.6190          | 77.5777  |
| 1.7516        | 0.0985 | 900  | 15.44 | 36.67 | 1.5614          | 95.0023  |
| 1.7502        | 0.1095 | 1000 | 20.65 | 38.42 | 1.5317          | 81.4948  |
| 1.6851        | 0.1204 | 1100 | 19.13 | 37.91 | 1.5289          | 87.7533  |
| 1.5154        | 0.1314 | 1200 | 19.79 | 41.21 | 1.4906          | 83.3408  |
| 1.3658        | 0.1423 | 1300 | 19.58 | 39.16 | 1.4623          | 96.3980  |
| 1.3828        | 0.1533 | 1400 | 22.84 | 42.83 | 1.4069          | 77.5777  |
| 1.5339        | 0.1642 | 1500 | 20.91 | 41.62 | 1.3909          | 86.5376  |
| 1.2441        | 0.1752 | 1600 | 23.35 | 43.43 | 1.3726          | 75.5966  |
| 1.1607        | 0.1861 | 1700 | 20.4  | 42.41 | 1.3471          | 85.4120  |
| 1.1043        | 0.1970 | 1800 | 21.13 | 43.4  | 1.3332          | 81.5849  |
| 1.0698        | 0.2080 | 1900 | 23.84 | 44.54 | 1.3413          | 73.3904  |
| 1.0698        | 0.2189 | 2000 | 28.34 | 47.2  | 1.2848          | 66.9068  |
| 1.053         | 0.2299 | 2100 | 25.19 | 46.75 | 1.2951          | 73.1652  |
| 0.9139        | 0.2408 | 2200 | 28.43 | 47.11 | 1.2852          | 70.7789  |
| 0.742         | 0.2518 | 2300 | 30.5  | 47.62 | 1.2580          | 63.6200  |
| 0.8627        | 0.2627 | 2400 | 29.97 | 48.38 | 1.2308          | 66.2314  |
| 0.7213        | 0.2737 | 2500 | 22.96 | 46.55 | 1.2176          | 83.7010  |
| 0.672         | 0.2846 | 2600 | 27.35 | 48.02 | 1.2272          | 71.7695  |
| 0.784         | 0.2956 | 2700 | 31.16 | 50.83 | 1.2010          | 65.3760  |
| 0.6463        | 0.3065 | 2800 | 30.67 | 51.24 | 1.1884          | 64.9257  |
| 0.6028        | 0.3175 | 2900 | 32.07 | 51.3  | 1.1866          | 61.4588  |
| 0.6494        | 0.3284 | 3000 | 32.04 | 50.96 | 1.1768          | 63.3048  |
| 0.657         | 0.3394 | 3100 | 1.2126| 30.55 | 50.18           | 66.0964  |
| 0.6239        | 0.3503 | 3200 | 1.1836| 33.69 | 52.06           | 60.2431  |
| 0.63          | 0.3612 | 3300 | 1.2201| 32.14 | 51.62           | 61.7290  |
| 0.5155        | 0.3722 | 3400 | 1.1956| 32.62 | 51.99           | 61.3688  |
| 0.5392        | 0.3831 | 3500 | 1.2010| 31.13 | 51.37           | 63.9802  |
| 0.5159        | 0.3941 | 3600 | 1.1831| 32.2  | 51.81           | 62.4043  |
| 0.4535        | 0.4050 | 3700 | 1.1744| 31.61 | 51.77           | 63.3949  |
| 0.3346        | 0.4160 | 3800 | 1.2066| 30.67 | 50.21           | 65.4660  |
| 0.3991        | 0.4269 | 3900 | 1.1870| 30.7  | 50.88           | 65.2409  |
| 0.395         | 0.4379 | 4000 | 1.1842| 28.6  | 49.54           | 68.5277  |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1