---
language:
- ga
- en
license: apache-2.0
base_model: openai/whisper-small
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: 27.57
    - name: Wer
      type: wer
      value: 70.64385411976588
---

<!-- 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-small](https://huggingface.co./openai/whisper-small) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1870
- Bleu: 27.57
- Chrf: 46.72
- Wer: 70.6439

## 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: 32
- eval_batch_size: 32
- seed: 42
- 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      |
|:-------------:|:-----:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.6685        | 0.07  | 100  | 5.05  | 20.18 | 2.0544          | 139.8919 |
| 2.4028        | 0.13  | 200  | 12.29 | 29.72 | 1.7367          | 95.5425  |
| 2.1231        | 0.2   | 300  | 14.33 | 30.77 | 1.6141          | 101.3958 |
| 1.9192        | 0.26  | 400  | 16.86 | 35.65 | 1.4778          | 91.0851  |
| 1.7129        | 0.33  | 500  | 16.77 | 37.53 | 1.3811          | 93.8766  |
| 1.5398        | 0.39  | 600  | 18.85 | 39.0  | 1.3427          | 90.2296  |
| 1.4257        | 0.46  | 700  | 25.73 | 43.3  | 1.2784          | 70.3287  |
| 1.3044        | 0.53  | 800  | 25.43 | 44.33 | 1.2274          | 72.3548  |
| 1.2626        | 0.59  | 900  | 25.09 | 44.62 | 1.1875          | 72.6249  |
| 1.2801        | 0.66  | 1000 | 25.68 | 45.53 | 1.1571          | 71.0491  |
| 1.2876        | 0.72  | 1100 | 20.62 | 41.49 | 1.2193          | 85.8622  |
| 1.2609        | 0.79  | 1200 | 29.47 | 45.04 | 1.2079          | 65.2859  |
| 1.187         | 0.85  | 1300 | 24.65 | 43.73 | 1.2086          | 72.9851  |
| 1.0342        | 0.92  | 1400 | 30.34 | 47.62 | 1.1766          | 64.3854  |
| 1.0519        | 0.98  | 1500 | 29.39 | 47.69 | 1.1425          | 64.9707  |
| 0.5473        | 1.05  | 1600 | 28.02 | 46.27 | 1.1842          | 67.6722  |
| 0.4886        | 1.12  | 1700 | 26.62 | 46.37 | 1.1845          | 76.4971  |
| 0.4354        | 1.18  | 1800 | 23.63 | 45.16 | 1.1621          | 86.1324  |
| 0.4709        | 1.25  | 1900 | 27.86 | 47.3  | 1.1544          | 73.7506  |
| 0.4802        | 1.31  | 2000 | 30.25 | 48.12 | 1.1571          | 64.9707  |
| 0.4565        | 1.38  | 2100 | 1.2095| 24.75 | 44.7            | 77.4426  |
| 0.4797        | 1.44  | 2200 | 1.2051| 28.46 | 46.03           | 67.1769  |
| 0.423         | 1.51  | 2300 | 1.2079| 28.34 | 47.65           | 68.6177  |
| 0.4254        | 1.58  | 2400 | 1.2251| 27.78 | 46.01           | 67.8523  |
| 0.4493        | 1.64  | 2500 | 1.1898| 26.61 | 47.8            | 71.1391  |
| 0.3614        | 1.71  | 2600 | 1.2079| 30.08 | 47.25           | 64.2954  |
| 0.4052        | 1.77  | 2700 | 1.1975| 30.88 | 47.44           | 64.2053  |
| 0.3541        | 1.84  | 2800 | 1.2006| 28.4  | 46.02           | 70.2837  |
| 0.3736        | 1.9   | 2900 | 1.1906| 30.82 | 47.52           | 64.1153  |
| 0.3326        | 1.97  | 3000 | 1.1870| 27.57 | 46.72           | 70.6439  |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2