---
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
metrics:
- bleu
- wer
model-index:
- name: Whisper Small GA-EN Speech Translation + VAD + warmup_ratio=0.01
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: IWSLT-2023, FLEURS, BiteSize, and SpokenWords
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - name: Bleu
      type: bleu
      value: 29.94
    - name: Wer
      type: wer
      value: 64.34038721296713
---

<!-- 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 + VAD + warmup_ratio=0.01

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IWSLT-2023, FLEURS, BiteSize, and SpokenWords dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7482
- Bleu: 29.94
- Chrf: 45.74
- Wer: 64.3404

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Bleu  | Chrf  | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.0518        | 0.2188 | 100  | 8.56  | 25.29 | 1.8072          | 123.9982 |
| 1.5449        | 0.4376 | 200  | 18.41 | 34.82 | 1.5746          | 83.7461  |
| 1.2518        | 0.6565 | 300  | 21.1  | 36.24 | 1.5009          | 83.9712  |
| 1.0947        | 0.8753 | 400  | 21.5  | 41.43 | 1.4582          | 89.8694  |
| 0.4439        | 1.0941 | 500  | 25.21 | 41.77 | 1.4979          | 72.5799  |
| 0.4416        | 1.3129 | 600  | 22.2  | 40.47 | 1.5107          | 79.8739  |
| 0.4417        | 1.5317 | 700  | 20.2  | 40.75 | 1.5215          | 88.8789  |
| 0.4108        | 1.7505 | 800  | 25.73 | 41.28 | 1.5278          | 67.8073  |
| 0.355         | 1.9694 | 900  | 20.6  | 39.37 | 1.5436          | 87.3030  |
| 0.1303        | 2.1882 | 1000 | 28.79 | 42.68 | 1.5936          | 68.1675  |
| 0.1421        | 2.4070 | 1100 | 27.84 | 42.58 | 1.5745          | 67.5371  |
| 0.1341        | 2.6258 | 1200 | 30.52 | 45.15 | 1.5953          | 66.5916  |
| 0.1365        | 2.8446 | 1300 | 26.93 | 43.72 | 1.6046          | 74.2909  |
| 0.0528        | 3.0635 | 1400 | 29.03 | 44.12 | 1.6303          | 64.8807  |
| 0.0519        | 3.2823 | 1500 | 27.75 | 44.34 | 1.6774          | 68.6177  |
| 0.0554        | 3.5011 | 1600 | 27.64 | 45.15 | 1.6637          | 71.1842  |
| 0.0514        | 3.7199 | 1700 | 30.26 | 44.62 | 1.6497          | 65.4660  |
| 0.0503        | 3.9387 | 1800 | 26.88 | 43.0  | 1.6780          | 70.4187  |
| 0.0259        | 4.1575 | 1900 | 29.6  | 44.51 | 1.6915          | 64.9707  |
| 0.0263        | 4.3764 | 2000 | 25.33 | 42.51 | 1.7080          | 72.3998  |
| 0.0254        | 4.5952 | 2100 | 30.59 | 45.35 | 1.6884          | 64.2954  |
| 0.0211        | 4.8140 | 2200 | 31.09 | 46.56 | 1.6984          | 64.0252  |
| 0.0137        | 5.0328 | 2300 | 28.96 | 43.67 | 1.7253          | 66.3665  |
| 0.0075        | 5.2516 | 2400 | 29.77 | 44.63 | 1.7112          | 66.9968  |
| 0.0056        | 5.4705 | 2500 | 29.96 | 45.51 | 1.7197          | 64.5655  |
| 0.0067        | 5.6893 | 2600 | 29.86 | 45.25 | 1.7464          | 66.0964  |
| 0.0064        | 5.9081 | 2700 | 29.47 | 45.36 | 1.7440          | 65.2859  |
| 0.0023        | 6.1269 | 2800 | 30.03 | 46.49 | 1.7419          | 64.4755  |
| 0.0016        | 6.3457 | 2900 | 29.76 | 45.64 | 1.7474          | 65.0158  |
| 0.0019        | 6.5646 | 3000 | 1.7482| 29.94 | 45.74           | 64.3404  |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1