---
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
- chrf
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
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - name: Bleu
      type: bleu
      value: 26.85
    - name: Wer
      type: wer
      value: 73.52543899144528
library_name: transformers
---

<!-- 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, and SpokenWords datasets.
The best model checkpoint (this version) is at step 3400, epoch 3.67, and it achieves the following results on the evaluation set:
- Loss: 1.5752
- Bleu: 30.77
- Chrf: 46.43
- Wer: 64.6556

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Experiment

- language=English
- +more steps

### 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Bleu  | Chrf  | Validation Loss | Wer      |
|:-------------:|:-----:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.4954        | 0.11  | 100  | 3.7   | 18.03 | 2.1286          | 179.7839 |
| 2.045         | 0.22  | 200  | 12.65 | 25.53 | 1.8146          | 100.9005 |
| 1.7928        | 0.32  | 300  | 13.78 | 30.2  | 1.7253          | 101.9811 |
| 1.6615        | 0.43  | 400  | 15.8  | 31.88 | 1.6834          | 92.5259  |
| 1.4491        | 0.54  | 500  | 15.61 | 36.27 | 1.5971          | 107.3841 |
| 1.2074        | 0.65  | 600  | 19.92 | 36.31 | 1.5939          | 84.3314  |
| 1.2308        | 0.76  | 700  | 20.37 | 38.72 | 1.5234          | 84.8267  |
| 1.107         | 0.86  | 800  | 21.35 | 37.87 | 1.5460          | 82.8906  |
| 0.9491        | 0.97  | 900  | 21.06 | 40.74 | 1.5161          | 82.5754  |
| 0.384         | 1.08  | 1000 | 23.24 | 41.98 | 1.4927          | 82.2152  |
| 0.362         | 1.19  | 1100 | 23.19 | 42.24 | 1.5567          | 80.2792  |
| 0.3756        | 1.29  | 1200 | 27.83 | 43.8  | 1.5265          | 69.2481  |
| 0.3401        | 1.4   | 1300 | 21.79 | 41.66 | 1.5522          | 92.3908  |
| 0.3346        | 1.51  | 1400 | 24.61 | 42.15 | 1.5085          | 75.4615  |
| 0.3101        | 1.62  | 1500 | 26.67 | 43.41 | 1.4933          | 70.7789  |
| 0.3231        | 1.73  | 1600 | 27.95 | 42.82 | 1.4979          | 68.3026  |
| 0.2665        | 1.83  | 1700 | 28.5  | 43.76 | 1.4977          | 68.1225  |
| 0.2704        | 1.94  | 1800 | 28.15 | 43.87 | 1.5063          | 68.8429  |
| 0.0769        | 2.05  | 1900 | 25.76 | 43.22 | 1.5162          | 77.6227  |
| 0.0597        | 2.16  | 2000 | 25.04 | 43.15 | 1.5216          | 79.0635  |
| 0.0743        | 2.27  | 2100 | 27.85 | 44.43 | 1.5313          | 68.3926  |
| 0.0878        | 2.37  | 2200 | 27.54 | 43.96 | 1.5495          | 68.3476  |
| 0.0712        | 2.48  | 2300 | 28.28 | 44.39 | 1.5355          | 65.8712  |
| 0.0789        | 2.59  | 2400 | 28.64 | 44.75 | 1.5277          | 65.7812  |
| 0.073         | 2.7   | 2500 | 29.09 | 44.65 | 1.5327          | 65.7812  |
| 0.073         | 2.8   | 2600 | 25.26 | 43.44 | 1.5304          | 78.2981  |
| 0.0697        | 2.91  | 2700 | 25.71 | 43.02 | 1.5460          | 78.4782  |
| 0.0398        | 3.02  | 2800 | 28.26 | 44.71 | 1.5580          | 72.8501  |
| 0.0302        | 3.13  | 2900 | 30.25 | 45.46 | 1.5688          | 66.1414  |
| 0.0424        | 3.24  | 3000 | 29.88 | 45.21 | 1.5693          | 66.0964  |
| 0.0397        | 3.34  | 3100 | 30.01 | 45.85 | 1.5934          | 65.6911  |
| 0.0346        | 3.45  | 3200 | 30.2  | 45.8  | 1.5818          | 65.8262  |
| 0.032         | 3.56  | 3300 | 29.81 | 46.5  | 1.5823          | 66.7267  |
| 0.0348        | 3.67  | 3400 | 30.77 | 46.43 | 1.5752          | 64.6556  |
| 0.0277        | 3.78  | 3500 | 30.3  | 46.02 | 1.5791          | 64.6105  |
| 0.0364        | 3.88  | 3600 | 29.92 | 45.38 | 1.5895          | 65.0608  |
| 0.0398        | 3.99  | 3700 | 27.79 | 44.59 | 1.6167          | 68.2575  |
| 0.0152        | 4.1   | 3800 | 28.42 | 44.83 | 1.6241          | 67.5822  |
| 0.0201        | 4.21  | 3900 | 29.02 | 45.11 | 1.6243          | 67.4921  |
| 0.0168        | 4.31  | 4000 | 26.85 | 44.41 | 1.6195          | 73.5254  |


### Framework versions

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