---
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
- ymoslem/EUbookshop-Speech-Irish
metrics:
- bleu
- wer
model-index:
- name: Whisper Medium GA-EN Speech Translation
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - name: Bleu
      type: bleu
      value: 31.02
    - 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 Medium 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, Wikimedia, and EUbookshop dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0576
- Bleu: 31.02
- Chrf: 53.51
- 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 | Validation Loss | Bleu  | Chrf  | Wer      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.5374        | 0.0138 | 100  | 2.1201          | 2.56  | 18.92 | 222.4674 |
| 2.446         | 0.0276 | 200  | 2.1960          | 3.07  | 20.56 | 170.5088 |
| 2.2819        | 0.0414 | 300  | 1.9811          | 5.87  | 25.17 | 114.5880 |
| 2.1904        | 0.0552 | 400  | 1.9974          | 8.41  | 25.65 | 99.1896  |
| 2.026         | 0.0690 | 500  | 1.8961          | 7.99  | 27.64 | 130.7069 |
| 2.0448        | 0.0828 | 600  | 1.9410          | 9.15  | 27.78 | 104.9077 |
| 1.8606        | 0.0966 | 700  | 1.8451          | 9.57  | 29.34 | 110.4908 |
| 1.9887        | 0.1103 | 800  | 1.7419          | 13.44 | 32.32 | 84.3314  |
| 1.8633        | 0.1241 | 900  | 1.7376          | 13.43 | 31.58 | 102.1162 |
| 1.7576        | 0.1379 | 1000 | 1.6879          | 11.9  | 32.68 | 106.6186 |
| 1.7142        | 0.1517 | 1100 | 1.7571          | 12.4  | 33.66 | 102.6114 |
| 1.7168        | 0.1655 | 1200 | 1.6003          | 17.35 | 36.55 | 87.9784  |
| 1.6741        | 0.1793 | 1300 | 1.5883          | 15.41 | 35.46 | 92.8411  |
| 1.6534        | 0.1931 | 1400 | 1.5366          | 17.12 | 37.24 | 90.2296  |
| 1.58          | 0.2069 | 1500 | 1.5141          | 17.49 | 38.5  | 92.1207  |
| 1.403         | 0.2207 | 1600 | 1.4606          | 16.78 | 39.13 | 88.9689  |
| 1.3806        | 0.2345 | 1700 | 1.4263          | 19.26 | 40.02 | 86.7177  |
| 1.5111        | 0.2483 | 1800 | 1.4060          | 18.4  | 39.47 | 92.2557  |
| 1.4261        | 0.2621 | 1900 | 1.3911          | 21.19 | 42.13 | 78.7033  |
| 1.2974        | 0.2759 | 2000 | 1.3871          | 15.6  | 38.66 | 100.3152 |
| 1.2694        | 0.2897 | 2100 | 1.3527          | 16.21 | 39.99 | 91.2652  |
| 1.204         | 0.3034 | 2200 | 1.3232          | 20.2  | 41.18 | 86.8978  |
| 1.1922        | 0.3172 | 2300 | 1.3338          | 16.44 | 40.85 | 103.1968 |
| 1.1237        | 0.3310 | 2400 | 1.2830          | 19.29 | 43.73 | 94.4620  |
| 1.0989        | 0.3448 | 2500 | 1.2844          | 25.11 | 46.84 | 75.0563  |
| 1.0766        | 0.3586 | 2600 | 1.2578          | 23.87 | 46.1  | 74.5160  |
| 1.0432        | 0.3724 | 2700 | 1.2414          | 22.31 | 44.91 | 86.9878  |
| 1.1588        | 0.3862 | 2800 | 1.2051          | 23.32 | 45.94 | 77.1724  |
| 1.0062        | 0.4    | 2900 | 1.2059          | 26.15 | 48.27 | 69.4282  |
| 0.9178        | 0.4138 | 3000 | 1.1756          | 29.13 | 48.92 | 64.7456  |
| 0.9108        | 0.4276 | 3100 | 1.1665          | 28.34 | 48.9  | 67.2220  |
| 0.9868        | 0.4414 | 3200 | 1.1489          | 25.64 | 48.93 | 75.3264  |
| 0.9563        | 0.4552 | 3300 | 1.1181          | 27.58 | 49.67 | 71.8145  |
| 0.9138        | 0.4690 | 3400 | 1.1247          | 28.37 | 50.96 | 71.4543  |
| 0.8508        | 0.4828 | 3500 | 1.1007          | 29.75 | 51.41 | 68.3476  |
| 0.836         | 0.4966 | 3600 | 1.1114          | 30.99 | 52.2  | 66.5916  |
| 0.8435        | 0.5103 | 3700 | 1.0782          | 30.64 | 52.77 | 68.2125  |
| 0.8323        | 0.5241 | 3800 | 1.0744          | 29.78 | 52.94 | 68.9779  |
| 0.818         | 0.5379 | 3900 | 1.0639          | 31.23 | 53.21 | 67.7623  |
| 0.8095        | 0.5517 | 4000 | 1.0576          | 31.02 | 53.51 | 68.5277  |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1