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---
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: 33.77
    - name: Wer
      type: wer
      value: 60.828455650607836
---

<!-- 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: 1.2028
- Bleu: 33.77
- Chrf: 52.79
- Wer: 60.8285

## 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      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.4145        | 0.0109 | 100  | 2.1019          | 2.32  | 16.08 | 170.5088 |
| 2.6073        | 0.0219 | 200  | 2.0370          | 5.94  | 23.77 | 158.8924 |
| 2.593         | 0.0328 | 300  | 1.8529          | 3.67  | 21.53 | 238.6312 |
| 2.3123        | 0.0438 | 400  | 1.8500          | 9.01  | 28.13 | 135.0293 |
| 2.3347        | 0.0547 | 500  | 1.7816          | 15.05 | 31.9  | 90.7249  |
| 2.1277        | 0.0657 | 600  | 1.6916          | 14.24 | 32.29 | 88.4286  |
| 2.1836        | 0.0766 | 700  | 1.6517          | 12.15 | 32.7  | 128.1405 |
| 2.0112        | 0.0876 | 800  | 1.6275          | 19.76 | 38.15 | 79.2886  |
| 1.8387        | 0.0985 | 900  | 1.6349          | 17.26 | 38.82 | 91.0851  |
| 1.8335        | 0.1095 | 1000 | 1.5843          | 20.93 | 38.02 | 75.9118  |
| 1.7849        | 0.1204 | 1100 | 1.5863          | 15.98 | 37.5  | 96.5781  |
| 1.5698        | 0.1314 | 1200 | 1.5371          | 16.42 | 39.07 | 103.6020 |
| 1.4759        | 0.1423 | 1300 | 1.5250          | 18.56 | 38.41 | 96.5781  |
| 1.4915        | 0.1533 | 1400 | 1.4862          | 22.05 | 40.15 | 75.1013  |
| 1.6583        | 0.1642 | 1500 | 1.4727          | 18.11 | 39.65 | 95.7677  |
| 1.3981        | 0.1752 | 1600 | 1.4367          | 27.31 | 44.5  | 66.0513  |
| 1.2646        | 0.1861 | 1700 | 1.4574          | 22.85 | 42.19 | 74.4710  |
| 1.2172        | 0.1970 | 1800 | 1.3818          | 20.77 | 42.5  | 82.7105  |
| 1.183         | 0.2080 | 1900 | 1.4380          | 22.75 | 41.28 | 76.7672  |
| 1.1931        | 0.2189 | 2000 | 1.3917          | 23.58 | 41.13 | 77.3075  |
| 1.172         | 0.2299 | 2100 | 1.3892          | 24.58 | 44.4  | 74.3809  |
| 1.0284        | 0.2408 | 2200 | 1.3806          | 23.34 | 44.1  | 78.0279  |
| 0.8507        | 0.2518 | 2300 | 1.3210          | 28.67 | 46.79 | 67.1769  |
| 0.9615        | 0.2627 | 2400 | 1.3103          | 27.95 | 46.8  | 70.0135  |
| 0.8049        | 0.2737 | 2500 | 1.3141          | 29.92 | 48.9  | 67.2220  |
| 0.7639        | 0.2846 | 2600 | 1.3085          | 30.91 | 49.05 | 64.2053  |
| 0.8594        | 0.2956 | 2700 | 1.3378          | 27.8  | 47.84 | 68.8879  |
| 0.7482        | 0.3065 | 2800 | 1.2978          | 30.6  | 48.62 | 64.9257  |
| 0.6941        | 0.3175 | 2900 | 1.3060          | 29.92 | 47.92 | 65.8712  |
| 0.7282        | 0.3284 | 3000 | 1.2959          | 31.09 | 48.13 | 65.3309  |
| 0.6298        | 0.3394 | 3100 | 1.2893          | 29.76 | 48.8  | 67.1769  |
| 0.619         | 0.3503 | 3200 | 1.2388          | 32.61 | 50.27 | 62.0891  |
| 0.6252        | 0.3612 | 3300 | 1.2550          | 32.71 | 50.96 | 62.4493  |
| 0.4699        | 0.3722 | 3400 | 1.2463          | 32.02 | 51.24 | 65.2409  |
| 0.5121        | 0.3831 | 3500 | 1.2214          | 32.26 | 51.29 | 63.7551  |
| 0.5092        | 0.3941 | 3600 | 1.2182          | 32.88 | 51.59 | 62.0891  |
| 0.4365        | 0.4050 | 3700 | 1.2049          | 32.16 | 51.5  | 62.3143  |
| 0.2971        | 0.4160 | 3800 | 1.2201          | 34.45 | 52.78 | 59.7479  |
| 0.389         | 0.4269 | 3900 | 1.2007          | 33.86 | 53.28 | 60.6033  |
| 0.3879        | 0.4379 | 4000 | 1.2028          | 33.77 | 52.79 | 60.8285  |


### Framework versions

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