---
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 Medium 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: 29.54
    - name: Wer
      type: wer
      value: 62.40432237730752
---

<!-- 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-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: 1.1929
- Bleu: 29.54
- Chrf: 51.58
- Wer: 62.4043

## 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      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.4382        | 0.0109 | 100  | 2.1114          | 3.07  | 16.85 | 171.0491 |
| 2.6151        | 0.0219 | 200  | 2.0207          | 6.25  | 23.02 | 126.9698 |
| 2.5699        | 0.0328 | 300  | 1.8660          | 5.71  | 24.03 | 155.5606 |
| 2.3084        | 0.0438 | 400  | 1.8084          | 9.87  | 28.45 | 129.0860 |
| 2.3327        | 0.0547 | 500  | 1.7823          | 12.01 | 31.92 | 102.7915 |
| 2.1495        | 0.0657 | 600  | 1.7238          | 13.97 | 32.4  | 98.6042  |
| 2.2164        | 0.0766 | 700  | 1.6538          | 11.21 | 33.19 | 146.0153 |
| 2.0071        | 0.0876 | 800  | 1.7038          | 14.34 | 35.72 | 96.9383  |
| 1.8334        | 0.0985 | 900  | 1.6329          | 16.51 | 37.23 | 96.8032  |
| 1.8359        | 0.1095 | 1000 | 1.6637          | 17.87 | 35.94 | 84.4665  |
| 1.7703        | 0.1204 | 1100 | 1.5626          | 19.54 | 39.02 | 79.7839  |
| 1.5805        | 0.1314 | 1200 | 1.5618          | 20.19 | 40.4  | 77.8028  |
| 1.4545        | 0.1423 | 1300 | 1.5599          | 13.88 | 35.53 | 112.5619 |
| 1.5177        | 0.1533 | 1400 | 1.4880          | 18.79 | 40.11 | 84.6916  |
| 1.6335        | 0.1642 | 1500 | 1.4996          | 16.41 | 38.64 | 96.9833  |
| 1.3809        | 0.1752 | 1600 | 1.4739          | 18.3  | 40.17 | 101.8910 |
| 1.2694        | 0.1861 | 1700 | 1.4498          | 22.53 | 43.15 | 76.9923  |
| 1.2321        | 0.1970 | 1800 | 1.4163          | 19.92 | 42.59 | 84.6015  |
| 1.1969        | 0.2080 | 1900 | 1.4137          | 21.63 | 44.92 | 85.3670  |
| 1.2023        | 0.2189 | 2000 | 1.3530          | 20.42 | 41.57 | 82.8906  |
| 1.1676        | 0.2299 | 2100 | 1.3723          | 22.82 | 44.23 | 78.1180  |
| 1.0332        | 0.2408 | 2200 | 1.3641          | 26.73 | 44.75 | 70.2386  |
| 0.8589        | 0.2518 | 2300 | 1.3344          | 26.94 | 46.89 | 72.7600  |
| 0.9829        | 0.2627 | 2400 | 1.3181          | 28.15 | 47.21 | 69.1130  |
| 0.8228        | 0.2737 | 2500 | 1.3049          | 26.98 | 47.41 | 74.0207  |
| 0.7667        | 0.2846 | 2600 | 1.2698          | 30.0  | 49.42 | 65.1058  |
| 0.8749        | 0.2956 | 2700 | 1.2878          | 27.91 | 47.67 | 66.9518  |
| 0.7504        | 0.3065 | 2800 | 1.2670          | 32.03 | 50.35 | 63.6650  |
| 0.7069        | 0.3175 | 2900 | 1.2771          | 30.7  | 49.53 | 64.4304  |
| 0.7199        | 0.3284 | 3000 | 1.2658          | 30.21 | 48.93 | 65.5561  |
| 0.6207        | 0.3394 | 3100 | 1.2687          | 30.82 | 49.11 | 66.0063  |
| 0.5995        | 0.3503 | 3200 | 1.2207          | 31.99 | 50.94 | 62.9446  |
| 0.6294        | 0.3612 | 3300 | 1.2422          | 31.05 | 50.85 | 64.7006  |
| 0.4612        | 0.3722 | 3400 | 1.2203          | 33.1  | 51.82 | 61.9090  |
| 0.5138        | 0.3831 | 3500 | 1.2007          | 32.08 | 51.86 | 63.0797  |
| 0.5059        | 0.3941 | 3600 | 1.2130          | 31.8  | 51.19 | 63.9352  |
| 0.417         | 0.4050 | 3700 | 1.1975          | 32.45 | 51.41 | 62.2692  |
| 0.2958        | 0.4160 | 3800 | 1.2046          | 29.29 | 51.39 | 62.7645  |
| 0.393         | 0.4269 | 3900 | 1.1968          | 28.95 | 51.45 | 63.1697  |
| 0.3858        | 0.4379 | 4000 | 1.1929          | 29.54 | 51.58 | 62.4043  |


### Framework versions

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