---
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
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     as
        well as a copy of the dataset with noise reduction and normalization (for
        both train and test)
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - name: Bleu
      type: bleu
      value: 30.66
    - name: Wer
      type: wer
      value: 65.46600630346691
---

<!-- 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, SpokenWords, Tatoeba, and Wikimedia     as well as a copy of the dataset with noise reduction and normalization (for both train and test) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3339
- Bleu: 30.66
- Chrf: 46.99
- Wer: 65.4660

## 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: 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: 0.01
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Bleu  | Chrf  | Validation Loss | Wer      |
|:-------------:|:-----:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 1.41          | 0.07  | 100  | 9.78  | 25.23 | 1.8782          | 96.3980  |
| 1.2436        | 0.13  | 200  | 10.23 | 28.66 | 1.8301          | 125.9343 |
| 1.593         | 0.2   | 300  | 9.53  | 30.7  | 1.7066          | 137.1454 |
| 1.9589        | 0.26  | 400  | 12.08 | 32.94 | 1.5629          | 109.3652 |
| 1.8174        | 0.33  | 500  | 13.73 | 34.5  | 1.5154          | 123.5930 |
| 1.6775        | 0.39  | 600  | 15.8  | 35.68 | 1.5220          | 102.2062 |
| 1.7074        | 0.46  | 700  | 16.62 | 37.96 | 1.4570          | 100.5853 |
| 1.5793        | 0.53  | 800  | 24.5  | 39.91 | 1.4265          | 71.3643  |
| 1.3708        | 0.59  | 900  | 24.35 | 42.26 | 1.3845          | 73.7956  |
| 1.3217        | 0.66  | 1000 | 19.34 | 41.3  | 1.3662          | 87.7533  |
| 1.2572        | 0.72  | 1100 | 21.59 | 41.35 | 1.3529          | 88.4286  |
| 1.1447        | 0.79  | 1200 | 28.39 | 44.99 | 1.3228          | 65.9163  |
| 1.1544        | 0.85  | 1300 | 23.69 | 43.07 | 1.2972          | 80.1891  |
| 1.0291        | 0.92  | 1400 | 29.36 | 45.45 | 1.2828          | 70.9590  |
| 0.9394        | 0.98  | 1500 | 26.44 | 44.0  | 1.2812          | 74.1558  |
| 0.3764        | 1.05  | 1600 | 26.95 | 44.82 | 1.3248          | 73.8406  |
| 0.3338        | 1.12  | 1700 | 26.5  | 44.96 | 1.3212          | 77.3976  |
| 0.3148        | 1.18  | 1800 | 29.57 | 46.31 | 1.3188          | 66.7267  |
| 0.3206        | 1.25  | 1900 | 30.87 | 47.21 | 1.3050          | 64.4755  |
| 0.3069        | 1.31  | 2000 | 30.15 | 46.19 | 1.3053          | 65.6911  |
| 0.3342        | 1.38  | 2100 | 1.3506| 24.14 | 44.12           | 77.2625  |
| 0.3125        | 1.44  | 2200 | 1.3369| 30.21 | 46.08           | 63.9802  |
| 0.319         | 1.51  | 2300 | 1.3601| 27.71 | 45.45           | 69.9235  |
| 0.3067        | 1.58  | 2400 | 1.3473| 26.92 | 45.73           | 69.3381  |
| 0.2621        | 1.64  | 2500 | 1.3354| 28.36 | 46.14           | 66.9068  |
| 0.2709        | 1.71  | 2600 | 1.3339| 28.75 | 45.47           | 65.2859  |
| 0.2644        | 1.77  | 2700 | 1.3100| 28.84 | 47.35           | 65.8262  |
| 0.2511        | 1.84  | 2800 | 1.3261| 29.41 | 47.31           | 69.4732  |
| 0.2232        | 1.9   | 2900 | 1.3382| 30.79 | 46.63           | 64.1153  |
| 0.236         | 1.97  | 3000 | 1.3339| 30.66 | 46.99           | 65.4660  |


### Framework versions

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