---
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, normalized
      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.
The datasets were processed with noise reduction and normalization (both the train and test splits).
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