---
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
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - name: Bleu
      type: bleu
      value: 27.85
    - name: Wer
      type: wer
      value: 73.43538946420531
---

<!-- 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 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4107
- Bleu: 27.85
- Chrf: 46.91
- Wer: 73.4354

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bleu  | Chrf  | Wer      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.3549        | 0.1312 | 100  | 1.8335          | 7.17  | 24.71 | 135.7497 |
| 1.8906        | 0.2625 | 200  | 1.5173          | 15.56 | 34.19 | 91.5353  |
| 1.653         | 0.3937 | 300  | 1.3530          | 17.17 | 36.35 | 103.7371 |
| 1.4901        | 0.5249 | 400  | 1.3334          | 24.65 | 43.44 | 78.2530  |
| 1.3551        | 0.6562 | 500  | 1.2763          | 27.04 | 43.88 | 67.4471  |
| 1.2187        | 0.7874 | 600  | 1.2618          | 27.08 | 43.98 | 69.2031  |
| 1.0359        | 0.9186 | 700  | 1.2644          | 20.82 | 40.76 | 96.8483  |
| 0.5364        | 1.0499 | 800  | 1.3258          | 24.9  | 42.9  | 65.8262  |
| 0.4892        | 1.1811 | 900  | 1.3296          | 23.82 | 42.86 | 72.3098  |
| 0.4504        | 1.3123 | 1000 | 1.3001          | 25.78 | 43.72 | 75.5065  |
| 0.4161        | 1.4436 | 1100 | 1.2948          | 27.16 | 44.31 | 67.3120  |
| 0.3953        | 1.5748 | 1200 | 1.3261          | 29.14 | 44.65 | 65.5110  |
| 0.3509        | 1.7060 | 1300 | 1.3398          | 22.75 | 44.32 | 80.1441  |
| 0.2955        | 1.8373 | 1400 | 1.3077          | 26.29 | 42.89 | 74.8762  |
| 0.2801        | 1.9685 | 1500 | 1.3206          | 25.51 | 43.39 | 76.5871  |
| 0.1084        | 2.0997 | 1600 | 1.3609          | 28.01 | 45.59 | 68.1225  |
| 0.1003        | 2.2310 | 1700 | 1.3722          | 26.4  | 42.69 | 72.8501  |
| 0.1083        | 2.3622 | 1800 | 1.3776          | 3.81  | 19.2  | 396.1279 |
| 0.0939        | 2.4934 | 1900 | 1.3729          | 28.43 | 45.61 | 69.2031  |
| 0.0909        | 2.6247 | 2000 | 1.3834          | 27.12 | 43.39 | 67.4921  |
| 0.0772        | 2.7559 | 2100 | 1.4094          | 28.44 | 44.15 | 65.5110  |
| 0.0753        | 2.8871 | 2200 | 1.3825          | 30.5  | 46.21 | 64.9257  |
| 0.0438        | 3.0184 | 2300 | 1.4198          | 30.44 | 46.18 | 62.5844  |
| 0.0257        | 3.1496 | 2400 | 1.4033          | 31.03 | 46.67 | 63.6650  |
| 0.0252        | 3.2808 | 2500 | 1.4045          | 31.2  | 46.44 | 62.4043  |
| 0.0241        | 3.4121 | 2600 | 1.3971          | 32.42 | 48.21 | 61.1436  |
| 0.0208        | 3.5433 | 2700 | 1.4129          | 30.36 | 46.28 | 65.7362  |
| 0.0186        | 3.6745 | 2800 | 1.4076          | 31.14 | 47.73 | 64.4304  |
| 0.018         | 3.8058 | 2900 | 1.4151          | 27.67 | 45.87 | 73.5254  |
| 0.0193        | 3.9370 | 3000 | 1.4107          | 27.85 | 46.91 | 73.4354  |


### Framework versions

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