---
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
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, and SpokenWords
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - name: Bleu
      type: bleu
      value: 27.06
    - name: Wer
      type: wer
      value: 73.4804142278253
---

<!-- 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-small](https://huggingface.co./openai/whisper-small) on the IWSLT-2023, FLEURS, BiteSize, and SpokenWords dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2998
- Bleu: 27.06
- Chrf: 47.61
- Wer: 73.4804

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.03
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Bleu  | Chrf  | Validation Loss | Wer      |
|:-------------:|:-----:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.5227        | 0.05  | 100  | 1.05  | 12.82 | 2.4253          | 343.2238 |
| 2.4775        | 0.11  | 200  | 10.04 | 24.39 | 2.0665          | 95.2724  |
| 2.114         | 0.16  | 300  | 8.79  | 28.6  | 1.9792          | 141.9181 |
| 1.9813        | 0.22  | 400  | 17.5  | 33.84 | 1.7596          | 82.8906  |
| 1.6979        | 0.27  | 500  | 13.89 | 33.51 | 1.6820          | 115.0383 |
| 1.7157        | 0.32  | 600  | 18.54 | 36.44 | 1.5795          | 91.4003  |
| 1.3845        | 0.38  | 700  | 19.51 | 39.03 | 1.4989          | 88.7888  |
| 1.3803        | 0.43  | 800  | 25.18 | 40.96 | 1.4176          | 69.5182  |
| 1.1           | 0.49  | 900  | 28.98 | 44.78 | 1.3666          | 65.9613  |
| 1.1843        | 0.54  | 1000 | 27.59 | 45.91 | 1.3298          | 70.4638  |
| 1.1317        | 0.59  | 1100 | 1.5018| 20.22 | 41.14           | 86.9878  |
| 1.071         | 0.65  | 1200 | 1.4600| 20.67 | 40.43           | 85.6371  |
| 1.1542        | 0.7   | 1300 | 1.4114| 26.84 | 43.76           | 69.5182  |
| 1.0729        | 0.76  | 1400 | 1.4056| 22.98 | 42.65           | 78.0729  |
| 0.8747        | 0.81  | 1500 | 1.3537| 24.65 | 44.89           | 73.4804  |
| 0.8626        | 0.86  | 1600 | 1.3391| 28.0  | 46.03           | 68.7978  |
| 0.7643        | 0.92  | 1700 | 1.3250| 27.23 | 45.31           | 70.3287  |
| 0.6971        | 0.97  | 1800 | 1.2795| 30.05 | 48.28           | 65.5110  |
| 0.3055        | 1.02  | 1900 | 1.2994| 27.41 | 47.91           | 71.1842  |
| 0.2801        | 1.08  | 2000 | 1.2998| 27.06 | 47.61           | 73.4804  |


### Framework versions

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