---
language:
- ga
- en
license: apache-2.0
base_model: openai/whisper-large
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 Large 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: 30.16
    - name: Wer
      type: wer
      value: 69.968482665466
---

<!-- 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 Large GA-EN Speech Translation

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset.
The datasets are augmented in two ways: noise augmentation, and truncating low-amplitude samples. 
The best model checkpoint (this version) based on ChrF is at step 3000, epoch 0.99, 
and it achieves the following results on the evaluation set:
- Loss: 1.1742
- Bleu: 30.16
- Chrf: 50.72
- Wer: 69.9685

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- 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: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu  | Chrf  | Wer      |
|:-------------:|:-----:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 3.1833        | 0.03  | 100  | 2.5169          | 2.03  | 16.8  | 215.5786 |
| 2.7632        | 0.07  | 200  | 2.1827          | 7.81  | 24.07 | 113.1022 |
| 2.5687        | 0.1   | 300  | 2.0746          | 6.16  | 24.2  | 158.8474 |
| 2.5615        | 0.13  | 400  | 1.9379          | 8.68  | 26.18 | 120.8465 |
| 2.4554        | 0.16  | 500  | 1.8932          | 12.14 | 28.94 | 103.1067 |
| 2.3546        | 0.2   | 600  | 1.8734          | 14.34 | 29.83 | 91.5353  |
| 2.2804        | 0.23  | 700  | 1.8075          | 13.18 | 33.07 | 105.5380 |
| 2.1408        | 0.26  | 800  | 1.7034          | 13.01 | 33.0  | 89.4642  |
| 2.0411        | 0.3   | 900  | 1.6556          | 16.73 | 34.97 | 91.4453  |
| 1.7766        | 0.33  | 1000 | 1.6505          | 17.21 | 35.54 | 83.5209  |
| 1.7704        | 0.36  | 1100 | 1.5800          | 17.54 | 38.11 | 77.1724  |
| 1.6537        | 0.39  | 1200 | 1.5684          | 14.2  | 35.39 | 95.6326  |
| 1.4841        | 0.43  | 1300 | 1.4970          | 22.96 | 39.35 | 71.3643  |
| 1.641         | 0.46  | 1400 | 1.4693          | 16.3  | 37.69 | 103.7821 |
| 1.393         | 0.49  | 1500 | 1.3923          | 27.21 | 43.87 | 69.3381  |
| 1.249         | 0.53  | 1600 | 1.3876          | 23.33 | 42.26 | 76.5421  |
| 1.3385        | 0.56  | 1700 | 1.3404          | 23.86 | 42.82 | 75.0563  |
| 1.2537        | 0.59  | 1800 | 1.3226          | 17.03 | 41.72 | 100.1801 |
| 1.2891        | 0.62  | 1900 | 1.2995          | 27.26 | 43.62 | 69.1580  |
| 1.226         | 0.66  | 2000 | 1.2605          | 30.89 | 47.34 | 63.5750  |
| 1.1268        | 0.69  | 2100 | 1.2783          | 27.43 | 45.97 | 67.4921  |
| 1.0007        | 0.72  | 2200 | 1.2521          | 27.21 | 47.25 | 71.0041  |
| 0.9565        | 0.76  | 2300 | 1.2219          | 31.65 | 48.07 | 64.2053  |
| 0.9309        | 0.79  | 2400 | 1.2193          | 31.4  | 48.18 | 64.1603  |
| 0.7923        | 0.82  | 2500 | 1.2099          | 28.88 | 48.89 | 69.7884  |
| 0.8199        | 0.85  | 2600 | 1.1972          | 29.37 | 48.07 | 67.3120  |
| 0.6974        | 0.89  | 2700 | 1.1857          | 29.7  | 48.95 | 70.5988  |
| 0.6736        | 0.92  | 2800 | 1.1884          | 29.33 | 48.97 | 72.7150  |
| 0.6826        | 0.95  | 2900 | 1.1834          | 30.76 | 50.11 | 68.1225  |
| 0.7001        | 0.99  | 3000 | 1.1742          | 30.16 | 50.72 | 69.9685  |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.0.1+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2