---
language:
- ga
- en
license: apache-2.0
base_model: openai/whisper-medium
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 Medium GA-EN Speech Translation
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia, augmented
        with noise
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - name: Bleu
      type: bleu
      value: 32.01
    - name: Wer
      type: wer
      value: 62.76452048626745
---

<!-- 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-medium](https://huggingface.co./openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia, augmented with noise dataset.
The datasets are augmented in two ways: noise augmentation, and truncating low-magnitude samples. 
The best model checkpoint (this version) based on ChrF is at step 2900, epoch 0.6349, 
and it achieves the following results on the evaluation set:
- Loss: 1.1883
- Bleu: 32.88
- Chrf: 51.52
- Wer: 62.0441

## 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: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.02
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bleu  | Chrf  | Wer      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.4487        | 0.0219 | 100  | 1.9518          | 8.34  | 24.49 | 117.2445 |
| 2.11          | 0.0438 | 200  | 1.6630          | 15.32 | 32.12 | 84.0612  |
| 1.9757        | 0.0657 | 300  | 1.5366          | 10.86 | 33.42 | 131.7875 |
| 1.7964        | 0.0876 | 400  | 1.4825          | 19.81 | 36.71 | 81.9451  |
| 1.6422        | 0.1095 | 500  | 1.4432          | 18.83 | 40.4  | 84.0162  |
| 1.3839        | 0.1314 | 600  | 1.4169          | 24.91 | 40.87 | 69.0230  |
| 1.352         | 0.1533 | 700  | 1.4340          | 25.01 | 41.57 | 71.5894  |
| 1.2434        | 0.1752 | 800  | 1.3813          | 24.05 | 41.29 | 73.7506  |
| 1.2223        | 0.1970 | 900  | 1.3578          | 25.89 | 41.61 | 70.5988  |
| 1.0414        | 0.2189 | 1000 | 1.3075          | 27.45 | 44.17 | 68.2575  |
| 0.9199        | 0.2408 | 1100 | 1.3022          | 23.14 | 44.3  | 84.1513  |
| 0.8648        | 0.2627 | 1200 | 1.3050          | 23.36 | 43.37 | 72.4448  |
| 0.8469        | 0.2846 | 1300 | 1.2853          | 28.37 | 45.97 | 67.1319  |
| 0.7649        | 0.3065 | 1400 | 1.2755          | 28.56 | 46.76 | 66.0964  |
| 0.7321        | 0.3284 | 1500 | 1.2750          | 27.23 | 46.1  | 69.3381  |
| 0.6541        | 0.3503 | 1600 | 1.2557          | 30.02 | 48.06 | 65.6011  |
| 0.6107        | 0.3722 | 1700 | 1.2520          | 30.41 | 49.23 | 64.2954  |
| 0.5738        | 0.3941 | 1800 | 1.2435          | 32.45 | 50.27 | 63.4399  |
| 0.4983        | 0.4160 | 1900 | 1.2007          | 31.17 | 48.58 | 64.0702  |
| 0.4439        | 0.4379 | 2000 | 1.2140          | 32.29 | 50.37 | 60.6033  |
| 0.367         | 0.4598 | 2100 | 1.2230          | 29.54 | 49.14 | 67.7172  |
| 0.2807        | 0.4817 | 2200 | 1.2277          | 33.1  | 51.21 | 62.9446  |
| 0.2621        | 0.5036 | 2300 | 1.2441          | 30.59 | 49.49 | 64.8807  |
| 0.2965        | 0.5255 | 2400 | 1.1969          | 31.82 | 49.67 | 63.5299  |
| 0.236         | 0.5473 | 2500 | 1.2275          | 31.17 | 50.29 | 65.1959  |
| 0.229         | 0.5692 | 2600 | 1.2008          | 30.02 | 50.27 | 70.6439  |
| 0.164         | 0.5911 | 2700 | 1.2192          | 31.37 | 50.57 | 63.6200  |
| 0.1786        | 0.6130 | 2800 | 1.1965          | 31.81 | 50.13 | 62.8546  |
| 0.1987        | 0.6349 | 2900 | 1.1883          | 32.88 | 51.52 | 62.0441  |
| 0.1633        | 0.6568 | 3000 | 1.1903          | 32.01 | 50.38 | 62.7645  |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.0.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1