---
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
metrics:
- bleu
- wer
model-index:
- name: Whisper Medium GA-EN Speech Translation Raw
  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: 27.65
    - name: Wer
      type: wer
      value: 71.09410175596578
---

<!-- 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 Raw

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, and SpokenWords dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6246
- Bleu: 27.65
- Chrf: 47.08
- Wer: 71.0941

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bleu  | Chrf  | Wer      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.3743        | 0.0539 | 100  | 2.1064          | 5.67  | 20.91 | 126.9248 |
| 2.3196        | 0.1079 | 200  | 2.1133          | 11.35 | 26.01 | 89.5092  |
| 2.2729        | 0.1618 | 300  | 2.0561          | 6.85  | 25.04 | 156.5061 |
| 2.0887        | 0.2157 | 400  | 1.9701          | 10.46 | 29.21 | 118.6853 |
| 1.9663        | 0.2697 | 500  | 1.9824          | 16.53 | 31.2  | 77.5326  |
| 1.9504        | 0.3236 | 600  | 1.8619          | 7.02  | 27.46 | 193.7416 |
| 1.7843        | 0.3776 | 700  | 1.8683          | 16.6  | 33.6  | 87.7082  |
| 1.8915        | 0.4315 | 800  | 1.7730          | 16.89 | 36.54 | 91.8505  |
| 1.6921        | 0.4854 | 900  | 1.8049          | 13.14 | 34.45 | 114.0477 |
| 1.4761        | 0.5394 | 1000 | 1.8310          | 22.12 | 37.3  | 77.1724  |
| 1.3067        | 0.5933 | 1100 | 1.7911          | 17.21 | 34.34 | 90.5448  |
| 1.3564        | 0.6472 | 1200 | 1.7045          | 20.09 | 39.67 | 85.1869  |
| 1.489         | 0.7012 | 1300 | 1.7601          | 15.3  | 36.53 | 107.8793 |
| 1.3023        | 0.7551 | 1400 | 1.7428          | 18.99 | 39.54 | 89.7794  |
| 1.1744        | 0.8091 | 1500 | 1.7446          | 21.68 | 41.78 | 79.4687  |
| 1.0122        | 0.8630 | 1600 | 1.7180          | 18.28 | 39.27 | 96.7582  |
| 1.0787        | 0.9169 | 1700 | 1.6144          | 16.94 | 39.74 | 98.8744  |
| 0.9561        | 0.9709 | 1800 | 1.6290          | 25.29 | 42.13 | 74.9662  |
| 0.4452        | 1.0248 | 1900 | 1.7223          | 18.95 | 39.14 | 97.0734  |
| 0.4397        | 1.0787 | 2000 | 1.6855          | 23.4  | 40.9  | 77.9379  |
| 0.4382        | 1.1327 | 2100 | 1.6911          | 24.95 | 41.19 | 72.8951  |
| 0.3937        | 1.1866 | 2200 | 1.7127          | 23.33 | 41.09 | 78.4331  |
| 0.4119        | 1.2406 | 2300 | 1.6796          | 23.25 | 42.32 | 83.6560  |
| 0.4139        | 1.2945 | 2400 | 1.6730          | 23.13 | 43.25 | 83.3408  |
| 0.3506        | 1.3484 | 2500 | 1.7361          | 23.37 | 42.31 | 79.9190  |
| 0.4109        | 1.4024 | 2600 | 1.6233          | 23.78 | 44.32 | 82.8005  |
| 0.3563        | 1.4563 | 2700 | 1.6383          | 20.41 | 43.66 | 98.1540  |
| 0.3355        | 1.5102 | 2800 | 1.6675          | 25.27 | 44.91 | 75.6866  |
| 0.2751        | 1.5642 | 2900 | 1.7011          | 24.64 | 43.19 | 74.2008  |
| 0.28          | 1.6181 | 3000 | 1.6308          | 24.76 | 45.49 | 79.4687  |
| 0.3108        | 1.6721 | 3100 | 1.5976          | 28.9  | 47.03 | 68.7978  |
| 0.3231        | 1.7260 | 3200 | 1.6070          | 27.82 | 46.1  | 69.8334  |
| 0.2665        | 1.7799 | 3300 | 1.5853          | 26.0  | 44.51 | 74.9212  |
| 0.2788        | 1.8339 | 3400 | 1.5689          | 26.37 | 46.94 | 75.0113  |
| 0.243         | 1.8878 | 3500 | 1.5885          | 29.12 | 46.94 | 67.4021  |
| 0.2605        | 1.9417 | 3600 | 1.5680          | 28.64 | 46.38 | 67.8523  |
| 0.1664        | 1.9957 | 3700 | 1.5910          | 28.45 | 46.64 | 68.0774  |
| 0.049         | 2.0496 | 3800 | 1.6385          | 27.78 | 46.51 | 69.9235  |
| 0.0635        | 2.1036 | 3900 | 1.6272          | 27.57 | 47.25 | 71.1391  |
| 0.0467        | 2.1575 | 4000 | 1.6246          | 27.65 | 47.08 | 71.0941  |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1