---
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: 28.37
    - name: Wer
      type: wer
      value: 68.12246735704638
---

<!-- 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:
- Bleu: 28.37
- Chrf: 45.85
- Loss: 1.4194
- Wer: 68.1225

## 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_ratio: 0.03
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Bleu  | Chrf  | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.5874        | 0.0539 | 100  | 4.9   | 19.49 | 2.1785          | 114.0027 |
| 2.3237        | 0.1079 | 200  | 6.48  | 22.77 | 2.1129          | 151.8235 |
| 2.192         | 0.1618 | 300  | 7.92  | 25.9  | 2.0182          | 148.6718 |
| 1.9861        | 0.2157 | 400  | 10.55 | 28.55 | 1.8607          | 121.0266 |
| 1.8893        | 0.2697 | 500  | 16.68 | 33.64 | 1.8560          | 89.7794  |
| 1.8526        | 0.3236 | 600  | 8.83  | 30.12 | 1.7738          | 166.9968 |
| 1.6537        | 0.3776 | 700  | 10.94 | 33.83 | 1.6781          | 152.2287 |
| 1.7103        | 0.4315 | 800  | 16.9  | 36.4  | 1.6389          | 92.2557  |
| 1.4837        | 0.4854 | 900  | 13.81 | 34.5  | 1.6077          | 124.2233 |
| 1.2784        | 0.5394 | 1000 | 14.79 | 37.53 | 1.6103          | 116.3440 |
| 1.111         | 0.5933 | 1100 | 19.31 | 39.0  | 1.5579          | 93.6965  |
| 1.167         | 0.6472 | 1200 | 20.88 | 41.7  | 1.5210          | 91.6704  |
| 1.2217        | 0.7012 | 1300 | 21.29 | 41.72 | 1.4719          | 84.9167  |
| 1.0613        | 0.7551 | 1400 | 28.3  | 44.37 | 1.4663          | 67.1319  |
| 0.9256        | 0.8091 | 1500 | 27.5  | 45.59 | 1.4258          | 68.7078  |
| 0.8023        | 0.8630 | 1600 | 27.1  | 46.27 | 1.4027          | 72.7600  |
| 0.8327        | 0.9169 | 1700 | 27.03 | 46.19 | 1.3784          | 73.0302  |
| 0.7019        | 0.9709 | 1800 | 28.91 | 46.34 | 1.4127          | 67.4921  |
| 0.2681        | 1.0248 | 1900 | 28.53 | 47.12 | 1.3955          | 68.3026  |
| 0.2659        | 1.0787 | 2000 | 28.37 | 45.85 | 1.4194          | 68.1225  |


### Framework versions

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