---
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 Small 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: 33.1
    - name: Wer
      type: wer
      value: 62.40432237730752
---

<!-- 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 Small 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 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1873
- Bleu: 33.1
- Chrf: 51.85
- Wer: 62.4043

## 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: 0.02
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bleu  | Chrf  | Wer      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.6291        | 0.0109 | 100  | 2.1971          | 2.33  | 16.34 | 175.5516 |
| 2.6591        | 0.0219 | 200  | 2.0357          | 5.57  | 22.49 | 122.2873 |
| 2.5637        | 0.0328 | 300  | 1.8690          | 7.67  | 26.29 | 133.0032 |
| 2.2954        | 0.0438 | 400  | 1.8062          | 11.2  | 30.03 | 114.2278 |
| 2.3292        | 0.0547 | 500  | 1.7421          | 9.85  | 29.28 | 117.2895 |
| 2.1223        | 0.0657 | 600  | 1.6739          | 14.56 | 32.56 | 84.2864  |
| 2.2398        | 0.0766 | 700  | 1.7187          | 13.86 | 34.74 | 98.9644  |
| 2.002         | 0.0876 | 800  | 1.6392          | 15.53 | 36.64 | 96.7582  |
| 1.8611        | 0.0985 | 900  | 1.6283          | 15.8  | 36.32 | 94.3719  |
| 1.8498        | 0.1095 | 1000 | 1.6102          | 17.58 | 36.0  | 85.5921  |
| 1.7585        | 0.1204 | 1100 | 1.6337          | 15.91 | 36.61 | 100.2251 |
| 1.6115        | 0.1314 | 1200 | 1.5381          | 22.21 | 39.94 | 76.8122  |
| 1.4415        | 0.1423 | 1300 | 1.5864          | 20.36 | 37.87 | 79.1986  |
| 1.5103        | 0.1533 | 1400 | 1.4925          | 23.2  | 41.26 | 75.2364  |
| 1.6576        | 0.1642 | 1500 | 1.4508          | 18.12 | 40.49 | 102.9266 |
| 1.3429        | 0.1752 | 1600 | 1.4399          | 27.88 | 43.74 | 69.7884  |
| 1.2522        | 0.1861 | 1700 | 1.4256          | 23.04 | 43.31 | 77.1724  |
| 1.2018        | 0.1970 | 1800 | 1.4072          | 21.06 | 40.39 | 78.6583  |
| 1.1945        | 0.2080 | 1900 | 1.4222          | 23.0  | 42.71 | 76.7222  |
| 1.1869        | 0.2189 | 2000 | 1.3992          | 22.54 | 42.02 | 75.8667  |
| 1.1752        | 0.2299 | 2100 | 1.3926          | 20.81 | 41.07 | 79.5137  |
| 1.0281        | 0.2408 | 2200 | 1.3633          | 27.24 | 45.55 | 69.6083  |
| 0.894         | 0.2518 | 2300 | 1.3287          | 28.6  | 45.58 | 65.8712  |
| 0.9788        | 0.2627 | 2400 | 1.3138          | 27.75 | 46.21 | 69.2931  |
| 0.8418        | 0.2737 | 2500 | 1.3064          | 27.85 | 46.17 | 68.3026  |
| 0.7559        | 0.2846 | 2600 | 1.2903          | 28.44 | 48.52 | 68.3476  |
| 0.8632        | 0.2956 | 2700 | 1.2834          | 27.87 | 46.86 | 68.3476  |
| 0.7501        | 0.3065 | 2800 | 1.2669          | 28.63 | 49.25 | 68.5277  |
| 0.6953        | 0.3175 | 2900 | 1.2615          | 30.46 | 48.83 | 64.4304  |
| 0.7195        | 0.3284 | 3000 | 1.2514          | 27.49 | 47.94 | 71.0941  |
| 0.6155        | 0.3394 | 3100 | 1.2428          | 30.06 | 49.64 | 66.5916  |
| 0.605         | 0.3503 | 3200 | 1.2040          | 31.64 | 50.27 | 63.8451  |
| 0.6349        | 0.3612 | 3300 | 1.2077          | 28.96 | 49.35 | 65.3760  |
| 0.4669        | 0.3722 | 3400 | 1.2219          | 31.17 | 48.95 | 64.2503  |
| 0.5196        | 0.3831 | 3500 | 1.2124          | 30.97 | 50.13 | 63.8001  |
| 0.5141        | 0.3941 | 3600 | 1.2026          | 31.97 | 50.8  | 63.0347  |
| 0.4221        | 0.4050 | 3700 | 1.1893          | 31.76 | 51.35 | 63.4399  |
| 0.2951        | 0.4160 | 3800 | 1.2049          | 32.4  | 51.08 | 63.1247  |
| 0.3898        | 0.4269 | 3900 | 1.1906          | 32.15 | 51.09 | 63.5299  |
| 0.4071        | 0.4379 | 4000 | 1.1873          | 33.1  | 51.85 | 62.4043  |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1