transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer ga en dataset:ymoslem/IWSLT2023-GA-EN dataset:ymoslem/FLEURS-GA-EN dataset:ymoslem/BitesizeIrish-GA-EN dataset:ymoslem/SpokenWords-GA-EN-MTed dataset:ymoslem/Tatoeba-Speech-Irish dataset:ymoslem/Wikimedia-Speech-Irish base_model:openai/whisper-small base_model:finetune:openai/whisper-small license:apache-2.0 model-index endpoints_compatible region:us
---
base_model: openai/whisper-small
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
language:
- ga
- en
license: apache-2.0
metrics:
- bleu
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper Small GA-EN Speech Translation
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - type: bleu
      value: 32.04
      name: Bleu
    - type: wer
      value: 63.39486717694732
      name: Wer
---

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# Whisper Small GA-EN Speech Translation

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4631
- Bleu: 32.04
- Chrf: 48.69
- Wer: 63.3949

## 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: 64
- eval_batch_size: 64
- 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: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bleu  | Chrf  | Wer      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.3783        | 0.1312 | 100  | 1.8852          | 7.56  | 22.6  | 113.2823 |
| 1.92          | 0.2625 | 200  | 1.5276          | 16.93 | 32.19 | 81.4498  |
| 1.6661        | 0.3937 | 300  | 1.3907          | 16.26 | 35.75 | 99.1896  |
| 1.4712        | 0.5249 | 400  | 1.3126          | 24.55 | 42.56 | 77.8478  |
| 1.3404        | 0.6562 | 500  | 1.2960          | 23.94 | 42.25 | 77.3976  |
| 1.2106        | 0.7874 | 600  | 1.2556          | 23.82 | 43.46 | 73.5705  |
| 1.0312        | 0.9186 | 700  | 1.3002          | 23.73 | 43.09 | 74.6060  |
| 0.5265        | 1.0499 | 800  | 1.2993          | 28.09 | 45.57 | 69.1580  |
| 0.4802        | 1.1811 | 900  | 1.3466          | 25.21 | 43.38 | 75.7767  |
| 0.4415        | 1.3123 | 1000 | 1.3456          | 29.77 | 47.56 | 66.9968  |
| 0.4164        | 1.4436 | 1100 | 1.3373          | 27.92 | 45.54 | 70.9140  |
| 0.3937        | 1.5748 | 1200 | 1.3162          | 30.09 | 46.51 | 64.2053  |
| 0.3391        | 1.7060 | 1300 | 1.3424          | 24.82 | 45.35 | 72.9401  |
| 0.2969        | 1.8373 | 1400 | 1.3271          | 31.78 | 48.51 | 62.5394  |
| 0.2755        | 1.9685 | 1500 | 1.3523          | 31.6  | 48.33 | 61.3237  |
| 0.1059        | 2.0997 | 1600 | 1.3910          | 30.26 | 45.88 | 65.3309  |
| 0.0975        | 2.2310 | 1700 | 1.4255          | 30.28 | 46.1  | 64.1603  |
| 0.1047        | 2.3622 | 1800 | 1.3923          | 29.99 | 46.44 | 64.9257  |
| 0.0874        | 2.4934 | 1900 | 1.4111          | 30.14 | 47.09 | 65.1058  |
| 0.0838        | 2.6247 | 2000 | 1.4378          | 25.63 | 45.79 | 77.4426  |
| 0.0757        | 2.7559 | 2100 | 1.4356          | 29.28 | 47.5  | 65.0608  |
| 0.0749        | 2.8871 | 2200 | 1.4532          | 30.56 | 46.58 | 64.3854  |
| 0.0463        | 3.0184 | 2300 | 1.4324          | 32.69 | 49.04 | 62.6294  |
| 0.0265        | 3.1496 | 2400 | 1.4311          | 31.24 | 48.58 | 62.9896  |
| 0.0266        | 3.2808 | 2500 | 1.4409          | 31.97 | 47.99 | 62.4944  |
| 0.0237        | 3.4121 | 2600 | 1.4310          | 32.44 | 48.86 | 62.2692  |
| 0.0208        | 3.5433 | 2700 | 1.4483          | 31.3  | 47.49 | 63.5299  |
| 0.0185        | 3.6745 | 2800 | 1.4513          | 32.86 | 48.98 | 62.6294  |
| 0.0178        | 3.8058 | 2900 | 1.4583          | 31.77 | 48.91 | 63.0797  |
| 0.0194        | 3.9370 | 3000 | 1.4631          | 32.04 | 48.69 | 63.3949  |


### Framework versions

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