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
- chrf
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 + augmented
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - type: bleu
      value: 28.44
      name: Bleu
    - type: wer
      value: 72.62494371904548
      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 + augmented dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3641
- Bleu: 28.44
- Chrf: 43.55
- Wer: 72.6249

## 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
- warmup_steps: 0
- training_steps: 3000
- mixed_precision_training: Native AMP
- generation_max_length: 128

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bleu  | Chrf  | Wer      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.3595        | 0.0438 | 100  | 1.7944          | 9.69  | 26.37 | 114.4529 |
| 1.9008        | 0.0876 | 200  | 1.5391          | 14.89 | 32.44 | 93.6065  |
| 1.535         | 0.1313 | 300  | 1.3972          | 18.24 | 33.57 | 81.9901  |
| 1.3307        | 0.1751 | 400  | 1.3684          | 21.34 | 37.37 | 72.8050  |
| 1.1263        | 0.2189 | 500  | 1.3284          | 19.33 | 39.83 | 91.8955  |
| 0.9805        | 0.2627 | 600  | 1.3301          | 23.67 | 38.68 | 78.3881  |
| 0.8989        | 0.3065 | 700  | 1.3123          | 20.32 | 36.94 | 76.3170  |
| 0.7557        | 0.3503 | 800  | 1.2717          | 25.74 | 40.16 | 72.4448  |
| 0.7216        | 0.3940 | 900  | 1.3090          | 22.34 | 37.79 | 78.9284  |
| 0.6131        | 0.4378 | 1000 | 1.2566          | 24.36 | 41.49 | 74.5160  |
| 0.5032        | 0.4816 | 1100 | 1.2742          | 21.69 | 41.12 | 83.3859  |
| 0.4567        | 0.5254 | 1200 | 1.2893          | 24.33 | 40.05 | 70.8690  |
| 0.3968        | 0.5692 | 1300 | 1.3000          | 26.97 | 41.45 | 69.6083  |
| 0.3353        | 0.6130 | 1400 | 1.2784          | 27.51 | 43.97 | 63.9352  |
| 0.2826        | 0.6567 | 1500 | 1.3165          | 24.36 | 39.83 | 70.6439  |
| 0.2643        | 0.7005 | 1600 | 1.3317          | 24.98 | 40.01 | 68.6628  |
| 0.2047        | 0.7443 | 1700 | 1.2905          | 28.01 | 42.72 | 65.8262  |
| 0.1946        | 0.7881 | 1800 | 1.2820          | 26.17 | 42.46 | 64.9257  |
| 0.1588        | 0.8319 | 1900 | 1.3172          | 26.9  | 43.02 | 63.5299  |
| 0.1322        | 0.8757 | 2000 | 1.3248          | 27.78 | 43.53 | 63.8001  |
| 0.1134        | 0.9194 | **2100** | 1.3198          | 28.98 | 45.27 | 72.7600  |
| 0.1031        | 0.9632 | 2200 | 1.3502          | 29.18 | 44.77 | 68.3476  |
| 0.0518        | 1.0070 | 2300 | 1.3433          | 28.6  | 42.96 | 69.0230  |
| 0.0481        | 1.0508 | 2400 | 1.3715          | 29.01 | 44.46 | 69.6983  |
| 0.0367        | 1.0946 | 2500 | 1.3696          | 26.94 | 42.39 | 73.6605  |
| 0.0309        | 1.1384 | 2600 | 1.3665          | 28.12 | 43.32 | 70.3737  |
| 0.0302        | 1.1821 | 2700 | 1.3836          | 29.6  | 44.56 | 67.2220  |
| 0.0302        | 1.2259 | 2800 | 1.3667          | 29.0  | 44.33 | 67.2220  |
| 0.0252        | 1.2697 | 2900 | 1.3633          | 29.07 | 44.09 | 70.6889  |
| 0.0257        | 1.3135 | 3000 | 1.3641          | 28.44 | 43.55 | 72.6249  |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1