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: 31.78
      name: Bleu
    - type: wer
      value: 62.31427285006754
      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: 1.3732
- Bleu: 31.78
- Chrf: 47.41
- Wer: 62.3143

## 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 | Bleu  | Chrf  | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.4081        | 0.0438 | 100  | 9.25  | 24.92 | 1.8707          | 101.1706 |
| 1.9316        | 0.0876 | 200  | 13.88 | 32.67 | 1.5419          | 101.5308 |
| 1.5533        | 0.1313 | 300  | 17.52 | 36.68 | 1.4046          | 83.1607  |
| 1.3403        | 0.1751 | 400  | 17.71 | 37.97 | 1.3565          | 93.5615  |
| 1.1303        | 0.2189 | 500  | 19.24 | 40.02 | 1.3224          | 82.0801  |
| 0.9892        | 0.2627 | 600  | 26.07 | 43.16 | 1.3077          | 72.8050  |
| 0.9005        | 0.3065 | 700  | 27.37 | 44.24 | 1.2918          | 64.1603  |
| 0.7547        | 0.3503 | 800  | 27.68 | 44.28 | 1.2754          | 66.0964  |
| 0.7199        | 0.3940 | 900  | 23.99 | 42.99 | 1.2895          | 78.0729  |
| 0.6095        | 0.4378 | 1000 | 1.2716| 16.56 | 41.2            | 116.5691 |
| 0.5072        | 0.4816 | 1100 | 1.2901| 25.39 | 44.04           | 75.2364  |
| 0.4599        | 0.5254 | 1200 | 1.2634| 28.45 | 45.71           | 67.0419  |
| 0.3987        | 0.5692 | 1300 | 1.3004| 25.84 | 45.63           | 75.1013  |
| 0.3443        | 0.6130 | 1400 | 1.2871| 29.09 | 46.25           | 65.4210  |
| 0.2882        | 0.6567 | 1500 | 1.3242| 29.14 | 44.4            | 66.0063  |
| 0.2687        | 0.7005 | 1600 | 1.3135| 22.9  | 43.76           | 92.2557  |
| 0.2059        | 0.7443 | 1700 | 1.3160| 31.13 | 47.45           | 63.6650  |
| 0.1991        | 0.7881 | 1800 | 1.2960| 31.45 | 47.47           | 63.6650  |
| 0.1523        | 0.8319 | 1900 | 1.3215| 31.21 | 47.38           | 64.1153  |
| 0.1349        | 0.8757 | 2000 | 1.3402| 30.58 | 46.32           | 63.7551  |
| 0.111         | 0.9194 | 2100 | 1.3311| 30.92 | 48.17           | 62.2242  |
| 0.1055        | 0.9632 | 2200 | 1.3548| 30.56 | 46.56           | 65.2409  |
| 0.0525        | 1.0070 | 2300 | 1.3754| 31.28 | 48.1            | 64.2954  |
| 0.0498        | 1.0508 | 2400 | 1.3729| 31.16 | 47.8            | 61.7290  |
| 0.0372        | 1.0946 | 2500 | 1.3498| 32.13 | 48.77           | 61.4588  |
| 0.029         | 1.1384 | 2600 | 1.3723| 32.04 | 48.32           | 61.8640  |
| 0.0285        | 1.1821 | 2700 | 1.3748| 31.91 | 47.58           | 61.8640  |
| 0.0292        | 1.2259 | 2800 | 1.3764| 31.92 | 47.96           | 61.1887  |
| 0.025         | 1.2697 | 2900 | 1.3799| 31.64 | 47.47           | 62.2242  |
| 0.0253        | 1.3135 | 3000 | 1.3732| 31.78 | 47.41           | 62.3143  |


### Framework versions

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