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: 32.44
      name: Bleu
    - type: wer
      value: 63.259792886087354
      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: 1.3690
- Bleu: 32.44
- Chrf: 48.06
- Wer: 63.2598

## 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: 0.01
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bleu  | Chrf  | Wer      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.3655        | 0.0438 | 100  | 1.7709          | 8.84  | 26.21 | 127.7803 |
| 1.8998        | 0.0876 | 200  | 1.5198          | 14.9  | 32.89 | 99.2796  |
| 1.5421        | 0.1313 | 300  | 1.3972          | 16.15 | 35.77 | 86.8077  |
| 1.3154        | 0.1751 | 400  | 1.3412          | 20.46 | 39.48 | 83.0707  |
| 1.1138        | 0.2189 | 500  | 1.3126          | 23.16 | 41.28 | 74.1108  |
| 0.9814        | 0.2627 | 600  | 1.3217          | 25.56 | 41.67 | 68.7528  |
| 0.8897        | 0.3065 | 700  | 1.2859          | 27.0  | 43.54 | 66.3215  |
| 0.7495        | 0.3503 | 800  | 1.2668          | 21.71 | 43.03 | 75.7767  |
| 0.7068        | 0.3940 | 900  | 1.2852          | 17.86 | 40.88 | 106.0333 |
| 0.6002        | 0.4378 | 1000 | 1.2476          | 24.0  | 44.26 | 78.4331  |
| 0.4989        | 0.4816 | 1100 | 1.2756          | 28.88 | 45.57 | 67.2670  |
| 0.4464        | 0.5254 | 1200 | 1.2756          | 27.81 | 45.53 | 66.8618  |
| 0.3883        | 0.5692 | 1300 | 1.2799          | 29.84 | 46.03 | 64.0702  |
| 0.341         | 0.6130 | 1400 | 1.2693          | 26.51 | 43.97 | 75.3715  |
| 0.2853        | 0.6567 | 1500 | 1.3310          | 26.99 | 45.58 | 74.0207  |
| 0.2611        | 0.7005 | 1600 | 1.3022          | 25.83 | 44.79 | 73.4354  |
| 0.2013        | 0.7443 | 1700 | 1.3266          | 30.78 | 46.61 | 63.6650  |
| 0.1886        | 0.7881 | 1800 | 1.2943          | 25.56 | 45.46 | 73.7055  |
| 0.1517        | 0.8319 | 1900 | 1.3193          | 28.93 | 45.09 | 64.3854  |
| 0.1288        | 0.8757 | 2000 | 1.3567          | 28.22 | 44.75 | 67.6722  |
| 0.1129        | 0.9194 | 2100 | 1.3431          | 29.55 | 46.22 | 66.2314  |
| 0.1           | 0.9632 | 2200 | 1.3365          | 31.46 | 48.14 | 64.9257  |
| 0.0505        | 1.0070 | 2300 | 1.3557          | 30.37 | 47.16 | 64.1153  |
| 0.0468        | 1.0508 | 2400 | 1.3648          | 31.57 | 48.17 | 62.0891  |
| 0.0373        | 1.0946 | 2500 | 1.3661          | 31.56 | 47.76 | 64.7456  |
| 0.0297        | 1.1384 | 2600 | 1.3638          | 31.13 | 47.74 | 64.3854  |
| 0.0283        | 1.1821 | 2700 | 1.3847          | 29.98 | 47.54 | 65.9613  |
| 0.0302        | 1.2259 | 2800 | 1.3730          | 32.32 | 48.28 | 64.0252  |
| 0.0229        | 1.2697 | 2900 | 1.3702          | 31.47 | 47.55 | 65.1508  |
| 0.0262        | 1.3135 | 3000 | 1.3690          | 32.44 | 48.06 | 63.2598  |


### Framework versions

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