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 dataset:ymoslem/EUbookshop-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
- ymoslem/EUbookshop-Speech-Irish
language:
- ga
- en
license: apache-2.0
metrics:
- bleu
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper Medium GA-EN Speech Translation
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - type: bleu
      value: 27.38
      name: Bleu
    - type: wer
      value: 72.17469608284557
      name: Wer
---

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# Whisper Medium 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, Wikimedia, and EUbookshop dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0491
- Bleu: 27.38
- Chrf: 51.97
- Wer: 72.1747

## 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      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.6534        | 0.0138 | 100  | 2.2446          | 1.43  | 15.99 | 269.1130 |
| 2.4519        | 0.0276 | 200  | 2.1941          | 2.13  | 18.36 | 250.5178 |
| 2.2928        | 0.0414 | 300  | 2.0086          | 7.14  | 25.95 | 128.3656 |
| 2.233         | 0.0552 | 400  | 2.0239          | 5.61  | 24.25 | 134.0837 |
| 2.0406        | 0.0690 | 500  | 1.9215          | 5.64  | 25.65 | 183.8361 |
| 2.0273        | 0.0828 | 600  | 1.8556          | 13.41 | 30.96 | 83.7010  |
| 1.895         | 0.0966 | 700  | 1.8278          | 7.02  | 26.82 | 158.2170 |
| 1.9889        | 0.1103 | 800  | 1.7842          | 12.22 | 31.62 | 99.6398  |
| 1.8484        | 0.1241 | 900  | 1.7648          | 10.97 | 30.45 | 91.1751  |
| 1.7491        | 0.1379 | 1000 | 1.7498          | 10.0  | 29.42 | 109.0050 |
| 1.699         | 0.1517 | 1100 | 1.6662          | 12.53 | 34.87 | 109.9054 |
| 1.6959        | 0.1655 | 1200 | 1.6287          | 14.54 | 34.8  | 92.3008  |
| 1.6682        | 0.1793 | 1300 | 1.5800          | 13.26 | 33.5  | 103.0617 |
| 1.6625        | 0.1931 | 1400 | 1.6115          | 19.71 | 37.33 | 75.9118  |
| 1.5462        | 0.2069 | 1500 | 1.4993          | 18.3  | 39.49 | 93.7866  |
| 1.3834        | 0.2207 | 1600 | 1.4906          | 20.32 | 40.87 | 79.2436  |
| 1.39          | 0.2345 | 1700 | 1.4752          | 17.3  | 38.16 | 93.1562  |
| 1.5061        | 0.2483 | 1800 | 1.4004          | 20.11 | 39.69 | 81.0446  |
| 1.4125        | 0.2621 | 1900 | 1.3854          | 23.82 | 42.67 | 73.3904  |
| 1.3181        | 0.2759 | 2000 | 1.3979          | 20.57 | 40.87 | 78.8384  |
| 1.283         | 0.2897 | 2100 | 1.3446          | 17.97 | 40.47 | 88.8789  |
| 1.2061        | 0.3034 | 2200 | 1.3130          | 25.12 | 45.42 | 73.5254  |
| 1.2091        | 0.3172 | 2300 | 1.3274          | 22.12 | 43.56 | 79.8739  |
| 1.1264        | 0.3310 | 2400 | 1.2771          | 22.94 | 45.96 | 78.2080  |
| 1.0972        | 0.3448 | 2500 | 1.2858          | 24.38 | 46.04 | 75.4615  |
| 1.0822        | 0.3586 | 2600 | 1.2376          | 27.39 | 48.34 | 67.6722  |
| 1.0316        | 0.3724 | 2700 | 1.2461          | 28.0  | 47.61 | 68.5277  |
| 1.165         | 0.3862 | 2800 | 1.1869          | 26.05 | 48.13 | 71.6794  |
| 1.025         | 0.4    | 2900 | 1.1716          | 27.14 | 47.91 | 68.7528  |
| 0.8978        | 0.4138 | 3000 | 1.1628          | 28.34 | 49.15 | 65.6461  |
| 0.9146        | 0.4276 | 3100 | 1.1703          | 25.81 | 48.42 | 71.7244  |
| 0.9764        | 0.4414 | 3200 | 1.1526          | 29.63 | 51.22 | 67.3570  |
| 0.9455        | 0.4552 | 3300 | 1.1108          | 25.31 | 49.73 | 72.6249  |
| 0.9073        | 0.4690 | 3400 | 1.1085          | 27.7  | 50.85 | 72.7150  |
| 0.8596        | 0.4828 | 3500 | 1.0927          | 28.34 | 52.39 | 67.9424  |
| 0.8241        | 0.4966 | 3600 | 1.1026          | 29.95 | 51.37 | 65.2859  |
| 0.8436        | 0.5103 | 3700 | 1.0718          | 27.18 | 51.45 | 71.2292  |
| 0.8318        | 0.5241 | 3800 | 1.0678          | 30.71 | 53.35 | 64.3404  |
| 0.8262        | 0.5379 | 3900 | 1.0534          | 27.05 | 51.94 | 71.5894  |
| 0.8129        | 0.5517 | 4000 | 1.0491          | 27.38 | 51.97 | 72.1747  |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
