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: 30.86
      name: Bleu
    - type: wer
      value: 67.04187303016658
      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: 1.0885
- Bleu: 30.86
- Chrf: 54.11
- Wer: 67.0419

## 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: 0.03
- training_steps: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Bleu  | Chrf  | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.5374        | 0.0138 | 100  | 2.56  | 18.92 | 2.1201          | 222.4674 |
| 2.446         | 0.0276 | 200  | 3.07  | 20.56 | 2.1960          | 170.5088 |
| 2.2819        | 0.0414 | 300  | 5.87  | 25.17 | 1.9811          | 114.5880 |
| 2.1904        | 0.0552 | 400  | 8.41  | 25.65 | 1.9974          | 99.1896  |
| 2.026         | 0.0690 | 500  | 7.99  | 27.64 | 1.8961          | 130.7069 |
| 2.0448        | 0.0828 | 600  | 9.15  | 27.78 | 1.9410          | 104.9077 |
| 1.8606        | 0.0966 | 700  | 9.57  | 29.34 | 1.8451          | 110.4908 |
| 1.9887        | 0.1103 | 800  | 13.44 | 32.32 | 1.7419          | 84.3314  |
| 1.8633        | 0.1241 | 900  | 13.43 | 31.58 | 1.7376          | 102.1162 |
| 1.7576        | 0.1379 | 1000 | 11.9  | 32.68 | 1.6879          | 106.6186 |
| 1.7142        | 0.1517 | 1100 | 12.4  | 33.66 | 1.7571          | 102.6114 |
| 1.7168        | 0.1655 | 1200 | 17.35 | 36.55 | 1.6003          | 87.9784  |
| 1.6741        | 0.1793 | 1300 | 15.41 | 35.46 | 1.5883          | 92.8411  |
| 1.6534        | 0.1931 | 1400 | 17.12 | 37.24 | 1.5366          | 90.2296  |
| 1.58          | 0.2069 | 1500 | 17.49 | 38.5  | 1.5141          | 92.1207  |
| 1.403         | 0.2207 | 1600 | 16.78 | 39.13 | 1.4606          | 88.9689  |
| 1.3806        | 0.2345 | 1700 | 19.26 | 40.02 | 1.4263          | 86.7177  |
| 1.5111        | 0.2483 | 1800 | 18.4  | 39.47 | 1.4060          | 92.2557  |
| 1.4261        | 0.2621 | 1900 | 21.19 | 42.13 | 1.3911          | 78.7033  |
| 1.2974        | 0.2759 | 2000 | 15.6  | 38.66 | 1.3871          | 100.3152 |
| 1.2694        | 0.2897 | 2100 | 16.21 | 39.99 | 1.3527          | 91.2652  |
| 1.204         | 0.3034 | 2200 | 20.2  | 41.18 | 1.3232          | 86.8978  |
| 1.1922        | 0.3172 | 2300 | 16.44 | 40.85 | 1.3338          | 103.1968 |
| 1.1237        | 0.3310 | 2400 | 19.29 | 43.73 | 1.2830          | 94.4620  |
| 1.0989        | 0.3448 | 2500 | 25.11 | 46.84 | 1.2844          | 75.0563  |
| 1.0766        | 0.3586 | 2600 | 23.87 | 46.1  | 1.2578          | 74.5160  |
| 1.0432        | 0.3724 | 2700 | 22.31 | 44.91 | 1.2414          | 86.9878  |
| 1.1588        | 0.3862 | 2800 | 23.32 | 45.94 | 1.2051          | 77.1724  |
| 1.0062        | 0.4    | 2900 | 26.15 | 48.27 | 1.2059          | 69.4282  |
| 0.9178        | 0.4138 | 3000 | 29.13 | 48.92 | 1.1756          | 64.7456  |
| 0.9108        | 0.4276 | 3100 | 28.34 | 48.9  | 1.1665          | 67.2220  |
| 0.9868        | 0.4414 | 3200 | 25.64 | 48.93 | 1.1489          | 75.3264  |
| 0.9563        | 0.4552 | 3300 | 27.58 | 49.67 | 1.1181          | 71.8145  |
| 0.9138        | 0.4690 | 3400 | 28.37 | 50.96 | 1.1247          | 71.4543  |
| 0.8508        | 0.4828 | 3500 | 29.75 | 51.41 | 1.1007          | 68.3476  |
| 0.836         | 0.4966 | 3600 | 30.99 | 52.2  | 1.1114          | 66.5916  |
| 0.8435        | 0.5103 | 3700 | 30.64 | 52.77 | 1.0782          | 68.2125  |
| 0.8323        | 0.5241 | 3800 | 29.78 | 52.94 | 1.0744          | 68.9779  |
| 0.818         | 0.5379 | 3900 | 31.23 | 53.21 | 1.0639          | 67.7623  |
| 0.8095        | 0.5517 | 4000 | 31.02 | 53.51 | 1.0576          | 68.5277  |
| 0.922         | 0.5655 | 4100 | 1.2445| 25.47 | 46.16           | 74.2909  |
| 1.0387        | 0.5793 | 4200 | 1.2634| 25.44 | 46.19           | 71.0491  |
| 0.9386        | 0.5931 | 4300 | 1.2457| 22.36 | 45.4            | 76.8122  |
| 0.9297        | 0.6069 | 4400 | 1.2502| 28.65 | 46.48           | 65.7362  |
| 0.9837        | 0.6207 | 4500 | 1.2503| 26.81 | 46.53           | 68.9779  |
| 1.0226        | 0.6345 | 4600 | 1.2282| 19.37 | 44.1            | 86.4926  |
| 0.9896        | 0.6483 | 4700 | 1.2568| 26.06 | 46.46           | 70.8240  |
| 0.9805        | 0.6621 | 4800 | 1.2364| 19.29 | 42.56           | 82.0351  |
| 0.8982        | 0.6759 | 4900 | 1.2346| 28.58 | 47.84           | 64.6556  |
| 0.8303        | 0.6897 | 5000 | 1.2136| 27.25 | 48.15           | 68.3476  |
| 0.905         | 0.7034 | 5100 | 1.1808| 27.99 | 50.31           | 67.2220  |
| 0.8125        | 0.7172 | 5200 | 1.1971| 28.91 | 47.63           | 65.4660  |
| 0.7965        | 0.7310 | 5300 | 1.1789| 25.96 | 47.21           | 69.5633  |
| 0.8244        | 0.7448 | 5400 | 1.2237| 28.65 | 48.63           | 66.6367  |
| 0.7637        | 0.7586 | 5500 | 1.1765| 30.4  | 50.24           | 66.6817  |
| 0.7333        | 0.7724 | 5600 | 1.1295| 29.94 | 51.34           | 68.8879  |
| 0.8141        | 0.7862 | 5700 | 1.1238| 27.51 | 50.61           | 74.7861  |
| 0.6969        | 0.8    | 5800 | 1.1350| 23.95 | 48.76           | 87.6632  |
| 0.7162        | 0.8138 | 5900 | 1.1493| 26.34 | 48.65           | 74.0207  |
| 0.7421        | 0.8276 | 6000 | 1.0976| 28.69 | 52.23           | 68.5727  |
| 0.593         | 0.8414 | 6100 | 1.1163| 34.96 | 53.13           | 59.3426  |
| 0.678         | 0.8552 | 6200 | 1.1072| 34.14 | 53.2            | 61.6839  |
| 0.6018        | 0.8690 | 6300 | 1.0959| 31.8  | 53.33           | 64.1153  |
| 0.6038        | 0.8828 | 6400 | 1.0959| 24.77 | 50.61           | 84.2413  |
| 0.6174        | 0.8966 | 6500 | 1.0891| 25.48 | 50.6            | 81.6749  |
| 0.595         | 0.9103 | 6600 | 1.1037| 23.83 | 48.07           | 83.3859  |
| 0.6114        | 0.9241 | 6700 | 1.0723| 28.03 | 52.18           | 70.7789  |
| 0.6257        | 0.9379 | 6800 | 1.0797| 33.13 | 52.95           | 61.5038  |
| 0.6689        | 0.9517 | 6900 | 1.0803| 30.53 | 52.41           | 68.4376  |
| 0.4908        | 0.9655 | 7000 | 1.0901| 30.1  | 51.71           | 69.1130  |
| 0.5439        | 0.9793 | 7100 | 1.0672| 25.81 | 49.36           | 76.5871  |
| 0.5994        | 0.9931 | 7200 | 1.0705| 31.56 | 52.51           | 66.1414  |
| 0.2451        | 1.0069 | 7300 | 1.1069| 33.0  | 53.29           | 64.7006  |
| 0.2609        | 1.0207 | 7400 | 1.0877| 31.68 | 54.3            | 64.9257  |
| 0.2813        | 1.0345 | 7500 | 1.0910| 34.93 | 54.74           | 60.1531  |
| 0.2367        | 1.0483 | 7600 | 1.0999| 30.87 | 53.09           | 65.9163  |
| 0.2018        | 1.0621 | 7700 | 1.0917| 35.53 | 54.42           | 58.7573  |
| 0.2407        | 1.0759 | 7800 | 1.0859| 34.38 | 54.5            | 60.9185  |
| 0.2385        | 1.0897 | 7900 | 1.0866| 31.27 | 54.12           | 65.3309  |
| 0.2074        | 1.1034 | 8000 | 1.0885| 30.86 | 54.11           | 67.0419  |


### Framework versions

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