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-medium base_model:finetune:openai/whisper-medium license:apache-2.0 model-index endpoints_compatible region:us
---
base_model: openai/whisper-medium
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: 32.0
      name: Bleu
    - type: wer
      value: 66.77172444844665
      name: Wer
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Whisper Medium GA-EN Speech Translation

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1067
- Bleu: 32.0
- Chrf: 52.48
- Wer: 66.7717

## 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      |
|:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.5219        | 0.0138 | 100  | 0.44  | 10.48 | 2.1106          | 107.2490 |
| 2.4608        | 0.0276 | 200  | 3.3   | 20.43 | 2.1816          | 179.1535 |
| 2.3008        | 0.0414 | 300  | 3.66  | 21.59 | 2.0587          | 206.4836 |
| 2.2095        | 0.0552 | 400  | 8.79  | 27.66 | 1.9459          | 100.3602 |
| 2.0454        | 0.0690 | 500  | 8.14  | 27.36 | 1.8681          | 122.1522 |
| 1.9937        | 0.0828 | 600  | 11.05 | 30.26 | 1.8717          | 97.2535  |
| 1.868         | 0.0966 | 700  | 9.14  | 29.03 | 1.7917          | 129.0410 |
| 1.9924        | 0.1103 | 800  | 12.62 | 33.2  | 1.7170          | 89.6443  |
| 1.8646        | 0.1241 | 900  | 11.98 | 30.77 | 1.7252          | 97.8838  |
| 1.7644        | 0.1379 | 1000 | 10.87 | 31.0  | 1.6832          | 109.1851 |
| 1.692         | 0.1517 | 1100 | 13.05 | 34.46 | 1.6837          | 93.3814  |
| 1.7044        | 0.1655 | 1200 | 20.95 | 37.42 | 1.5527          | 75.2364  |
| 1.6824        | 0.1793 | 1300 | 14.91 | 35.56 | 1.5611          | 92.6159  |
| 1.6557        | 0.1931 | 1400 | 14.0  | 36.54 | 1.5554          | 99.8199  |
| 1.5456        | 0.2069 | 1500 | 19.72 | 39.81 | 1.5058          | 83.5660  |
| 1.3755        | 0.2207 | 1600 | 18.04 | 37.95 | 1.5039          | 82.9806  |
| 1.3959        | 0.2345 | 1700 | 17.01 | 39.5  | 1.4374          | 85.2319  |
| 1.5012        | 0.2483 | 1800 | 14.93 | 39.24 | 1.4242          | 114.4079 |
| 1.4278        | 0.2621 | 1900 | 23.85 | 42.69 | 1.3904          | 73.0302  |
| 1.3285        | 0.2759 | 2000 | 17.7  | 37.23 | 1.4493          | 83.8811  |
| 1.2655        | 0.2897 | 2100 | 20.1  | 40.32 | 1.3661          | 79.7839  |
| 1.2074        | 0.3034 | 2200 | 24.45 | 43.79 | 1.3387          | 72.9851  |
| 1.1893        | 0.3172 | 2300 | 21.45 | 42.61 | 1.3308          | 82.3953  |
| 1.1236        | 0.3310 | 2400 | 22.77 | 44.17 | 1.3050          | 77.3075  |
| 1.0934        | 0.3448 | 2500 | 25.54 | 46.32 | 1.2793          | 72.2647  |
| 1.06          | 0.3586 | 2600 | 28.27 | 47.32 | 1.2396          | 65.6911  |
| 1.0327        | 0.3724 | 2700 | 28.45 | 47.01 | 1.2577          | 67.3570  |
| 1.1623        | 0.3862 | 2800 | 24.54 | 47.43 | 1.2194          | 73.6155  |
| 1.0215        | 0.4    | 2900 | 27.4  | 49.6  | 1.2039          | 69.2481  |
| 0.9185        | 0.4138 | 3000 | 27.04 | 49.24 | 1.1724          | 67.8973  |
| 0.9003        | 0.4276 | 3100 | 31.08 | 50.11 | 1.1674          | 63.8001  |
| 0.9839        | 0.4414 | 3200 | 30.24 | 50.63 | 1.1580          | 64.5655  |
| 0.9396        | 0.4552 | 3300 | 30.79 | 51.72 | 1.1202          | 64.9257  |
| 0.9051        | 0.4690 | 3400 | 30.34 | 53.08 | 1.1180          | 66.4566  |
| 0.8621        | 0.4828 | 3500 | 33.3  | 53.86 | 1.1042          | 60.7834  |
| 0.8236        | 0.4966 | 3600 | 32.77 | 53.21 | 1.1070          | 62.0441  |
| 0.829         | 0.5103 | 3700 | 32.49 | 54.21 | 1.0771          | 62.5844  |
| 0.8375        | 0.5241 | 3800 | 32.27 | 53.98 | 1.0780          | 63.0797  |
| 0.8206        | 0.5379 | 3900 | 33.26 | 55.07 | 1.0615          | 61.6389  |
| 0.8059        | 0.5517 | 4000 | 33.24 | 55.16 | 1.0552          | 61.5038  |
| 0.9133        | 0.5655 | 4100 | 1.2218| 29.38 | 49.22           | 66.0964  |
| 1.051         | 0.5793 | 4200 | 1.2304| 25.12 | 46.01           | 71.8145  |
| 0.954         | 0.5931 | 4300 | 1.2501| 25.47 | 45.88           | 75.3715  |
| 0.939         | 0.6069 | 4400 | 1.2204| 29.19 | 47.63           | 66.9068  |
| 0.9887        | 0.6207 | 4500 | 1.2099| 27.99 | 47.01           | 67.7172  |
| 1.0044        | 0.6345 | 4600 | 1.2080| 23.77 | 45.33           | 73.3904  |
| 0.9881        | 0.6483 | 4700 | 1.2188| 26.46 | 47.36           | 68.5277  |
| 0.9674        | 0.6621 | 4800 | 1.2296| 26.11 | 45.92           | 68.3026  |
| 0.8845        | 0.6759 | 4900 | 1.2347| 27.3  | 46.08           | 68.0324  |
| 0.8297        | 0.6897 | 5000 | 1.2108| 29.48 | 48.96           | 64.6105  |
| 0.9065        | 0.7034 | 5100 | 1.1873| 29.81 | 49.94           | 64.2503  |
| 0.8096        | 0.7172 | 5200 | 1.2122| 28.5  | 46.93           | 66.2314  |
| 0.8077        | 0.7310 | 5300 | 1.1945| 29.26 | 48.21           | 64.4755  |
| 0.8227        | 0.7448 | 5400 | 1.2310| 26.82 | 48.43           | 71.4093  |
| 0.7587        | 0.7586 | 5500 | 1.2067| 29.45 | 49.03           | 65.3309  |
| 0.7206        | 0.7724 | 5600 | 1.2114| 29.89 | 49.33           | 65.5561  |
| 0.8088        | 0.7862 | 5700 | 1.1689| 31.88 | 51.4            | 64.2954  |
| 0.693         | 0.8    | 5800 | 1.1644| 27.23 | 48.11           | 68.7078  |
| 0.7099        | 0.8138 | 5900 | 1.1852| 31.01 | 49.42           | 63.3949  |
| 0.7564        | 0.8276 | 6000 | 1.1554| 28.3  | 50.34           | 71.0941  |
| 0.584         | 0.8414 | 6100 | 1.1566| 34.79 | 51.69           | 59.0725  |
| 0.6817        | 0.8552 | 6200 | 1.1245| 34.08 | 51.95           | 59.8829  |
| 0.5968        | 0.8690 | 6300 | 1.1475| 32.4  | 51.59           | 62.9896  |
| 0.6092        | 0.8828 | 6400 | 1.1250| 32.83 | 52.82           | 62.5844  |
| 0.6325        | 0.8966 | 6500 | 1.1108| 29.29 | 51.68           | 69.1130  |
| 0.6002        | 0.9103 | 6600 | 1.0993| 27.64 | 52.7            | 71.0941  |
| 0.6247        | 0.9241 | 6700 | 1.0898| 28.39 | 52.4            | 68.3026  |
| 0.6257        | 0.9379 | 6800 | 1.0863| 28.54 | 52.33           | 70.9140  |
| 0.6719        | 0.9517 | 6900 | 1.0891| 31.43 | 53.53           | 66.1414  |
| 0.4994        | 0.9655 | 7000 | 1.1066| 33.81 | 52.77           | 61.0986  |
| 0.5469        | 0.9793 | 7100 | 1.0891| 30.52 | 53.13           | 67.3570  |
| 0.6031        | 0.9931 | 7200 | 1.0933| 33.16 | 54.03           | 62.1792  |
| 0.2469        | 1.0069 | 7300 | 1.1426| 33.76 | 52.38           | 62.8546  |
| 0.2572        | 1.0207 | 7400 | 1.1292| 33.16 | 51.71           | 64.8807  |
| 0.2762        | 1.0345 | 7500 | 1.1090| 34.76 | 54.28           | 60.7384  |
| 0.2332        | 1.0483 | 7600 | 1.1073| 30.95 | 52.28           | 66.1864  |
| 0.2069        | 1.0621 | 7700 | 1.0999| 32.39 | 53.08           | 65.5561  |
| 0.2417        | 1.0759 | 7800 | 1.1008| 31.3  | 53.87           | 65.1058  |
| 0.2403        | 1.0897 | 7900 | 1.1053| 32.18 | 53.3            | 66.4566  |
| 0.208         | 1.1034 | 8000 | 1.1067| 32.0  | 52.48           | 66.7717  |


### Framework versions

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



## Citation

```
@inproceedings{moslem-2024-leveraging,
    title = "Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation",
    author = "Moslem, Yasmin",
    booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand (in-person and online)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.iwslt-1.31/",
    doi = "10.18653/v1/2024.iwslt-1.31",
    pages = "265--273",
    abstract = "This paper describes our system submission to the International Conference on Spoken Language Translation (IWSLT 2024) for Irish-to-English speech translation. We built end-to-end systems based on Whisper, and employed a number of data augmentation techniques, such as speech back-translation and noise augmentation. We investigate the effect of using synthetic audio data and discuss several methods for enriching signal diversity."
}
```