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: 33.24
      name: Bleu
    - type: wer
      value: 61.50382710490771
      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: 1.0552
- Bleu: 33.24
- Chrf: 55.16
- Wer: 61.5038

## 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bleu  | Chrf  | Wer      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.5219        | 0.0138 | 100  | 2.1106          | 0.44  | 10.48 | 107.2490 |
| 2.4608        | 0.0276 | 200  | 2.1816          | 3.3   | 20.43 | 179.1535 |
| 2.3008        | 0.0414 | 300  | 2.0587          | 3.66  | 21.59 | 206.4836 |
| 2.2095        | 0.0552 | 400  | 1.9459          | 8.79  | 27.66 | 100.3602 |
| 2.0454        | 0.0690 | 500  | 1.8681          | 8.14  | 27.36 | 122.1522 |
| 1.9937        | 0.0828 | 600  | 1.8717          | 11.05 | 30.26 | 97.2535  |
| 1.868         | 0.0966 | 700  | 1.7917          | 9.14  | 29.03 | 129.0410 |
| 1.9924        | 0.1103 | 800  | 1.7170          | 12.62 | 33.2  | 89.6443  |
| 1.8646        | 0.1241 | 900  | 1.7252          | 11.98 | 30.77 | 97.8838  |
| 1.7644        | 0.1379 | 1000 | 1.6832          | 10.87 | 31.0  | 109.1851 |
| 1.692         | 0.1517 | 1100 | 1.6837          | 13.05 | 34.46 | 93.3814  |
| 1.7044        | 0.1655 | 1200 | 1.5527          | 20.95 | 37.42 | 75.2364  |
| 1.6824        | 0.1793 | 1300 | 1.5611          | 14.91 | 35.56 | 92.6159  |
| 1.6557        | 0.1931 | 1400 | 1.5554          | 14.0  | 36.54 | 99.8199  |
| 1.5456        | 0.2069 | 1500 | 1.5058          | 19.72 | 39.81 | 83.5660  |
| 1.3755        | 0.2207 | 1600 | 1.5039          | 18.04 | 37.95 | 82.9806  |
| 1.3959        | 0.2345 | 1700 | 1.4374          | 17.01 | 39.5  | 85.2319  |
| 1.5012        | 0.2483 | 1800 | 1.4242          | 14.93 | 39.24 | 114.4079 |
| 1.4278        | 0.2621 | 1900 | 1.3904          | 23.85 | 42.69 | 73.0302  |
| 1.3285        | 0.2759 | 2000 | 1.4493          | 17.7  | 37.23 | 83.8811  |
| 1.2655        | 0.2897 | 2100 | 1.3661          | 20.1  | 40.32 | 79.7839  |
| 1.2074        | 0.3034 | 2200 | 1.3387          | 24.45 | 43.79 | 72.9851  |
| 1.1893        | 0.3172 | 2300 | 1.3308          | 21.45 | 42.61 | 82.3953  |
| 1.1236        | 0.3310 | 2400 | 1.3050          | 22.77 | 44.17 | 77.3075  |
| 1.0934        | 0.3448 | 2500 | 1.2793          | 25.54 | 46.32 | 72.2647  |
| 1.06          | 0.3586 | 2600 | 1.2396          | 28.27 | 47.32 | 65.6911  |
| 1.0327        | 0.3724 | 2700 | 1.2577          | 28.45 | 47.01 | 67.3570  |
| 1.1623        | 0.3862 | 2800 | 1.2194          | 24.54 | 47.43 | 73.6155  |
| 1.0215        | 0.4    | 2900 | 1.2039          | 27.4  | 49.6  | 69.2481  |
| 0.9185        | 0.4138 | 3000 | 1.1724          | 27.04 | 49.24 | 67.8973  |
| 0.9003        | 0.4276 | 3100 | 1.1674          | 31.08 | 50.11 | 63.8001  |
| 0.9839        | 0.4414 | 3200 | 1.1580          | 30.24 | 50.63 | 64.5655  |
| 0.9396        | 0.4552 | 3300 | 1.1202          | 30.79 | 51.72 | 64.9257  |
| 0.9051        | 0.4690 | 3400 | 1.1180          | 30.34 | 53.08 | 66.4566  |
| 0.8621        | 0.4828 | 3500 | 1.1042          | 33.3  | 53.86 | 60.7834  |
| 0.8236        | 0.4966 | 3600 | 1.1070          | 32.77 | 53.21 | 62.0441  |
| 0.829         | 0.5103 | 3700 | 1.0771          | 32.49 | 54.21 | 62.5844  |
| 0.8375        | 0.5241 | 3800 | 1.0780          | 32.27 | 53.98 | 63.0797  |
| 0.8206        | 0.5379 | 3900 | 1.0615          | 33.26 | 55.07 | 61.6389  |
| 0.8059        | 0.5517 | 4000 | 1.0552          | 33.24 | 55.16 | 61.5038  |


### 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."
}
```
