---
language:
- ga
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
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
metrics:
- bleu
- wer
model-index:
- name: Whisper Medium GA-EN Speech Translation
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - name: Bleu
      type: bleu
      value: 30.79
    - name: Wer
      type: wer
      value: 66.59162539396668
---

<!-- 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-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: 1.1869
- Bleu: 30.79
- Chrf: 52.18
- Wer: 66.5916

## 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      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.7614        | 0.0328 | 100  | 2.1538          | 3.55  | 19.32 | 146.1954 |
| 2.6486        | 0.0657 | 200  | 1.9662          | 7.4   | 25.58 | 132.4178 |
| 2.5302        | 0.0985 | 300  | 1.8689          | 6.8   | 24.76 | 172.0846 |
| 2.4506        | 0.1314 | 400  | 1.8244          | 13.88 | 32.7  | 95.9478  |
| 2.3631        | 0.1642 | 500  | 1.6759          | 14.35 | 32.54 | 95.6776  |
| 2.173         | 0.1970 | 600  | 1.7051          | 13.14 | 34.29 | 93.2463  |
| 2.3489        | 0.2299 | 700  | 1.6077          | 15.85 | 36.52 | 87.7082  |
| 2.0183        | 0.2627 | 800  | 1.5894          | 15.72 | 36.54 | 94.1018  |
| 2.1502        | 0.2956 | 900  | 1.5739          | 16.58 | 36.4  | 96.7132  |
| 2.016         | 0.3284 | 1000 | 1.5470          | 18.99 | 39.22 | 83.9712  |
| 1.747         | 0.3612 | 1100 | 1.5428          | 15.49 | 38.02 | 101.5759 |
| 1.6728        | 0.3941 | 1200 | 1.5129          | 19.45 | 39.24 | 89.4642  |
| 1.6476        | 0.4269 | 1300 | 1.4747          | 21.77 | 40.53 | 82.8906  |
| 1.6764        | 0.4598 | 1400 | 1.4672          | 16.58 | 39.94 | 95.0923  |
| 1.5683        | 0.4926 | 1500 | 1.4116          | 22.0  | 42.55 | 77.8028  |
| 1.3607        | 0.5255 | 1600 | 1.4290          | 24.15 | 43.36 | 74.1108  |
| 1.4888        | 0.5583 | 1700 | 1.3684          | 22.61 | 42.39 | 83.5660  |
| 1.4222        | 0.5911 | 1800 | 1.3791          | 25.68 | 46.64 | 70.1036  |
| 1.3456        | 0.6240 | 1900 | 1.3312          | 26.77 | 46.59 | 68.1225  |
| 1.1232        | 0.6568 | 2000 | 1.3433          | 27.24 | 44.86 | 72.2197  |
| 1.1674        | 0.6897 | 2100 | 1.3228          | 23.2  | 45.04 | 84.1513  |
| 1.0711        | 0.7225 | 2200 | 1.2771          | 25.23 | 46.41 | 76.5421  |
| 1.2015        | 0.7553 | 2300 | 1.2549          | 24.98 | 47.79 | 84.4214  |
| 1.1339        | 0.7882 | 2400 | 1.2758          | 27.01 | 48.04 | 72.2197  |
| 1.0196        | 0.8210 | 2500 | 1.2501          | 26.74 | 48.19 | 72.8050  |
| 0.9275        | 0.8539 | 2600 | 1.2430          | 32.42 | 50.41 | 62.2692  |
| 0.8328        | 0.8867 | 2700 | 1.2413          | 30.63 | 50.63 | 65.5110  |
| 0.7923        | 0.9195 | 2800 | 1.2441          | 26.18 | 48.19 | 74.0207  |
| 0.8887        | 0.9524 | 2900 | 1.2109          | 30.91 | 50.87 | 62.3593  |
| 0.7954        | 0.9852 | 3000 | 1.2233          | 31.63 | 49.93 | 64.5205  |
| 0.2886        | 1.0181 | 3100 | 1.2340          | 28.74 | 49.67 | 72.9401  |
| 0.2889        | 1.0509 | 3200 | 1.2369          | 31.74 | 49.23 | 63.6650  |
| 0.2812        | 1.0837 | 3300 | 1.2589          | 32.95 | 50.09 | 63.6200  |
| 0.2634        | 1.1166 | 3400 | 1.2428          | 30.14 | 49.93 | 69.9685  |
| 0.2248        | 1.1494 | 3500 | 1.2486          | 33.38 | 50.48 | 62.0441  |
| 0.2266        | 1.1823 | 3600 | 1.2089          | 29.8  | 50.46 | 67.2220  |
| 0.2148        | 1.2151 | 3700 | 1.1988          | 31.57 | 51.49 | 64.0252  |
| 0.2345        | 1.2479 | 3800 | 1.1889          | 32.46 | 52.24 | 64.1603  |
| 0.1924        | 1.2808 | 3900 | 1.1888          | 29.38 | 51.57 | 72.6700  |
| 0.2056        | 1.3136 | 4000 | 1.1869          | 30.79 | 52.18 | 66.5916  |


### Framework versions

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