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---
language:
- ga
- en
license: apache-2.0
base_model: openai/whisper-medium
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 Small 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: 33.77
- name: Wer
type: wer
value: 60.828455650607836
---
<!-- 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 Small 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, and Wikimedia dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2028
- Bleu: 33.77
- Chrf: 52.79
- Wer: 60.8285
## 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 |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.4145 | 0.0109 | 100 | 2.1019 | 2.32 | 16.08 | 170.5088 |
| 2.6073 | 0.0219 | 200 | 2.0370 | 5.94 | 23.77 | 158.8924 |
| 2.593 | 0.0328 | 300 | 1.8529 | 3.67 | 21.53 | 238.6312 |
| 2.3123 | 0.0438 | 400 | 1.8500 | 9.01 | 28.13 | 135.0293 |
| 2.3347 | 0.0547 | 500 | 1.7816 | 15.05 | 31.9 | 90.7249 |
| 2.1277 | 0.0657 | 600 | 1.6916 | 14.24 | 32.29 | 88.4286 |
| 2.1836 | 0.0766 | 700 | 1.6517 | 12.15 | 32.7 | 128.1405 |
| 2.0112 | 0.0876 | 800 | 1.6275 | 19.76 | 38.15 | 79.2886 |
| 1.8387 | 0.0985 | 900 | 1.6349 | 17.26 | 38.82 | 91.0851 |
| 1.8335 | 0.1095 | 1000 | 1.5843 | 20.93 | 38.02 | 75.9118 |
| 1.7849 | 0.1204 | 1100 | 1.5863 | 15.98 | 37.5 | 96.5781 |
| 1.5698 | 0.1314 | 1200 | 1.5371 | 16.42 | 39.07 | 103.6020 |
| 1.4759 | 0.1423 | 1300 | 1.5250 | 18.56 | 38.41 | 96.5781 |
| 1.4915 | 0.1533 | 1400 | 1.4862 | 22.05 | 40.15 | 75.1013 |
| 1.6583 | 0.1642 | 1500 | 1.4727 | 18.11 | 39.65 | 95.7677 |
| 1.3981 | 0.1752 | 1600 | 1.4367 | 27.31 | 44.5 | 66.0513 |
| 1.2646 | 0.1861 | 1700 | 1.4574 | 22.85 | 42.19 | 74.4710 |
| 1.2172 | 0.1970 | 1800 | 1.3818 | 20.77 | 42.5 | 82.7105 |
| 1.183 | 0.2080 | 1900 | 1.4380 | 22.75 | 41.28 | 76.7672 |
| 1.1931 | 0.2189 | 2000 | 1.3917 | 23.58 | 41.13 | 77.3075 |
| 1.172 | 0.2299 | 2100 | 1.3892 | 24.58 | 44.4 | 74.3809 |
| 1.0284 | 0.2408 | 2200 | 1.3806 | 23.34 | 44.1 | 78.0279 |
| 0.8507 | 0.2518 | 2300 | 1.3210 | 28.67 | 46.79 | 67.1769 |
| 0.9615 | 0.2627 | 2400 | 1.3103 | 27.95 | 46.8 | 70.0135 |
| 0.8049 | 0.2737 | 2500 | 1.3141 | 29.92 | 48.9 | 67.2220 |
| 0.7639 | 0.2846 | 2600 | 1.3085 | 30.91 | 49.05 | 64.2053 |
| 0.8594 | 0.2956 | 2700 | 1.3378 | 27.8 | 47.84 | 68.8879 |
| 0.7482 | 0.3065 | 2800 | 1.2978 | 30.6 | 48.62 | 64.9257 |
| 0.6941 | 0.3175 | 2900 | 1.3060 | 29.92 | 47.92 | 65.8712 |
| 0.7282 | 0.3284 | 3000 | 1.2959 | 31.09 | 48.13 | 65.3309 |
| 0.6298 | 0.3394 | 3100 | 1.2893 | 29.76 | 48.8 | 67.1769 |
| 0.619 | 0.3503 | 3200 | 1.2388 | 32.61 | 50.27 | 62.0891 |
| 0.6252 | 0.3612 | 3300 | 1.2550 | 32.71 | 50.96 | 62.4493 |
| 0.4699 | 0.3722 | 3400 | 1.2463 | 32.02 | 51.24 | 65.2409 |
| 0.5121 | 0.3831 | 3500 | 1.2214 | 32.26 | 51.29 | 63.7551 |
| 0.5092 | 0.3941 | 3600 | 1.2182 | 32.88 | 51.59 | 62.0891 |
| 0.4365 | 0.4050 | 3700 | 1.2049 | 32.16 | 51.5 | 62.3143 |
| 0.2971 | 0.4160 | 3800 | 1.2201 | 34.45 | 52.78 | 59.7479 |
| 0.389 | 0.4269 | 3900 | 1.2007 | 33.86 | 53.28 | 60.6033 |
| 0.3879 | 0.4379 | 4000 | 1.2028 | 33.77 | 52.79 | 60.8285 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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