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---
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
metrics:
- bleu
- wer
- chrf
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
type: ymoslem/IWSLT2023-GA-EN
metrics:
- name: Bleu
type: bleu
value: 26.85
- name: Wer
type: wer
value: 73.52543899144528
library_name: transformers
---
<!-- 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-small](https://huggingface.co/openai/whisper-small) on the IWSLT-2023, FLEURS, BiteSize, and SpokenWords datasets.
The best model checkpoint (this version) based on ChrF is at step 3300, epoch 3.67, and it achieves the following results on the evaluation set:
- Loss: 1.5823
- Bleu: 29.81
- Chrf: 46.50
- Wer: 66.7267
The best checkpoint based on BLEU achieves the following results:
- Loss: 1.5752
- Bleu: 30.77
- Chrf: 46.43
- Wer: 64.6556
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Experiment
- language=English
- +more steps
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.03
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.4954 | 0.11 | 100 | 3.7 | 18.03 | 2.1286 | 179.7839 |
| 2.045 | 0.22 | 200 | 12.65 | 25.53 | 1.8146 | 100.9005 |
| 1.7928 | 0.32 | 300 | 13.78 | 30.2 | 1.7253 | 101.9811 |
| 1.6615 | 0.43 | 400 | 15.8 | 31.88 | 1.6834 | 92.5259 |
| 1.4491 | 0.54 | 500 | 15.61 | 36.27 | 1.5971 | 107.3841 |
| 1.2074 | 0.65 | 600 | 19.92 | 36.31 | 1.5939 | 84.3314 |
| 1.2308 | 0.76 | 700 | 20.37 | 38.72 | 1.5234 | 84.8267 |
| 1.107 | 0.86 | 800 | 21.35 | 37.87 | 1.5460 | 82.8906 |
| 0.9491 | 0.97 | 900 | 21.06 | 40.74 | 1.5161 | 82.5754 |
| 0.384 | 1.08 | 1000 | 23.24 | 41.98 | 1.4927 | 82.2152 |
| 0.362 | 1.19 | 1100 | 23.19 | 42.24 | 1.5567 | 80.2792 |
| 0.3756 | 1.29 | 1200 | 27.83 | 43.8 | 1.5265 | 69.2481 |
| 0.3401 | 1.4 | 1300 | 21.79 | 41.66 | 1.5522 | 92.3908 |
| 0.3346 | 1.51 | 1400 | 24.61 | 42.15 | 1.5085 | 75.4615 |
| 0.3101 | 1.62 | 1500 | 26.67 | 43.41 | 1.4933 | 70.7789 |
| 0.3231 | 1.73 | 1600 | 27.95 | 42.82 | 1.4979 | 68.3026 |
| 0.2665 | 1.83 | 1700 | 28.5 | 43.76 | 1.4977 | 68.1225 |
| 0.2704 | 1.94 | 1800 | 28.15 | 43.87 | 1.5063 | 68.8429 |
| 0.0769 | 2.05 | 1900 | 25.76 | 43.22 | 1.5162 | 77.6227 |
| 0.0597 | 2.16 | 2000 | 25.04 | 43.15 | 1.5216 | 79.0635 |
| 0.0743 | 2.27 | 2100 | 27.85 | 44.43 | 1.5313 | 68.3926 |
| 0.0878 | 2.37 | 2200 | 27.54 | 43.96 | 1.5495 | 68.3476 |
| 0.0712 | 2.48 | 2300 | 28.28 | 44.39 | 1.5355 | 65.8712 |
| 0.0789 | 2.59 | 2400 | 28.64 | 44.75 | 1.5277 | 65.7812 |
| 0.073 | 2.7 | 2500 | 29.09 | 44.65 | 1.5327 | 65.7812 |
| 0.073 | 2.8 | 2600 | 25.26 | 43.44 | 1.5304 | 78.2981 |
| 0.0697 | 2.91 | 2700 | 25.71 | 43.02 | 1.5460 | 78.4782 |
| 0.0398 | 3.02 | 2800 | 28.26 | 44.71 | 1.5580 | 72.8501 |
| 0.0302 | 3.13 | 2900 | 30.25 | 45.46 | 1.5688 | 66.1414 |
| 0.0424 | 3.24 | 3000 | 29.88 | 45.21 | 1.5693 | 66.0964 |
| 0.0397 | 3.34 | 3100 | 30.01 | 45.85 | 1.5934 | 65.6911 |
| 0.0346 | 3.45 | 3200 | 30.2 | 45.8 | 1.5818 | 65.8262 |
| 0.032 | 3.56 | 3300 | 29.81 | 46.5 | 1.5823 | 66.7267 |
| 0.0348 | 3.67 | 3400 | 30.77 | 46.43 | 1.5752 | 64.6556 |
| 0.0277 | 3.78 | 3500 | 30.3 | 46.02 | 1.5791 | 64.6105 |
| 0.0364 | 3.88 | 3600 | 29.92 | 45.38 | 1.5895 | 65.0608 |
| 0.0398 | 3.99 | 3700 | 27.79 | 44.59 | 1.6167 | 68.2575 |
| 0.0152 | 4.1 | 3800 | 28.42 | 44.83 | 1.6241 | 67.5822 |
| 0.0201 | 4.21 | 3900 | 29.02 | 45.11 | 1.6243 | 67.4921 |
| 0.0168 | 4.31 | 4000 | 26.85 | 44.41 | 1.6195 | 73.5254 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |