|
--- |
|
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 |
|
metrics: |
|
- bleu |
|
- wer |
|
model-index: |
|
- name: Whisper Small GA-EN Speech Translation, fine-tuned from 1.2, without SpokenWords |
|
results: |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: IWSLT-2023, FLEURS, and BiteSize |
|
type: ymoslem/IWSLT2023-GA-EN |
|
metrics: |
|
- name: Bleu |
|
type: bleu |
|
value: 29.96 |
|
- name: Wer |
|
type: wer |
|
value: 66.9968482665466 |
|
--- |
|
|
|
<!-- 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, fine-tuned from 1.2, without SpokenWords |
|
|
|
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IWSLT-2023, FLEURS, and BiteSize dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.7177 |
|
- Bleu: 29.96 |
|
- Chrf: 45.61 |
|
- Wer: 66.9968 |
|
|
|
## 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: 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: 5000 |
|
- 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 | |
|
| 0.0522 | 5.97 | 3500 | 1.6080| 29.69 | 45.47 | 65.8712 | |
|
| 0.0443 | 6.14 | 3600 | 1.6272| 29.54 | 44.71 | 65.1508 | |
|
| 0.0492 | 6.31 | 3700 | 1.6211| 29.3 | 45.36 | 68.3926 | |
|
| 0.0544 | 6.48 | 3800 | 1.6069| 30.08 | 44.39 | 64.9257 | |
|
| 0.0574 | 6.66 | 3900 | 1.6306| 28.86 | 44.6 | 66.2765 | |
|
| 0.0535 | 6.83 | 4000 | 1.6722| 27.92 | 43.48 | 67.9874 | |
|
| 0.0424 | 7.0 | 4100 | 1.6968| 27.48 | 44.29 | 70.3737 | |
|
| 0.0235 | 7.17 | 4200 | 1.6768| 27.97 | 45.34 | 70.0135 | |
|
| 0.0262 | 7.34 | 4300 | 1.6908| 28.77 | 45.74 | 68.3926 | |
|
| 0.0218 | 7.51 | 4400 | 1.6890| 28.97 | 46.57 | 69.5182 | |
|
| 0.0293 | 7.68 | 4500 | 1.6742| 29.51 | 45.38 | 68.8429 | |
|
| 0.0194 | 7.85 | 4600 | 1.6962| 29.63 | 45.18 | 67.9874 | |
|
| 0.0187 | 8.02 | 4700 | 1.6936| 30.1 | 45.28 | 66.0964 | |
|
| 0.0115 | 8.19 | 4800 | 1.7162| 30.0 | 46.02 | 67.6722 | |
|
| 0.0138 | 8.36 | 4900 | 1.7113| 30.34 | 46.01 | 66.6817 | |
|
| 0.0098 | 8.53 | 5000 | 1.7177| 29.96 | 45.61 | 66.9968 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
