metadata
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 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 as well as a copy of the dataset with noise reduction
and normalization (for both train and test)
type: ymoslem/IWSLT2023-GA-EN
metrics:
- name: Bleu
type: bleu
value: 30.66
- name: Wer
type: wer
value: 65.46600630346691
Whisper Small GA-EN Speech Translation
This model is a fine-tuned version of openai/whisper-small on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia as well as a copy of the dataset with noise reduction and normalization (for both train and test) dataset. It achieves the following results on the evaluation set:
- Loss: 1.3339
- Bleu: 30.66
- Chrf: 46.99
- Wer: 65.4660
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: 0.01
- training_steps: 3000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer |
|---|---|---|---|---|---|---|
| 1.41 | 0.07 | 100 | 9.78 | 25.23 | 1.8782 | 96.3980 |
| 1.2436 | 0.13 | 200 | 10.23 | 28.66 | 1.8301 | 125.9343 |
| 1.593 | 0.2 | 300 | 9.53 | 30.7 | 1.7066 | 137.1454 |
| 1.9589 | 0.26 | 400 | 12.08 | 32.94 | 1.5629 | 109.3652 |
| 1.8174 | 0.33 | 500 | 13.73 | 34.5 | 1.5154 | 123.5930 |
| 1.6775 | 0.39 | 600 | 15.8 | 35.68 | 1.5220 | 102.2062 |
| 1.7074 | 0.46 | 700 | 16.62 | 37.96 | 1.4570 | 100.5853 |
| 1.5793 | 0.53 | 800 | 24.5 | 39.91 | 1.4265 | 71.3643 |
| 1.3708 | 0.59 | 900 | 24.35 | 42.26 | 1.3845 | 73.7956 |
| 1.3217 | 0.66 | 1000 | 19.34 | 41.3 | 1.3662 | 87.7533 |
| 1.2572 | 0.72 | 1100 | 21.59 | 41.35 | 1.3529 | 88.4286 |
| 1.1447 | 0.79 | 1200 | 28.39 | 44.99 | 1.3228 | 65.9163 |
| 1.1544 | 0.85 | 1300 | 23.69 | 43.07 | 1.2972 | 80.1891 |
| 1.0291 | 0.92 | 1400 | 29.36 | 45.45 | 1.2828 | 70.9590 |
| 0.9394 | 0.98 | 1500 | 26.44 | 44.0 | 1.2812 | 74.1558 |
| 0.3764 | 1.05 | 1600 | 26.95 | 44.82 | 1.3248 | 73.8406 |
| 0.3338 | 1.12 | 1700 | 26.5 | 44.96 | 1.3212 | 77.3976 |
| 0.3148 | 1.18 | 1800 | 29.57 | 46.31 | 1.3188 | 66.7267 |
| 0.3206 | 1.25 | 1900 | 30.87 | 47.21 | 1.3050 | 64.4755 |
| 0.3069 | 1.31 | 2000 | 30.15 | 46.19 | 1.3053 | 65.6911 |
| 0.3342 | 1.38 | 2100 | 1.3506 | 24.14 | 44.12 | 77.2625 |
| 0.3125 | 1.44 | 2200 | 1.3369 | 30.21 | 46.08 | 63.9802 |
| 0.319 | 1.51 | 2300 | 1.3601 | 27.71 | 45.45 | 69.9235 |
| 0.3067 | 1.58 | 2400 | 1.3473 | 26.92 | 45.73 | 69.3381 |
| 0.2621 | 1.64 | 2500 | 1.3354 | 28.36 | 46.14 | 66.9068 |
| 0.2709 | 1.71 | 2600 | 1.3339 | 28.75 | 45.47 | 65.2859 |
| 0.2644 | 1.77 | 2700 | 1.3100 | 28.84 | 47.35 | 65.8262 |
| 0.2511 | 1.84 | 2800 | 1.3261 | 29.41 | 47.31 | 69.4732 |
| 0.2232 | 1.9 | 2900 | 1.3382 | 30.79 | 46.63 | 64.1153 |
| 0.236 | 1.97 | 3000 | 1.3339 | 30.66 | 46.99 | 65.4660 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2