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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
- 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: 30.91
- name: Wer
type: wer
value: 65.10580819450698
---
<!-- 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, SpokenWords, Tatoeba, and Wikimedia dataset.
The datasets are augmented in two ways: noise augmentation, and truncating low-amplitude samples.
The best model checkpoint (this version) based on ChrF is at step 2000, epoch 0.4378, and it achieves the following results on the evaluation set:
- Loss: 1.2119
- Bleu: 30.93
- Chrf: 49.09
- Wer: 63.1247
## 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: 0.02
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer |
|:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.7017 | 0.02 | 100 | 2.83 | 14.96 | 2.4392 | 169.5182 |
| 2.6732 | 0.04 | 200 | 7.27 | 22.72 | 1.9552 | 103.2868 |
| 2.1622 | 0.07 | 300 | 11.43 | 30.01 | 1.7297 | 108.2395 |
| 2.0314 | 0.09 | 400 | 12.96 | 31.0 | 1.6499 | 106.4385 |
| 1.7219 | 0.11 | 500 | 12.94 | 33.67 | 1.5543 | 107.6092 |
| 1.577 | 0.13 | 600 | 12.84 | 35.03 | 1.4812 | 118.5502 |
| 1.3569 | 0.1532 | 700 | 19.94 | 38.08 | 1.4559 | 84.2864 |
| 1.3401 | 0.1751 | 800 | 13.39 | 36.11 | 1.3855 | 126.4295 |
| 1.2272 | 0.1970 | 900 | 24.39 | 41.75 | 1.3764 | 70.7789 |
| 1.2793 | 0.2189 | 1000 | 23.01 | 42.13 | 1.3389 | 80.6844 |
| 1.0383 | 0.2408 | 1100 | 23.42 | 43.59 | 1.3125 | 82.3953 |
| 1.0485 | 0.2627 | 1200 | 25.42 | 42.99 | 1.2996 | 69.4732 |
| 1.0427 | 0.2846 | 1300 | 29.24 | 45.36 | 1.2996 | 65.6461 |
| 0.8174 | 0.3065 | 1400 | 27.28 | 45.67 | 1.2522 | 68.3926 |
| 0.7345 | 0.3284 | 1500 | 26.35 | 46.78 | 1.2349 | 79.1986 |
| 0.7551 | 0.3503 | 1600 | 27.81 | 46.49 | 1.2317 | 70.6439 |
| 0.6765 | 0.3722 | 1700 | 27.62 | 47.46 | 1.2062 | 70.9140 |
| 0.6613 | 0.3940 | 1800 | 26.56 | 47.12 | 1.2087 | 72.8050 |
| 0.6181 | 0.4159 | 1900 | 29.91 | 48.76 | 1.2139 | 65.2859 |
| 0.5809 | 0.4378 | 2000 | 30.93 | 49.09 | 1.2119 | 63.1247 |
| 0.5898 | 0.4597 | 2100 | 25.91 | 46.24 | 1.2540 | 73.9307 |
| 0.5926 | 0.4816 | 2200 | 25.19 | 44.72 | 1.2479 | 78.7933 |
| 0.5158 | 0.5035 | 2300 | 28.9 | 46.76 | 1.2532 | 66.3665 |
| 0.4511 | 0.5254 | 2400 | 28.89 | 46.83 | 1.2517 | 66.3215 |
| 0.4329 | 0.5473 | 2500 | 26.19 | 45.91 | 1.2573 | 72.6700 |
| 0.4106 | 0.5692 | 2600 | 26.91 | 46.84 | 1.2615 | 72.4899 |
| 0.4002 | 0.5911 | 2700 | 27.77 | 46.93 | 1.2396 | 71.0491 |
| 0.4047 | 0.6130 | 2800 | 29.9 | 47.79 | 1.2450 | 66.9968 |
| 0.3719 | 0.6349 | 2900 | 30.5 | 48.78 | 1.2522 | 65.1959 |
| 0.327 | 0.6567 | 3000 | 31.22 | 49.0 | 1.2493 | 64.1153 |
| 0.3138 | 0.6786 | 3100 | 30.1 | 47.82 | 1.2653 | 65.1959 |
| 0.3349 | 0.7005 | 3200 | 30.37 | 48.64 | 1.2651 | 63.9802 |
| 0.2807 | 0.7224 | 3300 | 26.02 | 45.46 | 1.2762 | 76.8573 |
| 0.2648 | 0.7443 | 3400 | 30.65 | 47.58 | 1.2761 | 64.6105 |
| 0.2633 | 0.7662 | 3500 | 29.73 | 47.74 | 1.2890 | 65.5110 |
| 0.2316 | 0.7881 | 3600 | 29.94 | 47.33 | 1.2886 | 66.4566 |
| 0.233 | 0.8100 | 3700 | 27.82 | 48.01 | 1.2905 | 73.1202 |
| 0.2196 | 0.8319 | 3800 | 31.51 | 48.66 | 1.2994 | 63.7100 |
| 0.2119 | 0.8538 | 3900 | 30.09 | 48.44 | 1.2910 | 65.0158 |
| 0.2082 | 0.8757 | 4000 | 30.91 | 47.99 | 1.2924 | 65.1058 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
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