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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 Medium 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: 30.79
- name: Wer
type: wer
value: 66.59162539396668
---
<!-- 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 Medium 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.
It achieves the following results on the evaluation set:
- Loss: 1.1869
- Bleu: 30.79
- Chrf: 52.18
- Wer: 66.5916
## 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: 0.02
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Wer |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.7614 | 0.0328 | 100 | 2.1538 | 3.55 | 19.32 | 146.1954 |
| 2.6486 | 0.0657 | 200 | 1.9662 | 7.4 | 25.58 | 132.4178 |
| 2.5302 | 0.0985 | 300 | 1.8689 | 6.8 | 24.76 | 172.0846 |
| 2.4506 | 0.1314 | 400 | 1.8244 | 13.88 | 32.7 | 95.9478 |
| 2.3631 | 0.1642 | 500 | 1.6759 | 14.35 | 32.54 | 95.6776 |
| 2.173 | 0.1970 | 600 | 1.7051 | 13.14 | 34.29 | 93.2463 |
| 2.3489 | 0.2299 | 700 | 1.6077 | 15.85 | 36.52 | 87.7082 |
| 2.0183 | 0.2627 | 800 | 1.5894 | 15.72 | 36.54 | 94.1018 |
| 2.1502 | 0.2956 | 900 | 1.5739 | 16.58 | 36.4 | 96.7132 |
| 2.016 | 0.3284 | 1000 | 1.5470 | 18.99 | 39.22 | 83.9712 |
| 1.747 | 0.3612 | 1100 | 1.5428 | 15.49 | 38.02 | 101.5759 |
| 1.6728 | 0.3941 | 1200 | 1.5129 | 19.45 | 39.24 | 89.4642 |
| 1.6476 | 0.4269 | 1300 | 1.4747 | 21.77 | 40.53 | 82.8906 |
| 1.6764 | 0.4598 | 1400 | 1.4672 | 16.58 | 39.94 | 95.0923 |
| 1.5683 | 0.4926 | 1500 | 1.4116 | 22.0 | 42.55 | 77.8028 |
| 1.3607 | 0.5255 | 1600 | 1.4290 | 24.15 | 43.36 | 74.1108 |
| 1.4888 | 0.5583 | 1700 | 1.3684 | 22.61 | 42.39 | 83.5660 |
| 1.4222 | 0.5911 | 1800 | 1.3791 | 25.68 | 46.64 | 70.1036 |
| 1.3456 | 0.6240 | 1900 | 1.3312 | 26.77 | 46.59 | 68.1225 |
| 1.1232 | 0.6568 | 2000 | 1.3433 | 27.24 | 44.86 | 72.2197 |
| 1.1674 | 0.6897 | 2100 | 1.3228 | 23.2 | 45.04 | 84.1513 |
| 1.0711 | 0.7225 | 2200 | 1.2771 | 25.23 | 46.41 | 76.5421 |
| 1.2015 | 0.7553 | 2300 | 1.2549 | 24.98 | 47.79 | 84.4214 |
| 1.1339 | 0.7882 | 2400 | 1.2758 | 27.01 | 48.04 | 72.2197 |
| 1.0196 | 0.8210 | 2500 | 1.2501 | 26.74 | 48.19 | 72.8050 |
| 0.9275 | 0.8539 | 2600 | 1.2430 | 32.42 | 50.41 | 62.2692 |
| 0.8328 | 0.8867 | 2700 | 1.2413 | 30.63 | 50.63 | 65.5110 |
| 0.7923 | 0.9195 | 2800 | 1.2441 | 26.18 | 48.19 | 74.0207 |
| 0.8887 | 0.9524 | 2900 | 1.2109 | 30.91 | 50.87 | 62.3593 |
| 0.7954 | 0.9852 | 3000 | 1.2233 | 31.63 | 49.93 | 64.5205 |
| 0.2886 | 1.0181 | 3100 | 1.2340 | 28.74 | 49.67 | 72.9401 |
| 0.2889 | 1.0509 | 3200 | 1.2369 | 31.74 | 49.23 | 63.6650 |
| 0.2812 | 1.0837 | 3300 | 1.2589 | 32.95 | 50.09 | 63.6200 |
| 0.2634 | 1.1166 | 3400 | 1.2428 | 30.14 | 49.93 | 69.9685 |
| 0.2248 | 1.1494 | 3500 | 1.2486 | 33.38 | 50.48 | 62.0441 |
| 0.2266 | 1.1823 | 3600 | 1.2089 | 29.8 | 50.46 | 67.2220 |
| 0.2148 | 1.2151 | 3700 | 1.1988 | 31.57 | 51.49 | 64.0252 |
| 0.2345 | 1.2479 | 3800 | 1.1889 | 32.46 | 52.24 | 64.1603 |
| 0.1924 | 1.2808 | 3900 | 1.1888 | 29.38 | 51.57 | 72.6700 |
| 0.2056 | 1.3136 | 4000 | 1.1869 | 30.79 | 52.18 | 66.5916 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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