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---
language:
- ga
- en
license: apache-2.0
base_model: openai/whisper-medium
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: 29.54
- name: Wer
type: wer
value: 62.40432237730752
---
<!-- 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-medium](https://huggingface.co./openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1929
- Bleu: 29.54
- Chrf: 51.58
- Wer: 62.4043
## 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: 0.03
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Wer |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.4382 | 0.0109 | 100 | 2.1114 | 3.07 | 16.85 | 171.0491 |
| 2.6151 | 0.0219 | 200 | 2.0207 | 6.25 | 23.02 | 126.9698 |
| 2.5699 | 0.0328 | 300 | 1.8660 | 5.71 | 24.03 | 155.5606 |
| 2.3084 | 0.0438 | 400 | 1.8084 | 9.87 | 28.45 | 129.0860 |
| 2.3327 | 0.0547 | 500 | 1.7823 | 12.01 | 31.92 | 102.7915 |
| 2.1495 | 0.0657 | 600 | 1.7238 | 13.97 | 32.4 | 98.6042 |
| 2.2164 | 0.0766 | 700 | 1.6538 | 11.21 | 33.19 | 146.0153 |
| 2.0071 | 0.0876 | 800 | 1.7038 | 14.34 | 35.72 | 96.9383 |
| 1.8334 | 0.0985 | 900 | 1.6329 | 16.51 | 37.23 | 96.8032 |
| 1.8359 | 0.1095 | 1000 | 1.6637 | 17.87 | 35.94 | 84.4665 |
| 1.7703 | 0.1204 | 1100 | 1.5626 | 19.54 | 39.02 | 79.7839 |
| 1.5805 | 0.1314 | 1200 | 1.5618 | 20.19 | 40.4 | 77.8028 |
| 1.4545 | 0.1423 | 1300 | 1.5599 | 13.88 | 35.53 | 112.5619 |
| 1.5177 | 0.1533 | 1400 | 1.4880 | 18.79 | 40.11 | 84.6916 |
| 1.6335 | 0.1642 | 1500 | 1.4996 | 16.41 | 38.64 | 96.9833 |
| 1.3809 | 0.1752 | 1600 | 1.4739 | 18.3 | 40.17 | 101.8910 |
| 1.2694 | 0.1861 | 1700 | 1.4498 | 22.53 | 43.15 | 76.9923 |
| 1.2321 | 0.1970 | 1800 | 1.4163 | 19.92 | 42.59 | 84.6015 |
| 1.1969 | 0.2080 | 1900 | 1.4137 | 21.63 | 44.92 | 85.3670 |
| 1.2023 | 0.2189 | 2000 | 1.3530 | 20.42 | 41.57 | 82.8906 |
| 1.1676 | 0.2299 | 2100 | 1.3723 | 22.82 | 44.23 | 78.1180 |
| 1.0332 | 0.2408 | 2200 | 1.3641 | 26.73 | 44.75 | 70.2386 |
| 0.8589 | 0.2518 | 2300 | 1.3344 | 26.94 | 46.89 | 72.7600 |
| 0.9829 | 0.2627 | 2400 | 1.3181 | 28.15 | 47.21 | 69.1130 |
| 0.8228 | 0.2737 | 2500 | 1.3049 | 26.98 | 47.41 | 74.0207 |
| 0.7667 | 0.2846 | 2600 | 1.2698 | 30.0 | 49.42 | 65.1058 |
| 0.8749 | 0.2956 | 2700 | 1.2878 | 27.91 | 47.67 | 66.9518 |
| 0.7504 | 0.3065 | 2800 | 1.2670 | 32.03 | 50.35 | 63.6650 |
| 0.7069 | 0.3175 | 2900 | 1.2771 | 30.7 | 49.53 | 64.4304 |
| 0.7199 | 0.3284 | 3000 | 1.2658 | 30.21 | 48.93 | 65.5561 |
| 0.6207 | 0.3394 | 3100 | 1.2687 | 30.82 | 49.11 | 66.0063 |
| 0.5995 | 0.3503 | 3200 | 1.2207 | 31.99 | 50.94 | 62.9446 |
| 0.6294 | 0.3612 | 3300 | 1.2422 | 31.05 | 50.85 | 64.7006 |
| 0.4612 | 0.3722 | 3400 | 1.2203 | 33.1 | 51.82 | 61.9090 |
| 0.5138 | 0.3831 | 3500 | 1.2007 | 32.08 | 51.86 | 63.0797 |
| 0.5059 | 0.3941 | 3600 | 1.2130 | 31.8 | 51.19 | 63.9352 |
| 0.417 | 0.4050 | 3700 | 1.1975 | 32.45 | 51.41 | 62.2692 |
| 0.2958 | 0.4160 | 3800 | 1.2046 | 29.29 | 51.39 | 62.7645 |
| 0.393 | 0.4269 | 3900 | 1.1968 | 28.95 | 51.45 | 63.1697 |
| 0.3858 | 0.4379 | 4000 | 1.1929 | 29.54 | 51.58 | 62.4043 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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