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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
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: 32.14
- name: Wer
type: wer
value: 65.96127870328681
---
<!-- 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.
The best model checkpoint (this version) is at step 1400, epoch 1.84 (4 x 0.46), and it achieves the following results on the evaluation set:
- Loss: 1.0240
- Bleu: 33.55
- Chrf: 50.95
- Wer: 60.1981
## 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_steps: 0.03
- training_steps: 2000
- mixed_precision_training: Native AMP
### Hardware
4 x A40 48GB VRAM, with batch size 4 per machine (total: 16)
### Training results
| Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.9468 | 0.03 | 100 | 4.72 | 20.55 | 2.2829 | 120.6213 |
| 2.5074 | 0.07 | 200 | 7.81 | 25.23 | 2.0136 | 114.8131 |
| 2.2406 | 0.1 | 300 | 11.24 | 29.39 | 1.8224 | 95.9928 |
| 2.2466 | 0.13 | 400 | 16.01 | 34.73 | 1.6530 | 83.4309 |
| 2.0276 | 0.16 | 500 | 16.69 | 34.76 | 1.5344 | 94.2368 |
| 1.8429 | 0.2 | 600 | 21.37 | 37.48 | 1.4923 | 78.5682 |
| 1.7621 | 0.23 | 700 | 23.4 | 40.89 | 1.3666 | 74.3359 |
| 1.5629 | 0.26 | 800 | 24.76 | 44.63 | 1.2876 | 76.6321 |
| 1.5458 | 0.3 | 900 | 25.81 | 44.59 | 1.2178 | 72.6249 |
| 1.2971 | 0.33 | 1000 | 27.63 | 46.91 | 1.1823 | 70.2837 |
| 1.3852 | 0.36 | 1100 | 27.18 | 46.16 | 1.2303 | 70.6889 |
| 1.309 | 0.39 | 1200 | 27.65 | 47.41 | 1.1573 | 72.0396 |
| 1.1818 | 0.43 | 1300 | 31.17 | 48.36 | 1.1304 | 61.6389 |
| 1.2711 | 0.46 | 1400 | 33.55 | 50.95 | 1.0839 | 60.1981 |
| 1.1305 | 0.49 | 1500 | 30.37 | 50.78 | 1.0718 | 68.6628 |
| 1.0544 | 0.53 | 1600 | 26.98 | 48.1 | 1.1109 | 73.7506 |
| 1.125 | 0.56 | 1700 | 30.76 | 50.19 | 1.0709 | 61.7740 |
| 1.1348 | 0.59 | 1800 | 33.71 | 50.6 | 1.0530 | 59.9280 |
| 1.14 | 0.62 | 1900 | 31.45 | 50.16 | 1.0392 | 66.9068 |
| 1.1059 | 0.66 | 2000 | 32.14 | 50.84 | 1.0240 | 65.9613 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.0.1+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
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