---
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
metrics:
- bleu
- wer
model-index:
- name: Whisper Small GA-EN Speech Translation Raw + warmup_ratio=0.01
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: IWSLT-2023, FLEURS, BiteSize, and SpokenWords
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - name: Bleu
      type: bleu
      value: 30.14
    - name: Wer
      type: wer
      value: 68.75281404772625
---

<!-- 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 Raw + warmup_ratio=0.01

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the IWSLT-2023, FLEURS, BiteSize, and SpokenWords dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6820
- Bleu: 30.14
- Chrf: 44.97
- Wer: 68.7528

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bleu  | Chrf  | Wer      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.0448        | 0.2155 | 100  | 1.7532          | 10.55 | 28.24 | 117.7848 |
| 1.5028        | 0.4310 | 200  | 1.5587          | 17.12 | 35.11 | 93.2913  |
| 1.3161        | 0.6466 | 300  | 1.5073          | 17.41 | 37.84 | 104.0973 |
| 1.0537        | 0.8621 | 400  | 1.4560          | 17.49 | 38.19 | 103.8721 |
| 0.4704        | 1.0776 | 500  | 1.4946          | 16.44 | 36.79 | 102.9266 |
| 0.4589        | 1.2931 | 600  | 1.5117          | 21.55 | 38.66 | 82.6204  |
| 0.4343        | 1.5086 | 700  | 1.5146          | 24.5  | 41.43 | 75.5966  |
| 0.4068        | 1.7241 | 800  | 1.4547          | 28.37 | 45.27 | 65.6011  |
| 0.3757        | 1.9397 | 900  | 1.4957          | 27.0  | 43.66 | 67.4471  |
| 0.1388        | 2.1552 | 1000 | 1.5642          | 26.72 | 41.74 | 66.5016  |
| 0.1472        | 2.3707 | 1100 | 1.5845          | 27.74 | 42.67 | 68.1675  |
| 0.1408        | 2.5862 | 1200 | 1.5932          | 28.95 | 44.07 | 65.4660  |
| 0.1436        | 2.8017 | 1300 | 1.5808          | 27.66 | 42.71 | 68.3476  |
| 0.1094        | 3.0172 | 1400 | 1.5684          | 27.1  | 43.29 | 71.3643  |
| 0.0677        | 3.2328 | 1500 | 1.6287          | 26.92 | 42.33 | 68.4376  |
| 0.0567        | 3.4483 | 1600 | 1.6431          | 23.58 | 42.85 | 81.7199  |
| 0.0594        | 3.6638 | 1700 | 1.6084          | 26.54 | 42.45 | 77.4426  |
| 0.0623        | 3.8793 | 1800 | 1.5817          | 29.29 | 45.85 | 67.8523  |
| 0.0323        | 4.0948 | 1900 | 1.6630          | 29.24 | 43.47 | 67.5822  |
| 0.0317        | 4.3103 | 2000 | 1.6494          | 26.43 | 43.4  | 73.3904  |
| 0.0325        | 4.5259 | 2100 | 1.6968          | 27.06 | 42.4  | 68.5277  |
| 0.025         | 4.7414 | 2200 | 1.6436          | 29.16 | 44.24 | 67.5371  |
| 0.0316        | 4.9569 | 2300 | 1.6412          | 29.9  | 46.15 | 66.5016  |
| 0.0159        | 5.1724 | 2400 | 1.6714          | 29.56 | 44.68 | 66.7717  |
| 0.0158        | 5.3879 | 2500 | 1.6458          | 29.56 | 45.46 | 65.8262  |
| 0.015         | 5.6034 | 2600 | 1.6595          | 29.97 | 44.9  | 68.0774  |
| 0.0145        | 5.8190 | 2700 | 1.6545          | 31.15 | 46.35 | 65.6461  |
| 0.0106        | 6.0345 | 2800 | 1.6724          | 30.24 | 45.36 | 66.8618  |
| 0.0076        | 6.25   | 2900 | 1.6834          | 30.25 | 45.13 | 67.9424  |
| 0.0049        | 6.4655 | 3000 | 1.6820          | 30.14 | 44.97 | 68.7528  |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1