---
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: 30.07
    - name: Wer
      type: wer
      value: 66.32147681224674
---

<!-- 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: 1.7281
- Bleu: 30.07
- Chrf: 46.7
- Wer: 66.3215

## 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: 0.03
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bleu  | Chrf  | Wer      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.1013        | 0.2155 | 100  | 1.8575          | 8.31  | 24.75 | 116.5241 |
| 1.5495        | 0.4310 | 200  | 1.5721          | 14.29 | 33.25 | 105.0428 |
| 1.3385        | 0.6466 | 300  | 1.5329          | 20.71 | 38.26 | 86.9428  |
| 1.071         | 0.8621 | 400  | 1.4540          | 20.37 | 39.28 | 85.1418  |
| 0.4771        | 1.0776 | 500  | 1.4936          | 18.05 | 39.17 | 93.8316  |
| 0.4685        | 1.2931 | 600  | 1.5303          | 24.36 | 39.36 | 75.6866  |
| 0.4477        | 1.5086 | 700  | 1.5242          | 22.93 | 42.01 | 80.3242  |
| 0.4238        | 1.7241 | 800  | 1.5052          | 26.32 | 43.01 | 68.8879  |
| 0.3802        | 1.9397 | 900  | 1.5171          | 25.94 | 41.44 | 73.7956  |
| 0.1429        | 2.1552 | 1000 | 1.5741          | 28.83 | 43.83 | 65.4660  |
| 0.1607        | 2.3707 | 1100 | 1.6029          | 27.67 | 43.2  | 64.9257  |
| 0.1513        | 2.5862 | 1200 | 1.6130          | 28.61 | 44.28 | 66.1864  |
| 0.137         | 2.8017 | 1300 | 1.6087          | 21.97 | 40.99 | 89.4642  |
| 0.112         | 3.0172 | 1400 | 1.6146          | 28.74 | 44.01 | 65.9613  |
| 0.0717        | 3.2328 | 1500 | 1.6156          | 27.3  | 42.78 | 70.0585  |
| 0.0596        | 3.4483 | 1600 | 1.6381          | 27.31 | 45.58 | 69.6983  |
| 0.064         | 3.6638 | 1700 | 1.6262          | 29.73 | 45.88 | 65.9163  |
| 0.0642        | 3.8793 | 1800 | 1.6798          | 30.78 | 46.13 | 68.2575  |
| 0.0335        | 4.0948 | 1900 | 1.6854          | 29.55 | 45.06 | 67.8523  |
| 0.0366        | 4.3103 | 2000 | 1.6963          | 28.83 | 44.42 | 68.8879  |
| 0.036         | 4.5259 | 2100 | 1.7062          | 28.05 | 43.79 | 69.6983  |
| 0.0259        | 4.7414 | 2200 | 1.7279          | 28.75 | 45.25 | 68.3926  |
| 0.0353        | 4.9569 | 2300 | 1.7084          | 29.7  | 46.13 | 66.3665  |
| 0.0138        | 5.1724 | 2400 | 1.6906          | 30.81 | 46.26 | 64.1603  |
| 0.0156        | 5.3879 | 2500 | 1.7135          | 29.09 | 45.94 | 67.4471  |
| 0.0133        | 5.6034 | 2600 | 1.7311          | 29.86 | 45.61 | 65.5110  |
| 0.0161        | 5.8190 | 2700 | 1.7067          | 29.5  | 45.22 | 67.0869  |
| 0.0098        | 6.0345 | 2800 | 1.7038          | 30.32 | 46.6  | 65.3309  |
| 0.008         | 6.25   | 2900 | 1.7261          | 29.88 | 46.41 | 66.8167  |
| 0.0045        | 6.4655 | 3000 | 1.7281          | 30.07 | 46.7  | 66.3215  |


### Framework versions

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