transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer ga en dataset:ymoslem/IWSLT2023-GA-EN dataset:ymoslem/FLEURS-GA-EN dataset:ymoslem/BitesizeIrish-GA-EN dataset:ymoslem/SpokenWords-GA-EN-MTed dataset:ymoslem/Tatoeba-Speech-Irish dataset:ymoslem/Wikimedia-Speech-Irish base_model:openai/whisper-small base_model:finetune:openai/whisper-small license:apache-2.0 model-index endpoints_compatible region:us
---
base_model: openai/whisper-small
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
language:
- ga
- en
library_name: transformers
license: apache-2.0
metrics:
- bleu
- wer
- chrf
tags:
- generated_from_trainer
model-index:
- name: Whisper Small GA-EN Speech Translation
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia + augmented
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - type: bleu
      value: 30.11
      name: Bleu
    - type: wer
      value: 71.49932462854571
      name: Wer
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Small 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 datasets.
The datasets are augmented in two ways: noise augmentation, and truncating low-amplitude samples.
The best model checkpoint (this version) based on ChrF is at step 2800, epoch 1.2259, and
it achieves the following results on the evaluation set:
- Loss: 1.3547
- Bleu: 32.57
- Chrf: 47.04
- Wer: 62.0891

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Hardware

1 NVIDIA A100-SXM4-80GB

### 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_steps: 0
- training_steps: 3000
- mixed_precision_training: Native AMP
- generation_max_length: 225

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bleu  | Chrf  | Wer      |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.3533        | 0.0438 | 100  | 1.7789          | 6.29  | 25.08 | 148.7618 |
| 1.9035        | 0.0876 | 200  | 1.5122          | 18.21 | 34.02 | 85.6821  |
| 1.5357        | 0.1313 | 300  | 1.3983          | 14.01 | 33.7  | 93.3363  |
| 1.3056        | 0.1751 | 400  | 1.3447          | 18.12 | 37.35 | 95.0023  |
| 1.1177        | 0.2189 | 500  | 1.3168          | 18.47 | 38.44 | 95.3624  |
| 0.984         | 0.2627 | 600  | 1.3202          | 26.82 | 41.23 | 67.3120  |
| 0.8945        | 0.3065 | 700  | 1.2947          | 26.73 | 42.53 | 67.1319  |
| 0.7508        | 0.3503 | 800  | 1.2476          | 25.67 | 42.06 | 74.2008  |
| 0.7127        | 0.3940 | 900  | 1.2630          | 22.59 | 41.05 | 75.7767  |
| 0.5944        | 0.4378 | 1000 | 1.2726          | 22.37 | 40.31 | 82.4854  |
| 0.4972        | 0.4816 | 1100 | 1.2898          | 22.88 | 42.52 | 82.5304  |
| 0.4517        | 0.5254 | 1200 | 1.2509          | 27.99 | 44.42 | 64.1603  |
| 0.3885        | 0.5692 | 1300 | 1.2887          | 29.58 | 44.8  | 63.1247  |
| 0.3337        | 0.6130 | 1400 | 1.2645          | 30.05 | 45.5  | 62.6294  |
| 0.2852        | 0.6567 | 1500 | 1.2972          | 28.2  | 43.57 | 68.6628  |
| 0.2583        | 0.7005 | 1600 | 1.2716          | 28.21 | 45.06 | 73.6155  |
| 0.2016        | 0.7443 | 1700 | 1.3346          | 27.55 | 43.21 | 74.3809  |
| 0.1883        | 0.7881 | 1800 | 1.3124          | 21.45 | 41.83 | 94.1018  |
| 0.1514        | 0.8319 | 1900 | 1.3178          | 28.2  | 44.09 | 63.7551  |
| 0.1311        | 0.8757 | 2000 | 1.3246          | 27.33 | 43.25 | 74.3359  |
| 0.1128        | 0.9194 | 2100 | 1.3464          | 25.21 | 42.93 | 83.2508  |
| 0.0994        | 0.9632 | 2200 | 1.3315          | 30.51 | 45.74 | 64.7456  |
| 0.0512        | 1.0070 | 2300 | 1.3377          | 30.89 | 46.44 | 63.3498  |
| 0.0447        | 1.0508 | 2400 | 1.3587          | 28.72 | 44.36 | 64.3404  |
| 0.0368        | 1.0946 | 2500 | 1.3619          | 31.53 | 46.56 | 61.9541  |
| 0.0281        | 1.1384 | 2600 | 1.3596          | 30.98 | 46.45 | 70.4638  |
| 0.0273        | 1.1821 | 2700 | 1.3656          | 32.09 | 46.85 | 62.1792  |
| 0.0287        | 1.2259 | 2800 | 1.3547          | 32.57 | 47.04 | 62.0891  |
| 0.025         | 1.2697 | 2900 | 1.3539          | 26.94 | 45.43 | 81.1796  |
| 0.0263        | 1.3135 | 3000 | 1.3512          | 30.11 | 46.73 | 71.4993  |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1