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
license: apache-2.0
metrics:
- bleu
- wer
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
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - type: bleu
      value: 30.91
      name: Bleu
    - type: wer
      value: 65.10580819450698
      name: Wer
---

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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 dataset.
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 2000, epoch 0.4378, and it achieves the following results on the evaluation set:
- Loss: 1.2119
- Bleu: 30.93
- Chrf: 49.09
- Wer: 63.1247
  
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.02
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Bleu  | Chrf  | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.7017        | 0.02   | 100  | 2.83  | 14.96 | 2.4392          | 169.5182 |
| 2.6732        | 0.04   | 200  | 7.27  | 22.72 | 1.9552          | 103.2868 |
| 2.1622        | 0.07   | 300  | 11.43 | 30.01 | 1.7297          | 108.2395 |
| 2.0314        | 0.09   | 400  | 12.96 | 31.0  | 1.6499          | 106.4385 |
| 1.7219        | 0.11   | 500  | 12.94 | 33.67 | 1.5543          | 107.6092 |
| 1.577         | 0.13   | 600  | 12.84 | 35.03 | 1.4812          | 118.5502 |
| 1.3569        | 0.1532 | 700  | 19.94 | 38.08 | 1.4559          | 84.2864  |
| 1.3401        | 0.1751 | 800  | 13.39 | 36.11 | 1.3855          | 126.4295 |
| 1.2272        | 0.1970 | 900  | 24.39 | 41.75 | 1.3764          | 70.7789  |
| 1.2793        | 0.2189 | 1000 | 23.01 | 42.13 | 1.3389          | 80.6844  |
| 1.0383        | 0.2408 | 1100 | 23.42 | 43.59 | 1.3125          | 82.3953  |
| 1.0485        | 0.2627 | 1200 | 25.42 | 42.99 | 1.2996          | 69.4732  |
| 1.0427        | 0.2846 | 1300 | 29.24 | 45.36 | 1.2996          | 65.6461  |
| 0.8174        | 0.3065 | 1400 | 27.28 | 45.67 | 1.2522          | 68.3926  |
| 0.7345        | 0.3284 | 1500 | 26.35 | 46.78 | 1.2349          | 79.1986  |
| 0.7551        | 0.3503 | 1600 | 27.81 | 46.49 | 1.2317          | 70.6439  |
| 0.6765        | 0.3722 | 1700 | 27.62 | 47.46 | 1.2062          | 70.9140  |
| 0.6613        | 0.3940 | 1800 | 26.56 | 47.12 | 1.2087          | 72.8050  |
| 0.6181        | 0.4159 | 1900 | 29.91 | 48.76 | 1.2139          | 65.2859  |
| 0.5809        | 0.4378 | 2000 | 30.93 | 49.09 | 1.2119          | 63.1247  |
| 0.5898        | 0.4597 | 2100 | 25.91 | 46.24 | 1.2540          | 73.9307  |
| 0.5926        | 0.4816 | 2200 | 25.19 | 44.72 | 1.2479          | 78.7933  |
| 0.5158        | 0.5035 | 2300 | 28.9  | 46.76 | 1.2532          | 66.3665  |
| 0.4511        | 0.5254 | 2400 | 28.89 | 46.83 | 1.2517          | 66.3215  |
| 0.4329        | 0.5473 | 2500 | 26.19 | 45.91 | 1.2573          | 72.6700  |
| 0.4106        | 0.5692 | 2600 | 26.91 | 46.84 | 1.2615          | 72.4899  |
| 0.4002        | 0.5911 | 2700 | 27.77 | 46.93 | 1.2396          | 71.0491  |
| 0.4047        | 0.6130 | 2800 | 29.9  | 47.79 | 1.2450          | 66.9968  |
| 0.3719        | 0.6349 | 2900 | 30.5  | 48.78 | 1.2522          | 65.1959  |
| 0.327         | 0.6567 | 3000 | 31.22 | 49.0  | 1.2493          | 64.1153  |
| 0.3138        | 0.6786 | 3100 | 30.1  | 47.82 | 1.2653          | 65.1959  |
| 0.3349        | 0.7005 | 3200 | 30.37 | 48.64 | 1.2651          | 63.9802  |
| 0.2807        | 0.7224 | 3300 | 26.02 | 45.46 | 1.2762          | 76.8573  |
| 0.2648        | 0.7443 | 3400 | 30.65 | 47.58 | 1.2761          | 64.6105  |
| 0.2633        | 0.7662 | 3500 | 29.73 | 47.74 | 1.2890          | 65.5110  |
| 0.2316        | 0.7881 | 3600 | 29.94 | 47.33 | 1.2886          | 66.4566  |
| 0.233         | 0.8100 | 3700 | 27.82 | 48.01 | 1.2905          | 73.1202  |
| 0.2196        | 0.8319 | 3800 | 31.51 | 48.66 | 1.2994          | 63.7100  |
| 0.2119        | 0.8538 | 3900 | 30.09 | 48.44 | 1.2910          | 65.0158  |
| 0.2082        | 0.8757 | 4000 | 30.91 | 47.99 | 1.2924          | 65.1058  |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
