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 base_model:openai/whisper-medium base_model:finetune:openai/whisper-medium license:apache-2.0 model-index endpoints_compatible region:us
---
base_model: openai/whisper-medium
datasets:
- ymoslem/IWSLT2023-GA-EN
- ymoslem/FLEURS-GA-EN
- ymoslem/BitesizeIrish-GA-EN
- ymoslem/SpokenWords-GA-EN-MTed
language:
- ga
- en
license: apache-2.0
metrics:
- bleu
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper Medium 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: 32.14
      name: Bleu
    - type: wer
      value: 65.96127870328681
      name: Wer
---

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# 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
