transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer gl en dataset:juanjucm/FLEURS-SpeechT-GL-EN base_model:openai/whisper-large-v3-turbo base_model:finetune:openai/whisper-large-v3-turbo license:mit endpoints_compatible region:us
---
base_model: openai/whisper-large-v3-turbo
datasets:
- juanjucm/FLEURS-SpeechT-GL-EN
language:
- gl
- en
library_name: transformers
license: mit
metrics:
- wer
- bleu
tags:
- generated_from_trainer
model-index:
- name: whisper-large-v3-turbo-FLEURS-GL-EN
  results: []
---


# whisper-large-v3-turbo-FLEURS-GL-EN

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) trained on [juanjucm/FLEURS-SpeechT-GL-EN](https://huggingface.co/datasets/juanjucm/FLEURS-SpeechT-GL-EN)
for **Galician-to-English Text to Speech Translation** task. It takes galician speech audios as input and generates the correspondant translated transcription in English.

The motivation behind this work is to increase the visibility of the Galician language, making it more accessible for non-Galician speakers to understand and engage with Galician audio content.

This model was developed during a 3-week Speech Translation workshop organised by [Yasmin Moslem](https://huggingface.co/ymoslem).


### Performance and training details

Baseline model achieved a BLEU score of **5.0** on the evaluation dataset.

After fine-tuning, it achieves the following results on the evaluation set:
- Loss: 1.4958
- Wer: 71.6814
- **BLEU: 18.9665**
- **ChrF++: 46.00**


The following hyperparameters were used during training:

- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
- 

### Training results

We used [BLEU Score](https://en.wikipedia.org/wiki/BLEU) as our reference translation metric for selecting the best checkpoint after training.

| Training Loss | Epoch | Step | Validation Loss | Wer      | Bleu    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|
| 4.2751        | 1.0   | 5    | 3.8850          | 76.6962  | 18.0512 |
| 2.3984        | 2.0   | 10   | 2.6965          | 97.0501  | 13.5327 |
| 1.4958        | 3.0   | 15   | 2.2308          | 71.6814  | 18.9665 |
| 1.27          | 4.0   | 20   | 2.0454          | 128.3186 | 12.2446 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0