transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer gl 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
library_name: transformers
license: mit
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-large-v3-turbo-FLEURS-GL
  results: []
---

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

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 Text to Speech** task. It takes galician speech audios as input and generates the correspondant transcription.

This Automatic Speech Recognition model, was developed to be the first stage of a Speech Translation cascade system for transcribing and translating Galician audios into English texts. After this first STT step, this [Galician-to-English MT model](https://huggingface.co/juanjucm/nllb-200-distilled-600M-FLEURS-GL-EN) can be applied over the generated Galician transcriptions to get English text translations.

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 WER score of **16.6** on the evaluation dataset.

After fine-tuning, it achieves the following results on the evaluation set:
- Loss: 0.2522
- **WER: 9.1731**

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: 10
- mixed_precision_training: Native AMP

### Training results

We used [WER (Word Error Rate)](https://en.wikipedia.org/wiki/Word_error_rate) as our reference transcription metric for selecting the best checkpoint after training.

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.125         | 1.0   | 86   | 0.2128          | 10.1464 |
| 0.0835        | 2.0   | 172  | 0.2006          | 9.4315  |
| 0.0322        | 3.0   | 258  | 0.2091          | 9.6985  |
| 0.0115        | 4.0   | 344  | 0.2325          | 9.8880  |
| 0.0127        | 5.0   | 430  | 0.2313          | 9.2506  |
| 0.0022        | 7.0   | 602  | 0.2498          | 9.2679  |
| 0.0021        | 6.0   | 516  | 0.2412          | 9.3885  |
| 0.0011        | 8.0   | 688  | 0.2522          | 9.1731  |
| 0.0017        | 9.0   | 774  | 0.2538          | 9.3023  |
| 0.0011        | 10.0  | 860  | 0.2556          | 9.2937  |


### Framework versions

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