whisper-large-v3-turbo-FLEURS-GL

This model is a fine-tuned version of openai/whisper-large-v3-turbo trained on 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 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.

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) 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