metadata

library_name: transformers
license: apache-2.0
base_model: openai/whisper-base
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - asierhv/composite_corpus_eu_v2.1
metrics:
  - wer
model-index:
  - name: Whisper Base Basque
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Mozilla Common Voice 18.0
          type: mozilla-foundation/common_voice_18_0
        metrics:
          - name: Wer
            type: wer
            value: 10.78
language:
  - eu

Whisper Base Basque

This model is a fine-tuned version of openai/whisper-base specifically for Basque (eu) language Automatic Speech Recognition (ASR). It was trained on the asierhv/composite_corpus_eu_v2.1 dataset, which is a composite corpus designed to improve Basque ASR performance.

Key improvements and results compared to the base model:

Significant WER reduction: The fine-tuned model achieves a Word Error Rate (WER) of 12.3080 on the validation set of the asierhv/composite_corpus_eu_v2.1 dataset, demonstrating improved accuracy compared to the base whisper-base model for Basque.
Performance on Common Voice: When evaluated on the Mozilla Common Voice 18.0 dataset, the model achieved a WER of 10.78. This demonstrates the model's ability to generalize to other Basque speech datasets, and highlights the improvement in accuracy due to the larger base model.

Model description

This model builds upon the whisper-base architecture, known for its strong performance in multilingual speech recognition. By fine-tuning this model on a dedicated Basque speech corpus, it specializes in accurately transcribing Basque speech. The whisper-base model offers a larger capacity than whisper-tiny, resulting in higher accuracy, albeit with increased computational requirements.

Intended uses & limitations

Intended uses:

High-accuracy automatic transcription of Basque speech.
Development of advanced Basque speech-based applications.
Research in Basque speech processing requiring higher accuracy.
Professional transcription services for Basque language.
Applications where slightly higher computational cost is acceptable for improved accuracy.

Limitations:

Performance remains dependent on audio quality, with challenges posed by background noise and poor recording conditions.
Accuracy may still be affected by highly dialectal or informal Basque speech.
While demonstrating improved performance, the model may still produce errors, especially with complex linguistic structures.
The base model is larger than the tiny, so inference will be slower and require more resources.

Training and evaluation data

Training dataset: asierhv/composite_corpus_eu_v2.1. This dataset is a carefully curated compilation of Basque speech data, designed to enhance the effectiveness of Basque ASR systems.
Evaluation Dataset: The test portion of asierhv/composite_corpus_eu_v2.1.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2.5e-05
train_batch_size: 32
eval_batch_size: 16
seed: 42
optimizer: AdamW with betas=(0.9, 0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 10000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	WER
0.4816	0.1	1000	0.5136	25.7525
0.2515	0.2	2000	0.4336	19.9950
0.1792	0.3	3000	0.4054	17.6408
0.2485	0.4	4000	0.3804	16.3794
0.1007	0.5	5000	0.4056	15.2554
0.1296	0.6	6000	0.3731	15.3241
0.1555	0.7	7000	0.3764	13.3820
0.114	0.8	8000	0.3097	12.7513
0.0775	0.9	9000	0.3170	12.4578
0.0836	1.0	10000	0.3183	12.3080

Framework versions

Transformers 4.49.0.dev0
Pytorch 2.6.0+cu124
Datasets 3.3.1.dev0
Tokenizers 0.21.0