Whisper Small Finetuned Iwslt Welsh

--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - covost2 metrics: - wer - bleu model-index: - name: whisper-small-finetuned-iwslt-welsh results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: covost2 type: covost2 config: cy_en split: validation args: cy_en metrics: - name: Wer type: wer value: 82.10851025222283 - name: Bleu type: bleu value: 14.098824712552057 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-finetuned-iwslt-welsh This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the covost2 dataset. It achieves the following results on the evaluation set: - Loss: 3.7340 - Wer: 82.1085 - Bleu: 14.0988 - Chrf++: 33.5907 - Comet: 0.5541 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 5000 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Bleu | Chrf++ | Comet | |:-------------:|:-------:|:----:|:---------------:|:-------:|:-------:|:-------:|:------:| | 1.6851 | 16.0256 | 1250 | 3.2626 | 89.7478 | 11.2753 | 31.8587 | 0.5606 | | 1.4611 | 32.0513 | 2500 | 3.4611 | 82.4896 | 14.0591 | 34.1147 | 0.5704 | | 1.4471 | 48.0769 | 3750 | 3.6301 | 81.8000 | 14.0228 | 33.4928 | 0.5625 | | 1.4415 | 64.1026 | 5000 | 3.7340 | 82.1085 | 14.0988 | 33.5907 | 0.5541 | ### Framework versions - Transformers 4.52.0 - Pytorch 2.11.0+cu128 - Datasets 2.21.0 - Tokenizers 0.21.4