--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - common_voice_17_0 metrics: - wer model-index: - name: whisper-small-test results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_17_0 type: common_voice_17_0 config: uz split: None args: uz metrics: - name: Wer type: wer value: 32.280771822969974 --- # whisper-small-test This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_17_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3346 - Wer: 32.2808 ## 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: 1e-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: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.5745 | 0.1088 | 1000 | 0.5290 | 45.9187 | | 0.4554 | 0.2176 | 2000 | 0.4041 | 37.0572 | | 0.3897 | 0.3264 | 3000 | 0.3553 | 33.9258 | | 0.3655 | 0.4353 | 4000 | 0.3346 | 32.2808 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.4.0 - Datasets 3.1.0 - Tokenizers 0.20.3