--- language: - zh license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small zh - seiching results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 config: zh-TW split: test args: zh-TW metrics: - name: Wer type: wer value: 43.76122978638451 --- # Whisper Small zh - seiching This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.2009 - Wer Ortho: 43.2539 - Wer: 43.7612 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | 0.2133 | 0.69 | 500 | 0.2009 | 43.2539 | 43.7612 | ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0