--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice_11_0 metrics: - wer model-index: - name: openai/whisper-large-v2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_11_0 type: common_voice_11_0 config: cs split: test args: cs metrics: - name: Wer type: wer value: 9.044032338262648 --- # openai/whisper-large-v2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2131 - Wer: 9.0440 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0149 | 4.25 | 1000 | 0.1622 | 10.0403 | | 0.0027 | 8.51 | 2000 | 0.1848 | 9.5136 | | 0.0008 | 12.76 | 3000 | 0.1930 | 9.3166 | | 0.0004 | 17.02 | 4000 | 0.2062 | 9.0330 | | 0.0003 | 21.28 | 5000 | 0.2131 | 9.0440 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2