--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: openai/whisper-medium.en results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_cmu_9h type: rishabhjain16/infer_cmu_9h config: en split: test metrics: - type: wer value: 15.53 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_pfs type: rishabhjain16/infer_pfs config: en split: test metrics: - type: wer value: 3.14 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_myst type: rishabhjain16/infer_myst config: en split: test metrics: - type: wer value: 15.84 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/libritts_dev_clean type: rishabhjain16/libritts_dev_clean config: en split: test metrics: - type: wer value: 5.28 name: WER --- # openai/whisper-medium.en This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1748 - Wer: 2.7097 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0329 | 5.0 | 500 | 0.1343 | 4.0125 | | 0.0013 | 10.01 | 1000 | 0.1531 | 2.8810 | | 0.0002 | 15.01 | 1500 | 0.1609 | 2.7321 | | 0.0002 | 20.01 | 2000 | 0.1608 | 2.7544 | | 0.0001 | 25.01 | 2500 | 0.1688 | 2.7321 | | 0.0002 | 30.02 | 3000 | 0.1722 | 2.7172 | | 0.0001 | 35.02 | 3500 | 0.1742 | 2.7172 | | 0.0001 | 40.02 | 4000 | 0.1748 | 2.7097 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2