--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: openai/whisper-large-v2 results: - 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: 12.28 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_cmu type: rishabhjain16/infer_cmu config: en split: test metrics: - type: wer value: 9.8 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: 4.82 name: WER - 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: 15.67 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_pf_swedish type: rishabhjain16/infer_pf_swedish config: en split: test metrics: - type: wer value: 25.56 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: 10.88 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_pf_german type: rishabhjain16/infer_pf_german config: en split: test metrics: - type: wer value: 65.58 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_pf_italian type: rishabhjain16/infer_pf_italian config: en split: test metrics: - type: wer value: 23.48 name: WER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: rishabhjain16/infer_so_chinese type: rishabhjain16/infer_so_chinese config: en split: test metrics: - type: wer value: 25.05 name: WER --- # 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 None dataset. It achieves the following results on the evaluation set: - Loss: 0.3834 - Wer: 11.8889 ## 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: 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 | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5582 | 0.12 | 500 | 0.3660 | 14.8170 | | 0.2285 | 1.02 | 1000 | 0.2919 | 12.6304 | | 0.2038 | 1.15 | 1500 | 0.2795 | 11.3850 | | 0.074 | 2.04 | 2000 | 0.3150 | 12.1043 | | 0.2165 | 2.17 | 2500 | 0.2978 | 12.8510 | | 0.0399 | 3.07 | 3000 | 0.3467 | 11.7322 | | 0.045 | 3.19 | 3500 | 0.3501 | 11.7218 | | 0.0187 | 4.09 | 4000 | 0.3834 | 11.8889 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2