--- 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: ur split: test args: ur metrics: - name: Wer type: wer value: 23.893256903830512 --- # 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: 0.6123 - Wer: 23.8933 ## 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: 16 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - 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: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1935 | 1.1 | 200 | 0.4241 | 29.6526 | | 0.0649 | 3.09 | 400 | 0.4683 | 26.0622 | | 0.0156 | 5.08 | 600 | 0.5444 | 25.8104 | | 0.0039 | 7.08 | 800 | 0.5947 | 23.5021 | | 0.0019 | 9.07 | 1000 | 0.6123 | 23.8933 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2