--- language: - pl tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Large v2 PL results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: pl split: test args: pl metrics: - type: wer value: 7.280175959972464 name: WER - type: wer value: 7.31 name: WER - type: wer_without_norm value: 20.18 name: WER unnormalized - type: cer value: 2.08 name: CER - type: mer value: 7.27 name: MER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: facebook/voxpopuli type: facebook/voxpopuli config: pl split: test metrics: - type: wer value: 9.61 name: WER - type: wer_without_norm value: 30.33 name: WER unnormalized - type: cer value: 5.5 name: CER - type: mer value: 9.45 name: MER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: google/fleurs type: google/fleurs config: pl_pl split: test metrics: - type: wer value: 8.68 name: WER - type: wer_without_norm value: 29.33 name: WER unnormalized - type: cer value: 3.63 name: CER --- # Whisper Large v2 PL This model is a fine-tuned version of [bardsai/whisper-large-v2-pl-v2](https://huggingface.co/bardsai/whisper-large-v2-pl-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3684 - Wer: 7.2802 ## 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: 4 - 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: 2100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0047 | 1.35 | 700 | 0.3428 | 8.5562 | | 0.0011 | 2.7 | 1400 | 0.3605 | 7.5505 | | 0.0003 | 4.05 | 2100 | 0.3684 | 7.2802 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2