--- library_name: transformers language: - hi license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Ori vi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 66.3190672749656 --- # Whisper Small Ori vi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 3.0544 - Wer: 66.3191 ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - 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 | |:-------------:|:------:|:----:|:---------------:|:-------:| | 1.7932 | 2.2222 | 1000 | 3.2617 | 90.6510 | | 0.5752 | 4.4444 | 2000 | 3.0216 | 78.4995 | | 0.1394 | 6.6667 | 3000 | 3.0541 | 70.0196 | | 0.0175 | 8.8889 | 4000 | 3.0544 | 66.3191 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.4.0 - Datasets 3.1.0 - Tokenizers 0.20.0