Whisper Small Jap

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: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2

--- library_name: transformers language: - ja license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - Bateesa/papoli metrics: - wer model-index: - name: Whisper results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: papoli voicee type: Bateesa/papoli args: 'config: ja, split: test' metrics: - name: Wer type: wer value: 21.207764198418406 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the papoli voicee dataset. It achieves the following results on the evaluation set: - Loss: 0.7821 - Wer: 21.2078 ## 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: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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 | |:-------------:|:-------:|:----:|:---------------:|:-------:| | 0.0025 | 24.3902 | 1000 | 0.7089 | 27.9655 | | 0.0001 | 48.7805 | 2000 | 0.7556 | 21.1359 | | 0.0000 | 73.1707 | 3000 | 0.7753 | 21.2078 | | 0.0000 | 97.5610 | 4000 | 0.7821 | 21.2078 | ### Framework versions - Transformers 5.0.0 - Pytorch 2.10.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2