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README.md

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+---
+language:
+- ja
+license: mit
+tags:
+- whisper
+- fine-tuning
+- jdd-topic1
+- speechbrain
+- automatic-speech-recognition
+base_model: openai/whisper-base
+datasets:
+- noflm/jdd_topic1_batch2
+pipeline_tag: automatic-speech-recognition
+---
+
+# Whisper Fine-tuning Experiment: jdd_topic1_batch2-whisper-base
+
+## Model Description
+
+This model contains a complete Whisper fine-tuning experiment including:
+- Training checkpoints (SpeechBrain format)
+- Final model (Transformers format)
+- Test results and evaluation metrics
+- Training history visualizations
+
+## Model Information
+
+- **Base Model**: openai/whisper-base
+- **Framework**: SpeechBrain v1.0.3
+- **Training Dataset**: [noflm/jdd_topic1_batch2](https://huggingface.co/datasets/noflm/jdd_topic1_batch2)
+- **Language**: Japanese (ja)
+- **Task**: Automatic Speech Recognition (ASR)
+
+## Test Results
+
+- **WER**: 12.17%
+- **CER**: 9.08%
+- **Test Loss**: 0.0814
+
+## Contents
+
+```
+├── checkpoints/          # Training checkpoints
+│   ├── CKPT+epoch_*/    # Per-epoch checkpoints
+│   ├── CKPT+BEST_WER/   # Best WER checkpoint
+│   └── CKPT+FINAL/      # Final checkpoint
+├── final_model/          # Transformers-compatible model
+│   ├── config.json      # Model configuration
+│   ├── model.safetensors # Model weights
+│   ├── preprocessor_config.json
+│   ├── tokenizer_config.json
+│   └── ...
+├── test_results.json     # Test metrics
+├── detailed_metrics.json # Detailed training history
+├── training_history_speechbrain.png  # Training curves
+└── training_report_speechbrain.txt   # Summary report
+```
+
+## Usage
+
+### Load checkpoint (SpeechBrain format)
+```python
+import torch
+checkpoint = torch.load('checkpoints/CKPT+BEST_WER/model.ckpt')
+```
+
+### Load final model (Transformers format)
+```python
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+
+model = WhisperForConditionalGeneration.from_pretrained("./final_model")
+processor = WhisperProcessor.from_pretrained("./final_model")
+```
+
+## Citation
+
+If you use this experiment data, please cite the original Whisper paper:
+```bibtex
+@article{radford2022robust,
+  title={Robust speech recognition via large-scale weak supervision},
+  author={Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
+  journal={arXiv preprint arXiv:2212.04356},
+  year={2022}
+}
+```