├── 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}
}

noflm/whisper-ft-jdd-topic1-docliponly-base-epoch30

Whisper Fine-tuning Experiment: jdd_topic1_docliponly_sample200-whisper-base-epoch30

Model Description

Model Information

Test Results

Training Details

Contents

Usage

Load checkpoint (SpeechBrain format)

Load final model (Transformers format)

Citation

noflm/
whisper-ft-jdd-topic1-docliponly-base-epoch30