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# whisper-large-v3-japanese-4k-steps

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 16.1 dataset. I followed a post by Sanchit Gandhi, https://huggingface.co/blog/fine-tune-whisper
It took 24 hours using an A100 on Google Colab to complete 4000 steps using the Common Voice 16.1 dataset. Training loss dropped over epochs but validation loss increased, so textbook overfitting. Furthermore, WER increased. It achieves the following results on the evaluation set:
- Loss: 0.4057
- Wer: 18.2149

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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       |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 0.1374        | 1.02  | 1000 | 0.3618          | 11.983182 |
| 0.0508        | 2.04  | 2000 | 0.3658          | 17.554657 |
| 0.0206        | 3.05  | 3000 | 0.3904          | 21.087484 |
| 0.0066        | 4.07  | 4000 | 0.4057          | 18.214909 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2

{"language": ["ja"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_1"], "metrics": ["wer"], "base_model": "openai/whisper-large-v3", "model-index": [{"name": "whisper-large-v3-japanese-4k-steps", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 16.1", "type": "mozilla-foundation/common_voice_16_1", "config": "ja", "split": "None", "args": "config: ja, split: test"}, "metrics": [{"type": "wer", "value": 1821.4909443725744, "name": "Wer"}]}]}]}
transformers tensorboard safetensors whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer ja dataset:mozilla-foundation/common_voice_16_1 base_model:openai/whisper-large-v3 license:apache-2.0 model-index endpoints_compatible region:us