| --- |
| license: apache-2.0 |
| base_model: openai/whisper-small |
| tags: |
| - generated_from_trainer |
| datasets: |
| - facebook/voxpopuli |
| metrics: |
| - wer |
| model-index: |
| - name: WhisperForSpokenNER-end2end |
| results: |
| - task: |
| name: Automatic Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: facebook/voxpopuli de+es+fr+nl |
| type: facebook/voxpopuli |
| config: de+es+fr_nl |
| split: None |
| metrics: |
| - name: Wer |
| type: wer |
| value: 0.08582479210984335 |
| --- |
| |
| <!-- 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. --> |
|
|
| # WhisperForSpokenNER-end2end |
|
|
| This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the facebook/voxpopuli de+es+fr+nl dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2755 |
| - Combined Wer: 0.1491 |
| - F1 Score: 0.7163 |
| - Label F1: 0.8200 |
| - Wer: 0.0858 |
|
|
| ## 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: 0.0001 |
| - train_batch_size: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 128 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 500 |
| - training_steps: 5000 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Combined Wer | F1 Score | Label F1 | Wer | |
| |:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:--------:|:------:| |
| | 0.3252 | 0.1 | 500 | 0.3396 | 0.1918 | 0.6148 | 0.7578 | 0.1193 | |
| | 0.2729 | 0.2 | 1000 | 0.3158 | 0.1730 | 0.6449 | 0.7907 | 0.1058 | |
| | 0.2369 | 0.3 | 1500 | 0.2971 | 0.1736 | 0.6917 | 0.8083 | 0.1067 | |
| | 0.1967 | 0.4 | 2000 | 0.2823 | 0.1634 | 0.6915 | 0.8095 | 0.0999 | |
| | 0.1623 | 0.5 | 2500 | 0.2804 | 0.1693 | 0.7088 | 0.8249 | 0.1052 | |
| | 0.1146 | 1.02 | 3000 | 0.2820 | 0.1593 | 0.7012 | 0.8106 | 0.0951 | |
| | 0.0938 | 1.12 | 3500 | 0.2792 | 0.1500 | 0.7205 | 0.8238 | 0.0875 | |
| | 0.1001 | 1.22 | 4000 | 0.2750 | 0.1549 | 0.7072 | 0.8061 | 0.0928 | |
| | 0.0848 | 1.32 | 4500 | 0.2741 | 0.1471 | 0.7243 | 0.8318 | 0.0860 | |
| | 0.0649 | 1.42 | 5000 | 0.2745 | 0.1468 | 0.7304 | 0.8350 | 0.0858 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.37.0.dev0 |
| - Pytorch 2.1.0 |
| - Datasets 2.14.6 |
| - Tokenizers 0.14.1 |
|
|
