transformers safetensors whisper_for_slu whisper-event generated_from_trainer dataset:facebook/voxpopuli base_model:openai/whisper-large-v2 license:apache-2.0 model-index endpoints_compatible region:us
---
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- whisper-event
- generated_from_trainer
datasets:
- facebook/voxpopuli
metrics:
- wer
model-index:
- name: WhisperForSpokenNER
  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: 0.06196300023221612
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# WhisperForSpokenNER

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the facebook/voxpopuli de+es+fr+nl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2797
- F1 Score: 0.7918
- Label F1: 0.8933
- Wer: 0.0620

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- 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 | F1 Score | Label F1 | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|
| 0.1748        | 0.36  | 200  | 0.1706          | 0.6541   | 0.8032   | 0.0656 |
| 0.1754        | 0.71  | 400  | 0.1769          | 0.7194   | 0.8502   | 0.0674 |
| 0.1606        | 1.07  | 600  | 0.1856          | 0.6991   | 0.8407   | 0.0708 |
| 0.1282        | 1.43  | 800  | 0.1835          | 0.7455   | 0.8724   | 0.0728 |
| 0.131         | 1.79  | 1000 | 0.1762          | 0.7331   | 0.8691   | 0.0713 |
| 0.0804        | 2.14  | 1200 | 0.1792          | 0.7544   | 0.8744   | 0.0685 |
| 0.0712        | 2.5   | 1400 | 0.1833          | 0.75     | 0.8846   | 0.0691 |
| 0.0746        | 2.86  | 1600 | 0.1800          | 0.7554   | 0.8732   | 0.0738 |
| 0.0331        | 3.22  | 1800 | 0.1992          | 0.7757   | 0.8804   | 0.0702 |
| 0.0363        | 3.57  | 2000 | 0.1938          | 0.7625   | 0.8805   | 0.0688 |
| 0.037         | 3.93  | 2200 | 0.1986          | 0.7771   | 0.8865   | 0.0677 |
| 0.0153        | 4.29  | 2400 | 0.2125          | 0.7765   | 0.8794   | 0.0666 |
| 0.0144        | 4.65  | 2600 | 0.2115          | 0.7763   | 0.8922   | 0.0681 |
| 0.0148        | 5.0   | 2800 | 0.2180          | 0.7781   | 0.8891   | 0.0647 |
| 0.0058        | 5.36  | 3000 | 0.2310          | 0.7918   | 0.8913   | 0.0629 |
| 0.0058        | 5.72  | 3200 | 0.2268          | 0.7828   | 0.8938   | 0.0627 |
| 0.0036        | 6.08  | 3400 | 0.2462          | 0.7911   | 0.8937   | 0.0621 |
| 0.0019        | 6.43  | 3600 | 0.2493          | 0.7948   | 0.8950   | 0.0629 |
| 0.0016        | 6.79  | 3800 | 0.2543          | 0.7917   | 0.8980   | 0.0631 |
| 0.0009        | 7.15  | 4000 | 0.2667          | 0.7880   | 0.8944   | 0.0619 |
| 0.0007        | 7.51  | 4200 | 0.2735          | 0.7909   | 0.8934   | 0.0624 |
| 0.0007        | 7.86  | 4400 | 0.2756          | 0.7901   | 0.8926   | 0.0621 |
| 0.0005        | 8.22  | 4600 | 0.2779          | 0.7913   | 0.8931   | 0.0624 |
| 0.0004        | 8.58  | 4800 | 0.2795          | 0.7909   | 0.8932   | 0.0620 |
| 0.0005        | 8.94  | 5000 | 0.2797          | 0.7918   | 0.8933   | 0.0620 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
