---
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: 0.059877955758962625
---

<!-- 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

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: 0.4253
- F1 Score: 0.7984
- Label F1: 0.8971
- Wer: 0.0599

## 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: 32
- 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 | F1 Score | Label F1 | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|
| 0.4435        | 0.36  | 200  | 0.4357          | 0.4513   | 0.7168   | 0.0599 |
| 0.4309        | 0.71  | 400  | 0.4306          | 0.6751   | 0.8354   | 0.0599 |
| 0.4235        | 1.07  | 600  | 0.4282          | 0.6722   | 0.8548   | 0.0599 |
| 0.4267        | 1.43  | 800  | 0.4269          | 0.7073   | 0.8455   | 0.0599 |
| 0.4254        | 1.79  | 1000 | 0.4264          | 0.7273   | 0.8678   | 0.0599 |
| 0.4264        | 2.14  | 1200 | 0.4264          | 0.7398   | 0.8780   | 0.0599 |
| 0.4206        | 2.5   | 1400 | 0.4262          | 0.7206   | 0.8583   | 0.0599 |
| 0.4232        | 2.86  | 1600 | 0.4260          | 0.7410   | 0.8685   | 0.0599 |
| 0.4249        | 3.22  | 1800 | 0.4255          | 0.7603   | 0.8926   | 0.0599 |
| 0.4239        | 3.57  | 2000 | 0.4256          | 0.7631   | 0.8835   | 0.0599 |
| 0.4213        | 3.93  | 2200 | 0.4255          | 0.7692   | 0.8988   | 0.0599 |
| 0.4213        | 4.29  | 2400 | 0.4256          | 0.7769   | 0.8926   | 0.0599 |
| 0.4244        | 4.65  | 2600 | 0.4253          | 0.7711   | 0.8996   | 0.0599 |
| 0.4234        | 5.0   | 2800 | 0.4254          | 0.7386   | 0.8797   | 0.0599 |
| 0.4222        | 5.36  | 3000 | 0.4252          | 0.7917   | 0.9      | 0.0599 |
| 0.4239        | 5.72  | 3200 | 0.4254          | 0.7801   | 0.8963   | 0.0599 |
| 0.4201        | 6.08  | 3400 | 0.4254          | 0.7950   | 0.8954   | 0.0599 |
| 0.4194        | 6.43  | 3600 | 0.4253          | 0.7851   | 0.9008   | 0.0599 |
| 0.4203        | 6.79  | 3800 | 0.4252          | 0.7934   | 0.9091   | 0.0599 |
| 0.4214        | 7.15  | 4000 | 0.4253          | 0.8050   | 0.9046   | 0.0599 |
| 0.4206        | 7.51  | 4200 | 0.4253          | 0.8      | 0.9      | 0.0599 |
| 0.4205        | 7.86  | 4400 | 0.4253          | 0.8050   | 0.9129   | 0.0599 |
| 0.4207        | 8.22  | 4600 | 0.4253          | 0.7951   | 0.9016   | 0.0599 |
| 0.4218        | 8.58  | 4800 | 0.4253          | 0.7984   | 0.8971   | 0.0599 |
| 0.4201        | 8.94  | 5000 | 0.4253          | 0.7984   | 0.8971   | 0.0599 |


### Framework versions

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