transformers safetensors whisper automatic-speech-recognition generated_from_trainer dataset:facebook/voxpopuli base_model:openai/whisper-small license:apache-2.0 model-index endpoints_compatible region:us
---
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: 0.14642407057340895
---

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# 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: 0.3933
- Wer: 0.1464

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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 | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3562        | 0.36  | 200  | 0.3265          | 0.1920 |
| 0.3149        | 0.71  | 400  | 0.3136          | 0.1842 |
| 0.2778        | 1.07  | 600  | 0.3204          | 0.1786 |
| 0.2288        | 1.43  | 800  | 0.3156          | 0.1717 |
| 0.2307        | 1.79  | 1000 | 0.3056          | 0.1708 |
| 0.1482        | 2.14  | 1200 | 0.3138          | 0.1682 |
| 0.1368        | 2.5   | 1400 | 0.3136          | 0.1656 |
| 0.1405        | 2.86  | 1600 | 0.3082          | 0.1617 |
| 0.0639        | 3.22  | 1800 | 0.3201          | 0.1612 |
| 0.0673        | 3.57  | 2000 | 0.3242          | 0.1612 |
| 0.0688        | 3.93  | 2200 | 0.3235          | 0.1584 |
| 0.0227        | 4.29  | 2400 | 0.3420          | 0.1558 |
| 0.0232        | 4.65  | 2600 | 0.3430          | 0.1525 |
| 0.0229        | 5.0   | 2800 | 0.3450          | 0.1528 |
| 0.0064        | 5.36  | 3000 | 0.3631          | 0.1498 |
| 0.0059        | 5.72  | 3200 | 0.3652          | 0.1482 |
| 0.0043        | 6.08  | 3400 | 0.3756          | 0.1482 |
| 0.0021        | 6.43  | 3600 | 0.3798          | 0.1477 |
| 0.002         | 6.79  | 3800 | 0.3824          | 0.1484 |
| 0.0014        | 7.15  | 4000 | 0.3876          | 0.1471 |
| 0.0013        | 7.51  | 4200 | 0.3900          | 0.1473 |
| 0.0013        | 7.86  | 4400 | 0.3917          | 0.1461 |
| 0.0012        | 8.22  | 4600 | 0.3929          | 0.1462 |
| 0.0012        | 8.58  | 4800 | 0.3932          | 0.1465 |
| 0.0012        | 8.94  | 5000 | 0.3933          | 0.1464 |


### Framework versions

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