---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: openai/whisper-small
datasets:
- facebook/voxpopuli
metrics:
- wer
model-index:
- name: WhisperForSpokenNER-end2end
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: facebook/voxpopuli de+es+fr+nl
      type: facebook/voxpopuli
      split: de+es+fr+nl
    metrics:
    - type: wer
      value: 0.38886263390044107
      name: Wer
---

<!-- 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: 0.3381
- Wer: 0.3889

## 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.3436        | 0.36  | 200  | 1.8791          | 0.8871 |
| 1.1682        | 0.71  | 400  | 1.0307          | 0.5048 |
| 0.7321        | 1.07  | 600  | 0.6300          | 0.3665 |
| 0.4564        | 1.43  | 800  | 0.4381          | 0.3515 |
| 0.4095        | 1.79  | 1000 | 0.4027          | 0.3330 |
| 0.3813        | 2.14  | 1200 | 0.3847          | 0.3360 |
| 0.3667        | 2.5   | 1400 | 0.3734          | 0.3392 |
| 0.3583        | 2.86  | 1600 | 0.3649          | 0.3490 |
| 0.3454        | 3.22  | 1800 | 0.3588          | 0.3572 |
| 0.3422        | 3.57  | 2000 | 0.3537          | 0.3705 |
| 0.3371        | 3.93  | 2200 | 0.3503          | 0.3811 |
| 0.3291        | 4.29  | 2400 | 0.3475          | 0.3678 |
| 0.324         | 4.65  | 2600 | 0.3451          | 0.3670 |
| 0.3262        | 5.0   | 2800 | 0.3431          | 0.3710 |
| 0.3168        | 5.36  | 3000 | 0.3419          | 0.3847 |
| 0.3178        | 5.72  | 3200 | 0.3406          | 0.3833 |
| 0.3136        | 6.08  | 3400 | 0.3400          | 0.3853 |
| 0.3092        | 6.43  | 3600 | 0.3393          | 0.3896 |
| 0.3106        | 6.79  | 3800 | 0.3389          | 0.3900 |
| 0.3057        | 7.15  | 4000 | 0.3388          | 0.3803 |
| 0.3087        | 7.51  | 4200 | 0.3383          | 0.3941 |
| 0.308         | 7.86  | 4400 | 0.3382          | 0.3874 |
| 0.3036        | 8.22  | 4600 | 0.3381          | 0.3896 |
| 0.3087        | 8.58  | 4800 | 0.3380          | 0.3910 |
| 0.3079        | 8.94  | 5000 | 0.3381          | 0.3889 |


### Framework versions

- PEFT 0.7.1.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1