---
language:
- de
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- rmacek/common_voice_zib2
metrics:
- wer
model-index:
- name: Whisper Small ZIB2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ZIB2 Common Voice
      type: rmacek/common_voice_zib2
      args: 'config: de, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 28.93835616438356
---

<!-- 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. -->

# Whisper Small ZIB2

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ZIB2 Common Voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3366
- Wer: 28.9384

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2391        | 10.0  | 100  | 0.2837          | 33.5616 |
| 0.0035        | 20.0  | 200  | 0.2701          | 27.7397 |
| 0.0012        | 30.0  | 300  | 0.2847          | 27.5685 |
| 0.0006        | 40.0  | 400  | 0.2990          | 27.9110 |
| 0.0004        | 50.0  | 500  | 0.3118          | 28.5959 |
| 0.0003        | 60.0  | 600  | 0.3221          | 28.5959 |
| 0.0002        | 70.0  | 700  | 0.3287          | 28.7671 |
| 0.0002        | 80.0  | 800  | 0.3333          | 28.9384 |
| 0.0002        | 90.0  | 900  | 0.3357          | 28.9384 |
| 0.0002        | 100.0 | 1000 | 0.3366          | 28.9384 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2