---
language:
- de
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper-Tiny-german-HanNeurAI
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: de
      split: test
      args: 'config: de, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 31.434636476207324
---

<!-- 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-Tiny-german-HanNeurAI

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5505
- Wer: 31.4346

This model is part of my school project, it uses shuffled 100k rows of train dataset since the computation power is limited.

Additional information can be found in this github: [HanCreation/Whisper-Tiny-German](https://github.com/HanCreation/Whisper-Tiny-German)

### 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4824        | 0.16  | 1000 | 0.6305          | 35.5019 |
| 0.4284        | 0.32  | 2000 | 0.5855          | 33.3615 |
| 0.4152        | 0.48  | 3000 | 0.5610          | 32.1068 |
| 0.4387        | 0.64  | 4000 | 0.5505          | 31.4346 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
  
## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure