---
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-V2-HanNeurAI
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0 German shuffled 200k rows
      type: mozilla-foundation/common_voice_11_0
      config: de
      split: test
      args: 'config: de, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 32.33273006844562
---

<!-- 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-V2-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: 0.5818
- Wer: 32.3327

This fine-tuning model is part of my school project.
With limitation of my compute, I scale down the dataset from german common voice to shuffled 200k rows

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

## Model description

Model Parameter (pipe.model.num_parameters()): 37760640 (37M)

### 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: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2054        | 0.08  | 1000 | 0.7062          | 39.0698 |
| 0.1861        | 0.16  | 2000 | 0.6687          | 36.4857 |
| 0.1677        | 0.24  | 3000 | 0.6393          | 35.6849 |
| 0.2019        | 0.32  | 4000 | 0.6193          | 34.4385 |
| 0.1808        | 0.4   | 5000 | 0.6103          | 33.8459 |
| 0.1697        | 0.48  | 6000 | 0.5956          | 32.8519 |
| 0.1468        | 0.56  | 7000 | 0.5884          | 32.7029 |
| 0.1906        | 0.64  | 8000 | 0.5818          | 32.3327 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure