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---
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
|